Method to transform structured data array

FIELD: information technologies.

SUBSTANCE: in the method of structured data array transformation, which contains text in natural language, they create (101) the first data structure of the structured data array from the end data structure of the structured data array. They create (102) a data base of logical connections between logical sections of elements of the first data structure. They create (103) the second data structure of the structured data array. They create (104) a data base of semantic parts of logical sections of elements of the second data structure. They create (105) grammatically and orthographically correct semantic parts of logical sections of the second data structure elements by means of linguistic transformations over the specified semantic parts. They create (106) the end data structure of the structured data array.

EFFECT: creation of logically, grammatically and orthographically true data structure, providing for quick and convenient navigation by structure elements.

17 cl, 15 dwg, 3 tbl

 

Group of inventions relates to the decisions in the field of data processing, in particular to decisions regarding the handling of structured data containing text in a natural language, and can be used for preliminary transformation of structured data array containing text in a natural language, for ease of subsequent processing.

The LEVEL of TECHNOLOGY

From the patent EA 002016 B1, G06F 17/30, 22.10.2001 (MATVEEV LION LAZAREVICH, ETC.) the known method of searching similar in text and/or semantic content of fragments of the electronic documents stored on the storage devices, namely, to each index stored in the archive document mentioned splitting documents into fragments and the formation of subjects from one or more fragment, determining the search parameters, the search, the ranking obtained in the search result list of fragments of documents, the search options, define a set included in the selected portion of the document unique pieces of information and expand it by pre-treatment of each of these unique pieces of information, where a unique block of information understand information block, encountered in the selected portion of the document one or more times, where as pre�tive processing using a receive operation, at least one unique block of information, one or more blocks of information associated with a unique block of information given ratio.

From the patent RU 2476927 C2, G06F 17/30, 27.02.2013 (ANSHUKOV SERGEI ALEKSANDROVICH, ETC.) a method of positioning of the texts in the space of knowledge, namely that is isolated from the input data items corresponding to patterns included in the taxa that form a taxonomy, United in the ontology; determine significant taxa that are weighed against the conditions assigned to the patterns; make a set of weighted vectors, positioning the input document in the space of knowledge, characterized in that it is used for positioning the set of ontologies, as well as the fact that in the preparation of sets of vectors consider only those elements that match the patterns belonging to a single taxon or taxa having a common parent taxa.

From the patent RU 2210809 C2, G06F 17/28, 20.08.2003 (OPEN JOINT-stock COMPANY MOSCOW TELECOMMUNICATION CORPORATION") is known a method for automatic conversion of the original text into a set of interrelated objects based on the settings table that contains knowledge about the structure of the system as an aggregate of its constituent classes, including a specific set of attributes (in �including the linkages and relationships between the entities of a given class) and set for each attribute rules for the recognition of the attribute in the text. Provides the possibility of determining the format of the original text and automatic translation of the fragments in the formation of objects.

From the patent RU 2292078 C1, G06F 17/30, 20.01.2007 (CLOSED JOINT stock COMPANY "MEDIALINGUA"), the known method of locating, marking and displaying information, comprising enter the required data objects of the source of electronic documents to search in information networks with terminal network subscriber carrying out the function of the source of the request the desired object data, comparing the desired object data source of electronic documents with the control data objects associated information in the information network, and the coincidence of the desired object to control the conversion of data objects of the source of electronic documents using markup objects data source electronic documents with hyperlinks, visualization at the terminal of the subscriber network of electronic documents with hyperlinks and a call to the terminal network subscriber data associated information information network, characterized in that prior to markup create at least two data regions, at least one of which is a resident of the area for the source of the request of the required data objects and binds to the primary data objects hyperlinks containing additional� parameters for addressing at least one other area, and at least one other region is non-resident for the source of the request of the required data objects and provides a binding to the secondary data objects hyperlinks for addressing at least one data resource associated information accessed from terminals of subscribers, at least one resident of the area, which is the primary source of the hyperlink, while resident in the area create an array of control data objects relevant to each designated primary object hyperlinks as linked data a non-resident in the area create an array of control data objects associated information corresponding to each of the specified object, at least one secondary hyperlink as linked data information associated information network.

From the patent RU 2386166 C2, G06F 17/30, 10.04.2010 (OPEN JOINT stock COMPANY "TAGANROG AVIATION SCIENTIFIC-TECHNICAL COMPLEX NAMED after. Company Beriev company") is known a method of forming a knowledge base that is formed in the form of three-dimensional information space, where the data about the document or portion thereof is determined in a cluster or clusters formed single line segments (vectors) of the characteristic signs. Full Ident�service document number form code ORT components of the characteristic signs and identification number of the document. Analyze each cluster to the completeness determination, limited them scope contained in the cluster documents. The result of the analysis are entered into the same cluster. Search and analysis of data is performed by forming and processing the request, and in the opposite direction through the training database for the expected user. The system also provides tools for working with the database, to search, control and analysis of information, documents, fields of work, for the creation and revision of documents by system administrators, experts and users according to access rights.

From the patent RU 2253893 C2, G06F 17/27, 10.06.2005 (CHERNIKOV BORIS), the known method of automated lexicological synthesis of documents, including creating and maintaining a unified form of document classification document content by allocating unified fixed information and variable information, save permanent data in the databases, making constant information in a standardized form document and the introduction of variable information in the document at which to allocate variable information variable standardized information related to sustainable formulations, variable input information representing the specifics�yousie information and variable non-standardized information containing free formulations, and standardized variable information is isolated by forming the aggregate of the reference words that uniquely identify the specific language in the document components and lexicological the skeleton of the document, and store in a database machine with an excess in relation to a particular instance of the document, form lexicological the document tree by determining the interdependence of the individual anchor words and then form an information control circuit document by establishing the method of implementation language variable standardized and non-standardized information depending on the nature of the relationship of the reference word with a document fragment.

From the application WO 2013043160 A1, G06F 17/21, 28.03.2013 (HEWLETT PACKARD DEVELOPMENT CO ET AL.) a method of processing text data array, which is to build a graph that represents a microcosm of the entities that form the body of the document being processed. The breaking of such a text in the nodes of the graph, where each node refers to the characteristics of the selected fragment of text, and referred to the nodes of the graph are linked by relations analogous context of text fragments corresponding to these nodes. Later built the nodes of the graph are ranked for about�for determining relevant data regarding the user's request.

From the application WO 2001001289 A1, G06F 17/27, 04.01.2001 (TNV MACHINE CORP INC) known way, which is the semantic processing of data represented in a natural language, and the method includes the input and storage of the terms and conditions, which are then used to search the data sets containing the data in a natural language, representations of the text that contains relevant user input information, the formatting of the mentioned concepts, retrieval of formatted representations of the text type of the subject-action-object (SDO) and storing them in a remote storage location, such as a database, restructuring identified in normalized LMS view, the purpose of the parts of the LMS, such as the action-object (TO) as the names of the folders, which contain parts of the LMS and the selected folders of one or more identical to the associated parts of the subject (S1, S2...Sn), which are associated with respective TO the parts. The method allows also to associate the sentence containing the relevant elements of the subjects S1, S2...Sn, and to select the relevant SRE with their next marking on the background of a common data set.

From patent US 8229730 B2, G06F 17/30, 24.07.2012 (MICROSOFT CORP. ET AL.) the known method of finding data on user demand, represented in natural language, and ability� is produced by parsing the text data array with the assignment of grammatical roles of the terms and their subsequent indexing, which are semantic connection with the terms of the search query, and mentioned the role contain a dominant and a subordinate role, identified in the analysis of the user query. This method allows to determine the relevant part of the document containing the terms with roles that coincide with the roles of the query text.

The application of EP 2400400 A1, G06F 17/27, 28.12.2011 (TNBENTA PROFESSIONAL SERVICES S L) a method is known semantic search for relevant information, namely that using lexical functions and criterion values of the text in the data presented in natural language, form a phrase or expression derived from a database of content, and choose the answer with the impending indicator of semantic matching, the method is to transform the content and query independent words or groups of words with their assigned tokens, which is transformed into a semantic representation, thereby applying the rules of the criterion values of a text through lexical functions, and each of these semantic representations consists of lemmas and semantic category.

From the application WO 2010105216 A2, G06F 17/20, 16.09.2010 (INVENTION MACHINE CORP) is known �] marking text data of the document, whereby carry out a linguistic analysis of the document, compare the document after the document analysis template with the required semantic relations between objects, to form a semantically marked-up text through the use of semantic relations based on the linguistic analysis of the text and pattern matching semantic relations and semantic labels associated with words or phrases, sentences of text, and identify the components of certain semantic relations may then be stored in a database of semantically marked-up text for the search of relevant information obtained by the data structure.

The application of EP 2105847 A1, G06F 17/30, 30.09.2009 (ALCATEL LUCENT) known method for automatic generation of ontology, namely that accept the term for which you want to create the ontology, define the value of the mentioned term with the help of the dictionary, remove the appropriate definition for the term, determine the value of each of the extracted definitions using the dictionary builds for each of the specific values of each term and the right term for the term of the initial request to create the ontology, at least one logical paragraph describing the relationship between the pair mentioned �odchodami terms over and above logical paragraphs define the ontology of the term you enter.

All the above solutions do not produce semantically and logically correct structured array data from the source data array containing the text presented in natural language, mentioned by splitting the array into logical sections that undergo semantic decomposition structures themselves partitions and elements referred to in the sections that follow their spelling and grammatical analysis, and subsequent evaluation of their interdependence in the original data array.

The closest analogue (prototype) of the claimed solution adopted method for the automated processing of natural language by its semantic indexing, described in patent RU 2399959 C2, G09B 19/00, 20.09.2010 (CLOSED JOINT stock COMPANY "AVICOMP SERVICES"). The known method is a method by which the text segment in electronic form in the basic unit, identify collocation, generate proposals, identify semantically important objects and semantically meaningful relationships between them, form for each semantically meaningful relationships with many of the triads, in which a single triad of the first type corresponds linkages established semantically meaningful relationship �between two semantically meaningful objects, each of the triads of the second type corresponds to the value of a specific attribute of one of these semantically meaningful objects, each of the triads of the third type corresponds to the value of a specific attribute of the semantically meaningful relationship index on the set of triads formed all associated semantic relations semantically meaningful objects individually and store in the database formed the triad and the resulting indices along with a link to the original text from which these formed the triad.

The disadvantage of this method is that during the formation of the above-mentioned triad, the text is segmented directly into elementary units, i.e. words, and not on logical partitions, this method does not provide for the formation of the intermediate structure of the original text array for subsequent grammatical and spelling analysis and does not provide the final logically, grammatically and spelling correct data structures, suitable for quick and easy navigation structure elements.

DISCLOSURE of INVENTION

Accordingly, the problem to be solved by the claimed invention is the provision of such processing structured data array containing text in a natural language, which �solila to generate logically, grammatically and spelling correct the converted structure that contains the logical structure of the elements of the array, providing quick and easy navigation through the elements of an array.

The technical result is the formation logically, grammatically and spelling correct data structures, suitable for quick and easy navigation structure elements.

The claimed technical result is achieved due to the fact that perform a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of logical links logical partitions mentioned elements of the first data structure;

(B) forming a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure, sformirovann�e using the information from the database logical links logical partitions, over and above logical partitions contain the first semantic part and the second semantic parts;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

Embodiments of the present invention relate to a method, device, system, and computer readable media for providing efficient conversion of a structured array of data containing at least the text in a natural language.

BRIEF description of the DRAWINGS

Illustrative embodiments of the present invention are described hereinafter in detail with reference to the accompanying drawings, which are not included in this document by reference and in which:

Fig.1 shows a General diagram of the steps of the claimed method of converting a structured array of data containing at least the text in a natural language.

Fig.2 shows a General diagram of the formation phase of the first data structure.

Fig.3 shows a General structure of the source data structure from which the first data structure.

Fig.4 shows a General diagram of the formation phase of the database logical links logical partitions.

Fig.5 shows the General principle of the formation of the database logical links logical partitions.

Fig.6 shows a General diagram of the formation phase of the second data structure.

Fig.7 shows a General structure of the second data structure.

Fig.8 shows a General diagram of the step of forming a database of semantic parts.

Fig.9 shows the General principle of building the database of semantic parts.

Fig.10 shows a General diagram of the step of forming grammatically and spelling correct semantic parts.

Fig.11 shows a General diagram of the second data structure obtained after the step of forming grammatically and spelling correct semantic parts.

Fig.12 shows a General diagram of the final stage of formation of the data structure.

Fig.13 shows odashima final data structure.

Fig.14 shows the General structure of the elements of the resulting data structure.

Fig.15 shows a General diagram of the system of converting structured data array containing conversion device structured data array.

Embodiments of the INVENTIONS

The following are embodiments of the present invention, disclosing examples of its implementation in private performances. However, the description itself is not intended to limit the scope of the rights granted under this patent. Rather should be understood that the invention can also be implemented in other ways so that will include different steps or combinations of steps similar to the steps described in this document, in combination with other existing and future technologies.

The inventive method will be discussed on the example of processing structured data array containing text in a natural language that represents, without limitation, regulatory legal acts (PPA). For the specialist should be obvious that, despite the fact that in this particular example implementation of the present invention, the transformation is ABO, this conversion method can be applied to any structured data array, CX�jego with ABO.

ABO is a document characterized by the following features:

1) ABO are legislative in nature: they have the law or established or modified, or canceled. Normative legal acts is media law;

2) the PPA contains legal instruments by which governing the legal effect.

3) the PPA is published only within the competence of law-making authority;

4) ABO clothed in documentary form and has the following details: type of regulatory act, its name, the body, took it, the date, the place of acceptance of act, number;

5) the PPA is not a chaotic set of provisions (bids), and has a certain structure;

6) ABO must comply with the Constitution or other higher ABO, more legally binding.

7) the PPA must be brought to the attention of citizens and organizations, i.e. the publication, and only then the state has the right to demand strict compliance with the presumption of knowledge of the law and to impose sanctions for his failure.

It should be noted that the term "structured data" in the context of the claimed invention may be considered not only a combination of ABO and and separate PPA consisting, for example: the Constitution, for�it, decree, judgment, etc., a Separate PPA may, for example, consist of parts, chapters, sections, articles. This legal instrument governing the impact of the PPA is a legal rule, convicted in the structure of regulatory requirements, which, in turn, is an element of (part of) law (law).

In the first embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of logical links logical partitions mentioned elements of the first data structure;

B) form a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the aforementioned base Yes�different logical links logical partitions, over and above logical partitions contain the first semantic part and the second semantic parts;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

In the second embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

A) identify the original data structure of the structured data; identify the elements of the source data structure; identify the first logical time�Elah mentioned elements of the source data structure and second logical partitions the elements of the source data structure; and forming the first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of logical links logical partitions mentioned elements of the first data structure;

B) form a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the database logical links logical partitions, and mentioned logical partitions contain the first semantic part and the second semantic parts;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) to form the final�ructure data structured data containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

In the third embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) identify the elements of the first data structure containing one mentioned first logical partition, and the entries in the first data structure containing one mentioned second logical partition; identify the elements of the first data structure containing more than one mentioned first logical partition, and the entries in the first data structure containing more than one mentioned second logical partition; in the elements of the first data structure containing more than one mentioned first logical partition, � elements in the first data structure, containing more than one mentioned second logical partition, identify the logical relationship between said first logical partitions or between said second logical partitions; the first elements of the data structure that contains more than one mentioned first logical partition, and the first elements of the data structure that contains more than one mentioned second logical partition, identify the elements of the first data structure that has no logical connection between the logical partitions; and provide a database of logical links logical partitions elements of the first data structure;

(B) forming a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the database logical links logical partitions, and mentioned logical partitions contain the first semantic part and the second semantic parts;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from the corresponding�following mentioned logical partitions;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

In the fourth embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of logical links logical partitions mentioned elements of the first data structure;

B) form a logical design logical partitions elements of the first data structure using the information from the database log�economic relations of logical partitions of the elements of the first data structure and logical partitions mentioned elements of the first data structure, containing one mentioned first logical partition and logical partitions mentioned elements of the first data structure containing one mentioned second logical partition; and forming a second data structure containing the elements of the second data structure, wherein the elements in the second data structure are formed logical design logical partitions of the first data structure;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

In the fifth embodiment of the present invention provides a method of converting a structured array d�nnyh, containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of logical links logical partitions mentioned elements of the first data structure;

(B) forming a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the database logical links logical partitions, and mentioned logical partitions contain the first semantic part and the second semantic parts;

G) identify the first logical partition of the elements of the second data structure and second logical partitions of the elements of the second data structure; in said first logical partition and the second logical partition of the elements of the second data structure to identify the first semantic part and the second Seeman�algebraic part; and in said first and second logical partitions of the elements of the second data structure to identify at least a particular semantic part of the first logical partition of the elements of the second data structure and a particular semantic part of the second logical partition of the elements of the second data structure and provide a database of special semantic parts of logical partitions of the elements of the second data structure by moving the mentioned special semantic parts formed in said database a special semantic parts of logical partitions of the elements of the second data structure;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

In the sixth embodiment of the present invention provides a method of converting a structured array of data containing at least the text on the natural�Venn language, moreover, the mentioned method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of logical links logical partitions mentioned elements of the first data structure;

(B) forming a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the database logical links logical partitions, and mentioned logical partitions contain the first semantic part and the second semantic parts;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;

D) in said second semantic parts mentioned second logical partition of the elements of the second data structure is identified by m�Nisha least clarifying the semantic structure of second parts of the second logical partition; and providing linguistic transformations over all semantic parts, except for the mentioned special semantic parts mentioned first and second logical partitions, to form grammatically and spelling correct semantic parts of logical partitions of the elements of the second data structure;

(E) form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

In the seventh embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of the hol logic�her logical partitions mentioned elements of the first data structure;

(B) forming a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the database logical links logical partitions, and mentioned logical partitions contain the first semantic part and the second semantic parts;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) forming first and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second mentioned data structures and grammar, and correct spelling of clarifying the structures of the second semantic parts of the second logical partition of the elements of the second data structure, the meaning of the combination grammatically and spelling correct semantic �ASTA second logical partition of the elements of the third data structure; and produce final data structure that contains the elements of the resulting data structure, over and above the elements of the resulting data structures are logical constructs that contain mentioned grammatically and spelling correct semantic part of the logical partitions of the elements of the second data structure.

In the eighth embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) provide a database of logical links logical partitions mentioned elements of the first data structure;

(B) forming a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the aforementioned base �ƈ logical links logical partitions, over and above logical partitions contain the first semantic part and the second semantic parts;

G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;

D) form a grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;

(E) forming first and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second mentioned data structures and grammar, and correct spelling of clarifying the structures of the second semantic parts of the second logical partition of the elements of the second data structure, the meaning of the combination grammatically and spelling correct semantic parts of the second logical partition of the elements of the third data structure; and produce final data structure that contains the elements of the resulting data structure, over and above the elements of the resulting data structures are logical constructs that contain mentioned grammatically and spelling correct semantic part of the logical partitions of the elements of the second data structure; PR�than the logical design from the final data structure optionally may contain mentioned the meaning of the combination formed and grammatically correct spelling semantic parts the second logical partition of the elements of the second data structure.

In the ninth embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

A) identify the original data structure of the structured data; identify the elements of the source data structure; identify the first logical partition of the mentioned elements of the source data structure and second logical partitions the elements of the source data structure; and forming the first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) identify the elements of the first data structure containing one mentioned first logical partition, and the entries in the first data structure containing one mentioned second logical partition; identify the elements of the first data structure containing more than one mentioned first logical partition, and the entries in the first data structure containing more than one mentioned second logical partition; in Fe�tah first data structure, containing more than one mentioned first logical partition, and the first elements of the data structure that contains more than one mentioned second logical partition, identify the logical relationship between said first logical partitions or between said second logical partitions; the first elements of the data structure that contains more than one mentioned first logical partition, and the first elements of the data structure that contains more than one mentioned second logical partition, identify the elements of the first data structure that has no logical connection between the logical partitions; and provide a database of logical links logical partitions elements of the first data structure;

B) form a logical design logical partitions elements of the first data structure using the information from the database logical links logical partitions elements of the first data structure, and logical partitions mentioned elements of the first data structure containing one mentioned first logical partition and logical partitions mentioned elements of the first data structure containing one mentioned second logical partition; and forming a second data structure containing the elements of the second data structure, wherein the said elements of the second structure is given�s are formed logical design logical partitions of the first data structure;

G) identify the first logical partition of the elements of the second data structure and second logical partitions of the elements of the second data structure; in said first logical partition and the second logical partition of the elements of the second data structure to identify the first semantic part and the second semantic parts; and in said first and second logical partitions of the elements of the second data structure to identify at least special semantic part of the first logical partition of the elements of the second data structure and a particular semantic part of the second logical partition of the elements of the second data structure and provide a database of special semantic parts of logical partitions of the elements of the second data structure by moving the mentioned special semantic parts formed in said database a special semantic parts of logical partitions of the elements of the second data structure;

D) in said second semantic parts mentioned second logical partition of the elements of the second data structure to identify at least clarifying the semantic structure of second parts of the second logical partition; and providing linguistic transformations over all semantic parts, except for the mentioned special semantic parts mentioned first� and second logical partitions, to form grammatically and spelling correct semantic parts of logical partitions of the elements of the second data structure;

(E) forming first and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second mentioned data structures and grammar, and correct spelling of clarifying the structures of the second semantic parts of the second logical partition of the elements of the second data structure, the meaning of the combination grammatically and spelling correct semantic parts of the second logical partition of the elements of the third data structure; and produce final data structure that contains the elements of the resulting data structure, over and above the elements of the resulting data structures are logical constructs that contain mentioned grammatically and spelling correct semantic part of the logical partitions of the elements of the second data structure.

In the tenth embodiment of the present invention provides a method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:

A) identify the original data structure of the structured data; identify the elements of the source page�of Keturah data; identify the first logical partition of the mentioned elements of the source data structure and second logical partitions the elements of the source data structure; and forming the first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;

B) identify the elements of the first data structure containing one mentioned first logical partition, and the entries in the first data structure containing one mentioned second logical partition; identify the elements of the first data structure containing more than one mentioned first logical partition, and the entries in the first data structure containing more than one mentioned second logical partition; in the elements of the first data structure containing more than one mentioned first logical partition, and the first elements of the data structure that contains more than one mentioned second logical partition, identify the logical relationship between said first logical partitions or between said second logical partition; in the elements of the first data structure containing more than one mentioned first logical partition, and in the elements of the first article�data structure, containing more than one mentioned second logical partition, identify the elements of the first data structure that has no logical connection between the logical partitions; and provide a database of logical links logical partitions elements of the first data structure;

B) form a logical design logical partitions elements of the first data structure using the information from the database logical links logical partitions elements of the first data structure and logical partitions mentioned elements of the first data structure containing one mentioned first logical partition and logical partitions mentioned elements of the first data structure containing one mentioned second logical partition; and forming a second data structure containing the elements of the second data structure, wherein the elements in the second data structure are formed logical design logical partitions of the first data structure;

G) identify the first logical partition of the elements of the second data structure and second logical partitions of the elements of the second data structure; in said first logical partition and the second logical partition of the elements of the second data structure to identify the first semantic part and the second semantic parts; and in said p�first and second logical partitions of the elements of the second data structure to identify, at least a special semantic part of the first logical partition of the elements of the second data structure and a particular semantic part of the second logical partition of the elements of the second data structure and provide a database of special semantic parts of logical partitions of the elements of the second data structure by moving the mentioned special semantic parts formed in said database a special semantic parts of logical partitions of the elements of the second data structure;

D) in said second semantic parts mentioned second logical partition of the elements of the second data structure to identify at least clarifying the semantic structure of second parts of the second logical partition; and providing linguistic transformations over all semantic parts, except for the mentioned special semantic parts mentioned first and second logical partitions, to form grammatically and spelling correct semantic parts of logical partitions of the elements of the second data structure;

(E) forming first and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second mentioned data structures and grammar, and correct spelling of clarifying the structures of the second semantic parts of the second logs�technical sections of the elements of the second data structure, the meaning of the combination grammatically and spelling correct semantic parts of the second logical partition of the elements of the third data structure; and produce final data structure that contains the elements of the resulting data structure, over and above the elements of the resulting data structures are logical constructs that contain mentioned grammatically and spelling correct semantic part of the logical partitions of the elements of the second data structure; and referred to the logical design from the final data structure optionally may contain mentioned the meaning of the combination formed and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second data structure.

When you do this to a person skilled in the art to which the present invention relates, it should be apparent that embodiments of the invention from the second to the tenth characterize clarified the steps of the method are described the first embodiment of the invention, and other embodiments of the invention can be implemented, and such other embodiments of the invention include various combinations of the specified steps of the method.

In the eleventh embodiment of the present invention provides a device for converting structured data array, comprising at least:

one or more processors;

one or more �of odula input/output (I/O); and

the memory containing program code that, when executed, prompts mentioned one or more processors mentioned device and/or device associated with said device to perform the steps of the method according to any one of embodiments of the present invention from the first to the tenth, and be converted contains one or more structured data containing at least the text in a natural language.

In the twelfth embodiment of the present invention provides a device for converting structured data array, comprising at least:

one or more processors;

one or more input/output (I/O); and

the memory containing program code that, when executed, prompts mentioned one or more processors mentioned device and/or device associated with said device to perform the steps of the method according to any one of embodiments of the present invention from the first to the tenth, and be converted contains one or more structured data containing at least the text in a natural language, and be referred to the transformation of one or more structured data sets are loaded, and the device you�elnino with the possibility of connection with the database, contains downloadable mentioned be converted one or more structured data, for loading in said memory device, at least one downloadable converting structured data array.

In the thirteenth embodiment of the present invention provides a system for converting structured data array, comprising at least:

one or more devices formed in the device according to any one of the eleventh or twelfth embodiments of the present invention;

one or more servers that provide for the regulation of the exchange of data in the system;

one or more databases for storing data are arranged to interact with said one or more devices;

one or more data networks, through which the interaction of the mentioned devices, servers, and databases.

In the fourteenth embodiment of the present invention provides a system for converting structured data array, comprising at least:

one or more devices formed in the device according to any one of the eleventh or twelfth embodiments of the present and�gaining;

one or more servers that provide for the regulation of the exchange of data in the system;

one or more databases for storing data are arranged to interact with said one or more devices;

one or more data networks, through which the interaction of the mentioned devices, servers, and databases; and

the method according to any one of embodiments of the present invention from the first to the tenth by one or more of the servers, as mentioned devices are thin client.

In the fifteenth embodiment of the present invention provides a system for converting structured data array, comprising at least:

one or more devices formed in the device according to any one of the eleventh or twelfth embodiments of the present invention;

one or more servers that provide for the regulation of the exchange of data in the system;

one or more databases for storing data are arranged to interact with said one or more devices;

one or more data networks, through which the interaction of the mentioned devices, servers, and databases; causes�m

the method according to any one of embodiments of the present invention from the first to the tenth by one or more of the servers, as mentioned devices are thin client; and

the database is used to store data representing at least one of: a program code that, when executed, prompts mentioned one or more processors mentioned device and/or device associated with said device to perform the steps of the method according to any one of embodiments of the present invention from the first to the tenth to be converted one or more structured data containing at least the text in a natural language.

In the sixteenth embodiment of the present invention is a system according to any one of the embodiments of the present invention from the thirteenth to the fifteenth, and the said data network is a local area network (LAN), wide area network (WAN), information and telecommunication network Internet, a virtual private network (VPN).

In the seventeenth embodiment of the present invention is provided a machine-readable storage medium containing program code that when executed causes a processor or processors)�an, interacts with machine-readable data storage medium, to perform the steps of the method according to any one of embodiments of the present invention from the first to the tenth.

DETAILED DESCRIPTION of DRAWINGS

This section describes a possible implementation variants of the present invention presented in non-limiting scope of the examples, specific modalities of implementation of the present invention, which in all their aspects are assumed to be illustrative and not constraints. Alternative options for the implementation of the present invention, is not beyond the scope of its legal protection, are obvious to experts in the field, with usual qualifications on which this invention is calculated.

Fig.1 as an example, but not limiting, shows a General diagram of the steps of the inventive method 100 of transforming structured data array that contains at least the text in a natural language. The inventive method 100 of transforming structured data array that contains at least the text in a natural language is characterized by execution of the step 101 of the formation of first data structures, which form a first data structure of the structured data array containing the elements by mentioning�the first data structure, moreover, the elements in the first data structure contain the first logical partition and the second logical partitions; the execution of step 102 of forming a database logical links logical partitions, which form the basis of the data of the logical links logical partitions mentioned elements of the first data structure; performing 103 forming a second data structures, which form the second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the database logical links logical partitions, over and above logical partitions contain the first semantic part and the second semantic parts; the execution of step 104 the formation of a database of semantic parts of logical partitions, which provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions; performing 105 forming grammatically and spelling correct semantic parts ofwhich form grammatically and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts; and the execution stage 106 form the final data structures, which form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

Fig.2 as an example, but not limiting, shows a General diagram of the progress through the stages of the stage 101 of forming the first data structure. The stage 101 is characterized by execution of step 1011 identify the original patterns, which identify the original structure of the structured data 1 data array; performing 1012 identify items, which identify the elements 11 of the source data structure 1; performing 1013 identification of logical partitions, which identifies the first logical sections 111 elements 11 of the source data structure 1 and the second logical partitions 112 elements 11 of the source data structure 1; and performing 1013 formation of first data structures, which form a first data structure 2 structured data array containing the elements 21 of the first data structure, which are the elements 11 of the original structure is given�s 1, moreover, the elements 21 of the first data structure contain the first 2 logical partitions 111 elements 11 of the source data structure 1, and containing the elements 22 of the first data structure, and the elements 22 of the first data structure 2 containing the second logical partitions 112, elements 11 source data structures 1.

Fig.3 as an example, but not limiting, shows a General structure of the source data structure 1, which forms the first data structure 2. The source data structure 1 is a structured array of data containing at least the text in a natural language. As mentioned above, such data may constitute, in particular, the legal act (PPA). The source data structure 1 contains elements 11, which are the sentences grammatically organized connections of words. Each proposal is characterized by the semantic completeness. Identification of proposals in step 1012 element identification is made by identifying the natural language of signs the offer ends. Signs of the end of the proposal are: point, semicolon, dot, etc. Identification of proposals is carried out in conjunction with the identification of signs of the beginning of the sentence. Signs start�La offers are: capital letter, the figure, numeral with a closing parenthesis, digit point etc in the identification also takes into account the presence of certain combinations of punctuation, namely punctuation marks - period, comma, parentheses, colons, etc., - slowerespecially, namely, blank, etc., - and typography - paragraph, number, degree, etc., In step 1012 element identification is the identification of elementary semantic units - sentences that are opinions. Simple judgment is a judgment, no part of which is not a judgment. The linguistic form of expression judgments are declarative sentences. Identification of the elements 11 of the source data structure 1 is carried out by identifying and Defrag offers the primary components of the proposal, namely, the words, particles, conjunctions, prepositions, etc., and punctuation. Then from the primary elements are formed concepts expressed in words and/or phrases on the basis of different reference books and dictionaries. Then, from the generated concepts are simple judgments, which are groups of interrelated concepts, the interrelatedness of concepts is determined on the basis of syntactic or other relationships between concepts. For the formation of simple judgments is linguistic and semantic analysis elem�the components 11 of the source data structure 1, whereby in elements 11 is the identification of structural elements of simple judgments. Under the structural elements of simple judgments are understood to be the subject of the judgment, the predicate of the judgment, ligament and quantifier word. The subject of the judgment (S) is the concept that expresses the subject of the judgment, i.e. what is being said in this judgment. Predicate judgments (P) is the notion of expressing information about the subject of the judgment. The subject of the judgment and the predicate of the judgment is the basic structural elements of the judgment which the terms of the judgment. The relationship between the subject of the judgment and the predicate judgments that reflect the real relationship between imaginable in terms of objects, is revealed by the logical connective. In Russian this link is expressed by the words: "is" ("not"), "is a" ("not"), "there" ("there"), etc., denoted by a dash, a colon, and may be implied in the agreement words ("it rains", "the Dog barks"). Ligament is a logical constant, since it represents the same content - every time she serves as an indicator of the presence or absence of something in the object of thought. "Quantifier" (for example, "every", "all", "none", "some", etc.) indicates whether information about the predicate of the judgment to all of the concepts that Express the subject of the judgment, Il� a portion. For example, in the proposition "Every transgression wrongful act" as the subject of the judgment is the notion of "crime", the predicate of the judgment is "wrongful act", is a bundle expressed a dash, the quantifier and the word "any" indicates that the characteristic of a "wrongful act" refers to the entire volume (for each element) of the concept of "crime". In the most General form of a simple judgment can be expressed by the formula: S is (not) P". Thus, as a result of identification of the source of the data structure 1 elements 11 are divided into a number of judgments which they contain, i.e. the number of judgments is equal to the number of logical partitions in the proposal. Further identifying a first logical partition 111 and second logical partitions 112 elements 11 of the source data structure 1. Identification is performed on the basis of the identification results revealed judgments defined as a complex judgments. Complex judgments is a group of simple judgments, in which there is a relationship between individual judgments established by logical conjunctions "and", "or", "if..., then...", "then and only then... when", "is it true that...". Types of linkages between individual judgments are expressed by logical bundles and are shown in Table 1.

The nature of the con�and is determined by the logical sense of the unions, which is the answer to the question: "Under what conditions is a difficult proposition to be true and what is false?". In other words, under what combinations of truth and falsity of simple judgments that make up a complex proposition, the logical Union defines the true relation, and which are false. The judgment considered as true if given them a description corresponds to reality (the real situation), and false if it does not meet her. "True" and "false" are called truth values, judgments and are the primary logical characterization of judgments. The meaning of logical conjunctions can be determined using a truth table (table 2), which in columns 1 and 2 contain all possible combinations of truth values of the simple propositions, and in columns 3-9 contains the values of complex judgments formed from simple judgments using the appropriate logical Union. The original assertions designated by the letters "A", "b", and the values of the truth of the characters, "and" true, "l" is false.

To identify the first logical partition 111 and second logical partitions 112 elements 11 mentioned the source of the data structure 1 is required in sentences of the text to identify assertions that have a implicative (conditional) logical link and reciprocal(dual) implicative (conditional) relationship. Conditional judgment (implicative proposition) is a complex proposition, in which assertions are combined by a logical Union "if..., then...". For example: "If a citizen breaks the law, this creates a liability for the violation" or "If a number is divisible by 2 without a remainder, then it is odd". Conditional judgment consists of two component species judgments. The judgment is recorded, after the word "if" is called the base (the previous one). The judgment is recorded, after the word "then" is called a consequence of (subsequent). The formula of conditional judgments can be represented as "A→B", where A is the base, B is the effect. The grounds and effects themselves can be as simple judgments and complex judgments. Formed in the preceding and subsequent judgments of conditional judgment, first of all, implies that there can be no way that what they say at the bottom, took place, and what the result was absent. In other words, if the substrate is true and the consequence is false, then such conditional proposition will be false. This condition specifies that a conditional proposition is true in all cases except one: the antecedent is, and no subsequent, i.e. the judgment by the formula "A→B" is false only in one case when A is true and B is false (see table 2, column 6). In the form of conditional judgments could�t can be expressed as an objective according to some objects from others, and the rights and obligations of subjects of legal relations related to these or other conditions. Equivalent judgment (double implication) is a complex proposition, in which judgments are combined with conditional mutual dependence. Equivalent judgments are formed using the Boolean Union "if and only if..., then...", which is denoted by the symbol "↔". The equivalence formula: "A↔B", where A, B - judgments, of which forms the equivalent of a judgment, for example: "Man has the right to retirement pension if and only if it has reached the retirement age. In natural language, including in the economic and legal texts, expressions are equivalent judgments are used grammatical conjunctions: "only under the condition that... that...", "only if..., then...", "if and only if, when..., then...". Conditions equivalent to the truth of propositions is presented in column 7 of table 2. Equivalent proposition is true in two cases - when both components of his judgments are true or when both are false. In other words, the relationship between the elements of the equivalent judgment can be described as necessary: "the truth of A is sufcient for the truth of B and Vice versa" and "the falsity of a measures the falsity of B and Vice versa." For the reason that double implicative judgments there are no distinct OS�hardware and effect, the main factor in the identification of the first and second logical partitions 111, 112 is the presence in the judgments that are in mutual implicative dependencies signs a legal fact. In the example mentioned implicative judgment "Man has the right to retirement pension if and only if it has reached retirement age, you can establish that the proposition "Man is entitled to retirement pension" does not contain such signs, and judgment "if and only if it has reached retirement age" has the symptom of a legal fact, which is the event - "pension age". Thus, it is a judgment that contains elements of a legal fact, recognized for the purpose of identifying the base (A). Another judgment is recognized as the consequence (In). The term "legal fact" means a certain circumstance with which the rule of law binds the emergence, change or termination of legal relations or relations. If the offer contains one simple judgment (or a few simple judgments) or one complex proposition (or more complex judgments) that is not identified as a conditional judgment, the result of linguistic and semantic text analysis, "ambient" such a proposal may be detected actual contextual view of the simple suede�Oia - the first logic section 111 or the second logical partition 112. Formed in step 101, the first data structure contains 2 elements such as sentences (element 11 of the source data structure 1) and the judgment logical sections 111 or 112 of the elements 11 of the source data structure 1. When this judgment is additionally identified on the logical relationship as the Foundation, i.e. as the first logical partition 111 in the source data structure 11 having a logical connection 1 type and which is the judgment, "A", and as a consequence, i.e. as a second logical partition 112 of element 11 of the source data structure 1 having a logical connection 1 type and which judgment "B". In the first data structure 2 the elements of the original data structure 1 separated on the basis of the presence of the mentioned first logical sections 111 or the second logical partition 112 of the source data structure 1, whereby the formed elements 21 of the first data structure 2 having the first logical partition 111, and the elements 22 of the first data structure 2 having the second logical partitions 112.

Fig.4 as an example, but not limiting, shows a General diagram of the progress through the stages of step 102 of forming a database 3 logical links logical partitions. Step 102 of forming a database 3 logical links logical partitions is characterized run�m phase 1021 identify the elements of the first data structure 2, to identify which elements 21 of the first data structure 2 containing one mentioned first logical partition 111 constituting elements 31 of the first data structure 2, and the elements 22 of the first data structure 2 containing one mentioned second logical partition 112 representing the elements 32 of the first data structure 2; the implementation stage 1022 identify the elements of the first data structure 2, which identify the elements 21 of the first data structure containing more than one mentioned first logical partition 111 constituting elements 33 of the first data structure elements 2 and 22 of the first data structure, containing more than one mentioned second logical partition that represents the elements 34 of the first data structure 2; performing 1023 identify logical connections, which is among the elements 33 of the first data structure 2 containing more than one mentioned first logical partition 111 and elements 34 of the first data structure 2 containing more than one mentioned second logical partition 112, identify the logical relationship between said first logical volume 111 or logical connection between said second logical partitions 112; performing 1024 identification of the lack of logical connections, which is among the elements 33 first St�data structure 2, containing more than one mentioned first logical partition 111, and among the elements 34 of the first data structure 2 containing more than one mentioned second logical partition 112, identify the elements 35 of the first data structure 2 having no logical connections between your logical partitions; and performing 1025 development of the database, which provide a database of 3 logical links logical partitions of the elements of the first data structure. To clarify all of the additional (not only implicative) existing logical connections between logical partitions (judgments) all the entries in the first data structure 2, namely arrays of elements 21 of sentences containing the first logical partition 111, and item 22 of sentences containing the second logical partitions 112, it is necessary to separate into groups of elements 31, 33 and 32, 34 containing either only the first logical sections 111 or the second logical partitions 112, respectively. Each element included in the array of elements 31, 33, containing the first logical partition 111, is identified as the element 31, having only one logical partition 111, and as the element 33 having more than one logical partition 111. Thus in the case of identified elements 31, 33 of the second logical partition 112, the second logical partitions 112 are removed�Xia of the identified elements 31, 33. The resulting arrays of elements 31, 33 are still related to the element from which they are allocated and on this basis identified as separate elements of a given array of items. In turn, each element included in the array of elements 32, 34, containing a second logical partition 112 is identified as element 32, having only one logical partition 112, or as an element 34 that has more than one logical partition 112. Thus in the case of identified elements 32, 34 of the first logical partition 111, the first logical sections 111 are removed from the identified elements 32, 34. The resulting arrays of elements 32, 34 remain associated with the element from which they are allocated and on this basis identified as separate elements of a given array of items. It determines the nature of logical connections between the same type of judgments in the elements of two arrays of elements 31, 33 and elements 32, 34. In the same type of elements of arrays of elements 33, 34 identifies a logical connection between the judgments listed in tables 1 and 2, namely, the connecting links (conjunction), the separation of communication (disjunction, strict disjunction), is equivalent to relation (equivalence). Conjunctive connective judgment is a complex judgment, formed from the original judgment by�m logical conjunction "and", denoted by the symbol "". For example, the proposition: "Today I'm going to a lecture on logic and in cinema," is "conjunctive proposition, which consists of two simple propositions (let us denote them by A and B, respectively) - "Today I'm going to a lecture on logic" (A) "Today I'll go to the movies" (In). This complex proposition can be represented by the formula: "AB", where A, B elements of conjunction; ""the logical symbol of the Union is a conjunction. In the Russian language conjunctive Boolean Union is expressed by many grammatical conjunctions "and", "a", "no", "Yes", "although", "however", "but also...". Often such grammatical conjunctions are replaced with punctuation marks - comma, colon, semicolon. Disjunctive (dividing) the judgment is a complex judgment, formed from the "source" of propositions by logical "or", denoted by the symbol "V". For example, the proposition: "the Law can contribute to economic development or hinder it," is a disjunctive proposition, consisting of two simple propositions: "the Law can contribute to economic development and Law can impede economic development." Accordingly, denoting them through the letters A, B, such a proposition may be represented by the formula: "A V B". Because the conjunction "or" is used in two different meanings - and non-exclusive claim�causam, we distinguish between weak and strong (strict) disjunction. Weak disjunction is true in cases when true, at least one of the components of its judgments (or both), and false when both components of its false judgments (see table 2, column 4). Strong disjunction ("VV") differs from the weak disjunction fact that its components are mutually exclusive. For example, "a Crime may have been deliberate or through negligence". In order to emphasize strictly separating precluding the nature of communication in natural language is used reinforced double form of separation: "...either... or...", "...or..., or...", for example: "Either I will find a way, or I'll make it." Strict disjunction is true only when one of its constituent propositions is true and another is false (see table 2, column 5). It identifies all of the logical relationships between items (judgments) of arrays of elements 33, 34. However, it is allowed that some of the elements of these arrays may not have logical relationships with each other. Next, to clarify the type of judgments that have not yet been identified as a complex judgments, the elements of the array elements must be subjected to identification on their compliance deny the judgment. This analysis should be subjected to arrays of elements 31, 32 and, in part, arrays of elements 33, 4, which remained judgments that do not have logical relationships with other judgments. The negated proposition is a complex judgment formed by using the Boolean Union "is incorrect that..." (or just "not"), which, as a rule, is represented by a sign of negation (the " ~ " symbol). Unlike the above-mentioned binary unions, the Union refers to a single judgment. The addition of this Union to any judgment means the formation of a new judgment, which is in particular based on the original judgment is the negated proposition is true if the original proposition is false, and Vice versa (see table 2, columns 8, 9). For example, if the original proposition: "All witnesses are truthful," deny: "true that all witnesses are truthful." If a separate logical partition (simple judgment) is not identified from the point of view of the logical nature of judgment, the result of linguistic and semantic analysis of the text surrounding the proposal, which contains such a section may be detected actual contextual view of this simple judgment. Thus, the first data structure contains logical partitions proposals of the source data structure. According to the results of identification of all the elements of the first data structure identifies all of the logical partitions proposals (judgment), from the point of view of their n�differences and the nature of relationships judgments, forming complex judgments. On the basis of the revealed characters ties judgments (including lack of) is formed database 3 logical links logical partitions (Fig.5).

Fig.6 as an example, but not limiting, shows a General diagram of the progress through the stages of the stage 103 forming a second data structure 4. Stage 103 forming a second data structure 4 is characterized by performing 1031 of the formation of logical structures, which form a logical design logical partitions 41 111, 112 elements 31, 32, 33, 34 of the first data structure 2 using the information from the database 3 logical links logical partitions elements 31, 32, 33, 34 of the first data structure 2 and the logical partitions mentioned elements 31 of the first data structure 2 containing one mentioned first logical partition 111, and logical partitions of the elements 32 of the first data structure 2 containing one mentioned second logical partition 112; and performing 1032 forming a second data structure 4, which form the second data structure 4, containing the elements 41 of the second data structure 4, wherein the said elements of the second data structures 4 are formed logical design logical partitions 41 111, 112 elements 31, 32, 33, 34 of the first data structure 2. Logic 42 - et� the conversion result data of the converted structured data array. Logic 41 are formed in accordance with the specifics of the converted text in a natural language, in particular PPA. The specificity of the PPA is that it contains the law (legal norms). Also the specificity of the PPA is that in theory the rule of law there is the concept of logical rules of law and legal norms. These concepts are not identical. The difference is that the logical rule of law includes the content of all elements of the rule of law established in legal science, including the hypothesis, the disposition and sanction, and the legal rule of law reflects the specific regulations contained in the specific proposals specific PPA. In fact, the difference is that one particular logical rule of law may be contained in a particular set of legal rules of law, i.e. in the set regulations. Logical design is the basis (frame) of the basic regulations, containing two main structural element is a "situation" and "rule" (see tab.3). The basic regulatory requirement (hereinafter, a statutory order) is a tool of legal regulation and includes regulatory and enforcement regulations. In this case, a situation in regulatory requirements means any conditionality rules and� rule refers to any rules including rules (model) behavior of subjects of legal relations. In other words, the situation is a judgment that has implicative logical connection and which is the basis, and the rule is a judgment that has implicative logical connection and which is consequences. In the formation of logical structures 41, i.e. regulations, you must also consider that each of the elements of this design (and the situation and rule) can consist of one judgment, and group judgments. For the formation of logical structures is necessary to use the Database logical links logical partitions. In addition to the identified logical connections between logical sections for the formation of logic must apply to the rules of formation of logical structures. The rules of formation of logical structures reflect the requirements of legal science and legal practice in the composition and structure of the regulations (regulations). For example, the condition that one order can contain two different rules, leads to the fact that the regulations provide that if in one sentence contains two consequences that are logically weak disjunctive relationship, it means that these judgments are different rules and regulations. Thus if� these two effects have a strong logical disjunctive relationship, it unites them in one complex rules within a single instruction. Essentially, the rules of formation of logical structures 42 are reduced to allowable combinations of logical connections between the same type of judgments within the same regulatory requirements.

If there are several situations combined logic "OR" (weak disjunctive communication), this means that each of these situations creates its own individual regulations with the same rules that were used in the first order. This means that several situations with this logic "OR" can't be in the same order. In addition, there are a few rules, combined logic "OR" (weak disjunctive communication). This means that each of these rules forms its own individual regulations with the same situations that were used in the first order. This means that some "rules" with this logic "OR" can't be in the same order. Formed above the second data structure contains items such as judgments (logical partition in the source data structure) and regulations (logical design logical partitions 41 in the source data structure) (Fig.7). With�the frame identified by the presence of implicative logical connection to two of the main logical partition:

1) of the base (the first logical partition in the source data structure containing implicative logical connection, connection of the 1st type, type A);

2) investigation (second logical partition in the source data structure containing implicative logical connection, connection of the 1st type, type);

The grounds and further investigation identified also in fact identify other logical connections between the same type of implicative judgments within the same sentence as additional logical partitions:

1) judgment "And" (logical partition in the source data structure that contains the logical conjunctive connective connection, connection 2nd type);

2) judgment "OR" (logical partition in the source data structure that contains the logical weak disjunctive (separation) relationship, communication is the 3rd type);

3) judgment "OR*" (logical partition in the source data structure that contains a strong logical disjunctive (separation) relationship, communication is the 4th type).

In addition, the above sections can be separately identified as "deny judgment" (logical partition in the source data structure that contains the negated logical link, the link 5 type).

Fig.8 as an example, but not limiting, shows a General diagram of the implementation stages stage� 104 development of the database 5 semantic parts. Step 104 the formation of semantic databases 5 parts characterized by performing 1041 identification of logical partitions, which identifies the first logical sections 411 elements 41 of the second data structure 4 and the second logical partitions 412 of the elements of the second data structure 4; performing 1042 identify semantic parts, where in said first logical sections 411 and second logical partitions 412 of the elements of the second data structure 4 identify the first semantic part 4110 and the second semantic part 4120; and performing 1043 identify specific semantic parts, where in said first and second logical partitions 411, 412 elements 41 of the second data structure 4 identify at least special semantic portion 4111 of the first logical partition elements 411 41 of the second data structure 4 and a special semantic part 4121 second logical partitions 412 elements 41 of the second data structure 4 and establish a database 5 special semantic parts of logical partitions elements 41 of the second data structure 4 by moving the mentioned special semantic parts 4111, 4121 formed in said database 5 special semantic parts of logical partitions elements 41 of the second data structure 4 (Fig.9). Formed in the second data structure a logical�ski design 41 are the framework and the basis of regulatory requirements, but still not a complete match. To achieve maximum compliance structures logical structures 41 the structure of the regulations is necessary to conduct a comprehensive semantic analysis of logical sections 411, 412, including syntactic and logical analysis of terms and concepts, to identify the relationships between the concepts of judgments between the terms and complex concepts. The purpose of this semantic analysis is the detection and identification in logical sections 411, 412 logical structures 41 of the second data structure 4 of a number of specific parts (second parts) of logical partitions that result in:

1) mixing the basic concepts of legal norms - to the confusion of situations and rules by including in the judgment of various factors;

2) blur - the bloom of the meaning of the judgment by including in the judgment of various qualitative and quantitative refinements and detail.

At this stage is the identification of specific parts, i.e. identifying logical sections 411, 412 logical structures 41 of the second data structure of the first and second semantic parts 4110, 4120 logical sections 411, 412. The first semantic part 4110 formed by the removal of a logical sections 411, 412 of the second semantic parts 4120 (specific parts). Parasimpaticescoe part 4110 logical partition - this is the conceptual core of judgments, i.e. judgments, stripped of specific parts. The semantic core of a judgment are the basic elements of judgment, such as the subject of the judgment, the predicate of the judgment and ligament. Feature bundles is that the ligament is part of the semantic kernel judgment only when it can be interpreted in "bulk", in cases when the ligament reveals the inclusion (or exclusion) of a subclass in the class of objects or accessory (not belonging) of the element class. For example, in the judgment: "a Crime is an illegal act," the subject of the judgment is the word "crime", the predicate of the judgment the phrase "wrongful act", and a bunch of the word "is". The second semantic part 4120 logical partition - term and judgments which are identified as signs of the subject of the judgment, the predicate of the judgment, as well as the terms of the judgment - bundle (when it can be interpreted in "bulk") and quantifier word, and other special parts. For example, the concept (the subject of the judgment) "crime provided by the Criminal Code," contains the notion of "crime" and a sign of concepts - the phrase "under the Criminal Code". The signs of the concept is the content of the concept, indicating the presence or absence of a particular property, SOS�of oania or relationships. In other words, the characteristic concept is all what concepts may be similar or distinct from each other. All signs of the concepts that form the content of the concepts identified as relevant and irrelevant on the principle of the loss of quality (inability to be oneself) without this trait. For example, in the judgment: "a Crime is a wrongful act", is the predicate of the judgment is the concept of "wrongful act" (In), which is a complex concept or "attitude" in which "the act" is the subject of the judgment (A), and the term "illegal" is a symptom of A. For example, the "relationship" a "wrongful act" shows that there are "concepts", in which the volume of one fully included in the scope of another, but does not exhaust it. In other words, all elements of the volume (V) are elements of volume (A), but not Vice versa. Form of these relations is the "obedience", i.e. rodowodowe a relationship where a more General "concept" is born, and less common species. Quantifier a word indicates whether information about the predicate of the judgment to all of the concepts that Express the subject of the judgment, or part thereof. For example, in the proposition: "Every transgression there is a wrongful act", - quantifier word "any" indicates that information about the subject of the judgment (the phrase "wrongful act") applies to all�the volume (each volume element) of the subject judgment - the word "crime". Under other special parts are defined as separate concepts and groups of concepts, judgements, which also clarify the meaning of concepts constituting the first semantic part 4110 logical partitions 41, but does not formally belong to the characteristics of the concept, conjunction or quantifier word. For example, in the judgment: "Crime (including fraud, theft, murder) a wrongful act," the concepts given in parentheses ("fraud" and "theft", "murder") are real (life) examples of the concept of "crime". The basis of the concept of "wrongful" is essential to the concept of "act", because without it, the concept of "wrongful act" loses its quality, ceases to be himself. The scope is a class (set) conceivable in the concept of entities. The signs of the concept and the scope of interrelated within each concept. This relationship allows you to set the real volume of the concept, i.e. what is really implied in the semantic content of the notion. To achieve maximum compliance structures logical structures 41 the structure of the regulations is necessary to separate the species of the second semantic parts 4120. From a technical point of view, identified in the comprehensive semantic analysis of other special parts are not El�entom main (regulatory or enforcement) regulations. In this regard, they are removed from logical partitions and establish a database 5 special semantic parts, representing a regulatory guide or other normative reference material representing a variety of special regulations. The information in this guide is available and relevant, but structurally and methodologically, it is beyond the scope of main (regulatory and enforcement) regulations (Fig.9). From the point of view of legal science special regulations are regulations establishing basic principles, mechanisms, procedures and purpose of the legal regulation of social relations, reinforce the legal categories and concepts (for example, definitive provisions - provisions enshrining in summary, the indications or other legal concepts).

Fig.10 as an example, but not limiting, shows a General diagram of the implementation stages stage 105 forming grammatically and spelling correct semantic parts, which form a grammatically and spelling correct semantic parts mentioned logical partitions 41 by linguistic transformations above mentioned semantic parts. Step 105 involves performing stage 1051 identification clarifying structures, which is referred to in the second� semantic parts 4120 mentioned second logical partitions 412 elements 41 of the second data structure 4 identify, at least clarifying the semantic structure of second 4122 parts of the second logical partitions 412; and performing 1052 linguistic transformations, comprising linguistic transformations over all semantic parts, except for the mentioned special semantic parts 4111, 4121 mentioned first and second logical partitions 411, 412 to form grammatically and spelling correct semantic parts 4123 structures and clarifying 4122 logical partitions elements 41 of the second data structure 4. The General scheme of the received second data structure 4 shown in Fig.11. To achieve maximum compliance structures logical structures 41 the structure of the regulations needed to further identify remaining second semantic parts. Additional identification is also performed as part of a comprehensive semantic analysis. The subject of the analysis will be an array of values identified these types of second semantic parts 4120, i.e. an array of concepts contained in the logical partitions and identified as the species concerned. Each concept data arrays must be identified from the point of view of his belonging to the refinements 4124 or dependency 4125. When this specification is such characteristics�and concepts which makes the transition from wider to narrower concept, and based, contain elements of a legal fact, i.e. a certain event, in the presence (absence) of which the concept refers to the dependence, updated, or Vice versa becomes irrelevant. Linguistic transformations over all semantic parts of logical partitions associated with the restoration of correct grammar and spelling separate semantic parts that will be required in connection with the actual division of the text sentences into separate parts - semantic part 4110, 4120 logical partitions, and given the removal of special semantic sections 4111, 4121 from the specified text. Under specified linguistic change is understood, in particular, the harmonization of birth, numbers, cases, edit (change and delete) inappropriate punctuation.

Fig.12 as an example, but not limiting, shows a General diagram of the progress through the stages of the stage 106 form the final data structure 6, which form the final structure of the structured data 6 data array containing the elements 61 mentioned final data structure 6, and the said elements 61 final data structures contain logic 61, comprising at least mentioned grammatically and orfograficheskii� correct semantic part 4123 logical partitions. The stage 106 is characterized by performing 1061 formation of semantic combinations, which form the first and grammatically correct spelling semantic parts 4123 second logical partitions 412 elements 41 of the second data structure 4 and mentioned grammatically and spelling correct lookup structures 4122 second semantic parts 4120 second logical partitions 412 elements 41 of the second data structure 4 the meaning of the combination 611 grammatically and spelling correct semantic parts 4122, 4123 second logical partitions 412 elements 41 of the second data structure 4; and performing 1062 form the final data structure 6, which form the final data structure 6, containing elements 61 final data structure 6, and the said elements 61 final data structures 6 are logical structures 61, containing mentioned grammatically and spelling correct semantic part 4122, 4123 logical partitions elements 41 of the second data structure 4. It is also possible when the logical design 61 from the final data structure 6 also have mentioned the meaning of the combination formed 611 grammatically and spelling correct semantic parts 4122, 4123 second logical partitions 412 elements 41 of the second data structure 4 (Fig.13). It�engineering General structure of the element 61 final data structure 6 shown in Fig.14. The basis for the regulations is a legal rule (rule), which can be as well-formed as a result of the complex semantic analysis of logical partitions logical structures. At the stage of forming logical structures most of the conditioning was separated from the rules (second logical partition 412) and a separate section for the first logical partition 411. According to the results of a comprehensive semantic analysis revealed the conceptual core rules (second logical partition 412) and the remains of conditioning were also highlighted in the second semantic part 4120, in which some of the parts identified as clarification 4124. As a result of all the transformations made possible in the structure of the logical structures 61 to create the meaning of the combination 611, i.e. the combination of the first semantic parts 4110 second logical partition 412 and second semantic parts 4120 second logical partitions 412, identified as a refiner 4124. These semantic combinations are legal rules. The resulting data structure is a structured design, the elements of which correspond to the structure of regulations. The resulting data structure generated in this form for the purpose of simplification and re�lament professional work on the creation and updating of laws and regulations. The resulting data structure is a structure that allows you to literally visualize regulations, to see all the factual elements of the semantic structure, which allows them full-fledged multilateral analysis for the purpose of conducting a point adjustments as existing regulations and project requirements at different stages of their creation.

Fig.15 as examples, but not limitations, illustrated is an exemplary diagram of the inventive system 200 of converting structured data array, which in the preferred embodiment contains at least one or more devices 201 transformation of a structured array of data containing at least one or more processors 2011, one or more modules input/output (I/O) 2012 2013 and memory. Mentioned device 201 transformation of a structured array of data may represent, but not be limited to: a personal computer, portable computer, tablet computer, pocket PC, smartphone, thin client, and the like. Memory (machine-readable storage medium) 2013 device 201 transformation of a structured array of data that contains the program code that, when executed, prompts mentioned one or more processors 2011 mentioned device 21 and/or the device 201, associated with the device 201, to perform the actions described above, the method of converting the structured data array, and be converted contains one or more structured data containing at least the text in a natural language. Moreover, due to the conversion of one or more structured data sets can be loaded and stored, in particular, in the database system 203 converting structured data array. As an example, but not limitation, computer-readable storage medium may include random access memory (RAM); read only memory (ROM); an electrically-erasable programmable read only memory (EEPROM); flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical or holographic media; magnetic cassettes, magnetic tape storage device on magnetic disks or other magnetic storage devices, carrier waves, or other storage medium that can be used to encode the required information, and which can be accessed by means of the described device. The memory includes a data medium on the basis of memory and storage devices in the form of energy�dependent or non-volatile memory, or combinations thereof. Exemplary hardware devices include solid-state memory, hard drives, optical drive, etc is stored In the memory exemplary environment in which using computer commands or codes stored in the memory device, can be carried out the conversion of a structured data set. The device comprises one or more processors 2011, which are designed to execute computer commands or codes stored in the memory device to ensure the procedure of converting structured data array. Computer commands or codes stored in memory, are designed to convert structured data array. These commands and codes include, at a minimum, the team of forming a first data structure of a structured array of data, the team building the database of logical connections, commands, forming a second data structure of a structured array of data, the team building the database of semantic parts of logical partitions, team formation and grammatically correct spelling semantic parts, teams form the final data structure of a structured array of data and commands intended for vypolnyayuthij teams. Modules I/O 2012 device 201 are not limited to, typical and known in the art management tools device: Microsoft mouse, keyboard, joystick, touchpad, trackball, stylus, stylus, touch-screen display and the like. Also modules I/O 2012 are not limited to, typical and known in the art tools for showing information: display, monitor, projector, printer, plotter, and the like. The system 200 also may include a database (DB) 202. The database 202 may be, but not limited to: a hierarchical database, network database, relational database, object database, object-oriented databases, object-relational databases, spatial databases, the combination of the above two or more databases, and the like. The database 202 stores data in memory, which may be, but not limited to: read only memory (ROM), electrically-erasable programmable read only memory (EEPROM), flash memory, CDROM, digital versatile disk (DVD) or other optical or holographic media; magnetic cassettes, magnetic tape storage device on magnetic disks or other magnetic storage devices, carrier waves, or other storage medium, which can be used to store desired �nformation and which can be accessed through the device 201 transformation of structured data array and the server 203. The database 202 is used to store data representing at least the team of forming a first data structure of a structured array of data, the team building the database of logical connections, commands, forming a second data structure of a structured array of data, the team building the database of semantic parts of logical partitions, team formation and grammatically correct spelling semantic parts, teams form the final data structure of a structured array of data and commands intended for the implementation teams; be converted one or more structured data containing at least the text in a natural language, which can be loaded into memory 2013 device 201 transformation of structured data; and other data necessary for the functioning of the system. An exemplary system 200 of converting structured data array further comprises a server computing device (server) 203, which preserves and promotes the manipulation of computer commands or codes described earlier in this document, which, accordingly, are not further described. The server 203 may be a personal computer, partitionnumber, tablet PC, pocket PC, smartphone, car, databases and the like. The server 203 provides for the regulation of the exchange of data in the system 200 of converting structured data, and also allows the processing of data in connection thereto of one or more devices 201 transformation of a structured array of data or when the device 201 transformation of structured data array is a thin client. In this case, all the processing power needed to ensure the implementation of the transformation procedure of a structured array of data located on the server 203. The system 200 also includes one or more networks 204 data. Network 204 data may include, but not be limited to, one or more local area networks (LAN) and/or wide area networks (WANs), or may constitute the information-telecommunication network Internet, or Intranet or a virtual private network (VPN), or a combination thereof, and the like. The server 203 also has the ability to provide virtual computing environment (Virtual Machine) to ensure interoperability between the device 201 transformation of structured data array and the database 202. The network 204 is used to ensure communication between the device 201, the database 202 and the server system 203�s 200 conversion structured data array.

1. A method of converting a structured array of data containing at least the text in a natural language, wherein the said method comprises at least the stages on which:
(A) forming a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;
B) provide a database of logical links logical partitions mentioned elements of the first data structure;
(B) forming a second data structure of the structured data array containing the elements mentioned second data structure, wherein the elements in the second data structure to contain the logical design logical partitions mentioned elements of the first data structure is generated using information from the database logical links logical partitions, and mentioned logical partitions contain the first semantic part and the second semantic part;
G) provide a database of semantic parts of logical partitions from the second semantic parts, and the second mentioned semantic parts are excluded from mentioned respective logical partitions;
D) form grammaticus�and spelling correct semantic parts mentioned logical partitions by linguistic transformations above mentioned semantic parts;
(E) form the final data structure of the structured data array containing the elements mentioned final data structure, over and above the elements of the resulting data structures contain a logical structure that contains at least mentioned grammatically and spelling correct semantic part of the logical partitions.

2. A method according to claim 1, characterized in that step A) is at least the stages at which:
- identify the original data structure of the structured data;
- identify the elements of the source data structure;
- identify the first logical partition of the mentioned elements of the source data structure and second logical partitions the elements of the source data structure; and
- form a first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions.

3. A method according to claim 1, characterized in that step B) is characterized at least by stages, in which:
- identify the elements of the first data structure containing one mentioned first logical partition, and the entries in the first data structure containing one mentioned second logical section�;
- identify the elements of the first data structure containing more than one mentioned first logical partition, and the entries in the first data structure containing more than one mentioned second logical partition;
- among the elements of the first data structure containing more than one mentioned first logical partition, and the first elements of the data structure that contains more than one mentioned second logical partition, identify the logical relationship between said first logical partitions or between said second logical partitions;
- among the elements of the first data structure containing more than one mentioned first logical partition, and the first elements of the data structure that contains more than one mentioned second logical partition, identify the elements of the first data structure that has no logical connection between the logical partitions; and
- provide a database of logical links logical partitions of the first elements of the data structure.

4. A method according to claim 1, characterized in that stage b) is characterized at least by stages, in which:
- form a logical design logical partitions elements of the first data structure using the information from the database logical links logical partitions of the elements of the first structure is given�'s and logical partitions mentioned elements of the first data structure, containing one mentioned first logical partition and logical partitions mentioned elements of the first data structure containing one mentioned second logical partition; and
- form the second data structure containing the elements of the second data structure, wherein the elements in the second data structure are formed logical design logical partitions of the first data structure.

5. A method according to claim 1, characterized in that step D) is characterized at least by stages, in which:
- identify the first logical partition of the elements of the second data structure and second logical partitions of the elements of the second data structure;
is in said first logical partition and the second logical partition of the elements of the second data structure to identify the first semantic part and the second semantic parts; and
- mentioned in the first and second logical partitions of the elements of the second data structure to identify at least a particular semantic part of the first logical partition of the elements of the second data structure and a particular semantic part of the second logical partition of the elements of the second data structure and provide a database of special semantic parts of logical partitions of the elements of the second data structure by moving the mentioned special semanti�die parts formed in said database a special semantic parts of logical partitions of the elements of the second data structure.

6. A method according to claim 1, characterized in that step D) is characterized at least by stages, in which:
is in said second semantic parts mentioned second logical partition of the elements of the second data structure to identify at least clarifying the semantic structure of second parts of the second logical partition; and
- carry out linguistic transformations over all semantic parts, except for the mentioned special semantic parts mentioned first and second logical partitions, to form grammatically and spelling correct semantic parts of logical partitions of the elements of the second data structure.

7. A method according to claim 1, characterized in that step E) is characterized at least by stages, in which:
is formed of first and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second mentioned data structures and grammar, and correct spelling of clarifying the structures of the second semantic parts of the second logical partition of the elements of the second data structure, the meaning of the combination grammatically and spelling correct semantic parts of the second logical partition of the elements of the second data structure; and
- produce final data structure that contains the elements of the resulting data structure, p�eacham mentioned the elements of the resulting data structures are logical constructs, contains mentioned grammatically and spelling correct semantic part of the logical partitions of the elements of the second data structure.

8. A method according to claim 7, characterized in that said logical design from the final data structure optionally may contain mentioned the meaning of the combination formed and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second data structure.

9. A method according to claim 1, characterized in that said method for converting a structured array of data containing at least the text in a natural language that contains at least the time that:
A) identify the original data structure of the structured data; identify the elements of the source data structure; identify the first logical partition of the mentioned elements of the source data structure and second logical partitions the elements of the source data structure; and forming the first data structure of the structured data array containing the elements mentioned first data structure, wherein the elements in the first data structure contain the first logical partition and the second logical partitions;
B) identify the elements of the first data structure containing one mentioned p�first logical partition, and the entries in the first data structure containing one mentioned second logical partition; identify the elements of the first data structure containing more than one mentioned first logical partition, and the entries in the first data structure containing more than one mentioned second logical partition; in the elements of the first data structure containing more than one mentioned first logical partition, and the first elements of the data structure that contains more than one mentioned second logical partition, identify the logical relationship between said first logical partitions or between said second logical partitions; the first elements of the data structure that contains more than one mentioned first logical partition, and in the elements of the first data structure containing more than one mentioned second logical partition, identify the elements of the first data structure that has no logical connection between the logical partitions; and provide a database of logical links logical partitions elements of the first data structure;
B) form a logical design logical partitions elements of the first data structure using the information from the database logical links logical partitions elements of the first data structure and logical partitions of womenoriented first data structure, containing one mentioned first logical partition and logical partitions mentioned elements of the first data structure containing one mentioned second logical partition; and forming a second data structure containing the elements of the second data structure, wherein the elements in the second data structure are formed logical design logical partitions of the first data structure;
G) identify the first logical partition of the elements of the second data structure and second logical partitions of the elements of the second data structure; in said first logical partition and the second logical partition of the elements of the second data structure to identify the first semantic part and the second semantic parts; and in said first and second logical partitions of the elements of the second data structure to identify at least special semantic part of the first logical partition of the elements of the second data structure and a particular semantic part of the second logical partition of the elements of the second data structure and provide a database of special semantic parts of logical partitions of the elements of the second data structure by moving the mentioned special semantic parts formed in said database a special semantic parts of logical partitions elements �Torah data structure;
D) in said second semantic parts mentioned second logical partition of the elements of the second data structure to identify at least clarifying the semantic structure of second parts of the second logical partition; and providing linguistic transformations over all semantic parts, except for the mentioned special semantic parts mentioned first and second logical partitions, to form grammatically and spelling correct semantic parts of logical partitions of the elements of the second data structure;
(E) forming first and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second mentioned data structures and grammar, and correct spelling of clarifying the structures of the second semantic parts of the second logical partition of the elements of the second data structure, the meaning of the combination grammatically and spelling correct semantic parts of the second logical partition of the elements of the third data structure; and produce final data structure that contains the elements of the resulting data structure, over and above the elements of the resulting data structures are logical constructs that contain mentioned grammatically and spelling correct semantic part of the logical partitions elem�the components of the second data structure.

10. A method according to claim 9, characterized in that said logical design from the final data structure optionally may contain mentioned the meaning of the combination formed and grammatically correct spelling of the semantic parts of a second logical partition of the elements of the second data structure.

11. Conversion device structured data array, comprising at least:
one or more processors;
one or more input/output (I/O); and
the memory containing program code that, when executed, prompts mentioned one or more processors mentioned device and/or device associated with said device to perform the steps of the method according to any one of claims.1-10 formula, and be converted contains one or more structured data containing at least the text in a natural language.

12. The device according to claim 11, characterized in that said be converted one or more structured data sets are loaded, and the said device is arranged to connect to the database that stores mentioned loadable be converted one or more structured data, for loading in said memory device�and, at least one downloadable converting structured data array.

13. Transformation system for structured data array, comprising at least:
one or more devices formed in the device according to any of claims.11 or formula 12;
one or more servers that provide for the regulation of the exchange of data in the system;
one or more databases for storing data are arranged to interact with said one or more devices;
one or more data networks, through which the interaction of the mentioned devices, servers, and databases.

14. A system according to claim 13, characterized in that the method according to any one of claims.1-10 formula is implemented by one or more of the servers, as mentioned devices are thin client.

15. A system according to claim 14, characterized in that said database is used to store data representing at least one of: a program code that, when executed, prompts mentioned one or more processors mentioned device and/or device associated with said device to perform the steps of the method according to any one of claims.1-10 formula to be converted one or more structured data sets containing �about least the text in natural language.

16. System according to any of claims.13-15, characterized in that said data transmission network is one of: a local area network (LAN), wide area network (WAN), information and telecommunication network Internet, a virtual private network (VPN).

17. The machine-readable storage medium containing program code that when executed causes a processor or processors of the device, which interacts with the machine-readable storage medium, to perform the steps of the method according to any one of claims.1-10 formula.



 

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17 cl, 12 dwg

FIELD: physics, computer engineering.

SUBSTANCE: invention relates to computer engineering and specifically to automated intelligent assistant systems. Disclosed is method of operating an automated intelligent assistant. The method is carried out in an electronic device having a processor and memory which stores instructions for execution by the processor. The processor executes instructions on which a user request is received, wherein the user request includes a speech input received from the user. Further, the method comprises identifying a respective candidate domain relevant to the user request from a plurality of predefined domains, wherein each predefined domain presents a respective area of service offered by the automated intelligent assistant, and wherein the respective candidate domain provides a reservation service to the user.

EFFECT: automating interaction of a user with an electronic device through an automated intelligent assistant.

15 cl, 50 dwg, 5 tbl

FIELD: physics, computer engineering.

SUBSTANCE: invention relates to computer engineering and specifically to automated intelligent assistant systems. The method is carried out in an electronic device having a processor and memory which stores instructions for execution by the processor. The processor executes instructions on which a user request is received, wherein the user request includes a speech input received from the user. Further, the user is presented with an echo of the speech input based on a textual interpretation of the speech input. A task to be executed by an electronic device is also determined from a plurality of tasks which can be executed by the mobile device. The task is determined by processing natural language with respect to the speech input.

EFFECT: automating interaction of a user with an electronic device through an automated intelligent assistant; disclosed is a method of operating an automated intelligent assistant.

12 cl, 50 dwg, 5 tbl

FIELD: physics, computer engineering.

SUBSTANCE: invention relates to a method of identifying and classifying an object. The method comprises the following steps: detecting an object using at least one physical detector disposed on said object; using the output signal of the detector, by forming a threshold value, and an analysing device to detect at least one object defined as a singly-connected region having defined physical properties which uniquely distinguish the object from other objects; identifying and/or classifying the object from the output signal based on predetermined properties; using the output signal for the object to deduce multiple different physical features; based on the selected physical features, associating the object with at least one of N predetermined base classes; ordering the N base classes in a predetermined sequence into an N-dimensional vector V, which is associated with the object, wherein elements v1…vN of the vector V indicate the identity of the object to the corresponding base class; depending on the vector V, associating the object with an arbitrary class selected from a reference database, wherein if the object belongs to the corresponding base class, the vector element v1…vN is assigned a binary value "1", otherwise the binary value "0" is assigned.

EFFECT: faster identification and classification of objects by predefining N base classes ordered by an N-dimensional vector V.

2 cl, 2 tbl

FIELD: information technology.

SUBSTANCE: method of determining vulnerable functions in automated scanning of web applications for presence of vulnerabilities and non-declared capabilities comprises compiling a list of source texts of web applications intended for generating testing parameters, and setting source text parameters for testing; parsing the source texts using the given parameters and adding distinctive labels to the source text with indication of label-function pairs; performing automatic scanning and search for program errors in web applications and, in case of error, obtaining debugging data in the form of machine code, describing the currently executed module and containing the name of the corresponding label; determining, from said label, the corresponding label-function pair and obtaining the name of the vulnerable function, as well as the full name of the module containing the vulnerable function.

EFFECT: high number of potentially detected vulnerabilities of web applications, shorter time needed for manual analysis of program errors in order to determine criticality thereof.

3 cl

FIELD: information technology.

SUBSTANCE: method for automatic semantic classification of natural language texts comprises presenting each text to be classified in digital form for subsequent processing; indexing the text to obtain elementary units of the first through fifth levels; detecting the frequency of occurrence of units of the fourth level, each being a semantically significant object or attribute, and the frequency of occurrence of semantically significant relationships linking semantically significant objects, as well as objects and attributes; forming a semantic network from a triad which is units of the fifth level; renormalising the frequencies of occurrence into the semantic weight of the units of the fourth level; ranking the units of the fourth level according to the semantic weight by comparison thereof with a threshold value and those having a weight below the threshold value; detecting the degree of crossing semantic networks of the text and text samples; selecting as a class for text object regions, the degree of crossing the semantic network with the semantic network of text is greater than the threshold.

EFFECT: faster process of comparing texts.

6 cl, 2 dwg, 24 tbl

FIELD: physics, computer engineering.

SUBSTANCE: invention relates to information technology. The disclosed method includes presenting two texts to be compared in digital form for subsequent processing; indexing the texts to obtain elementary units of first to fifth levels; detecting the frequency of occurrence of elementary units of the fourth level, each being a semantically significant object or attribute, and the frequency of occurrence of semantically significant relationships linking semantically significant objects, as well as the semantically significant objects and attributes; storing the formed elementary units of the second to fifth levels, and the obtained indices together with links to specific sentences of said text; forming from a triad, which are elementary units of the fifth level, a semantic network; ranking the elementary units of the fourth level according to semantic weight by comparing the semantic weight of each of them with a predetermined threshold and removing elementary units of the fourth level having a semantic weight below the threshold; detecting for two compared texts the degree of crossing of their semantic networks.

EFFECT: faster process of comparing texts.

4 cl, 2 dwg, 26 tbl

FIELD: information technology.

SUBSTANCE: method of generating syntactically and semantically correct commands includes converting a text Backus-Naur form (BNF), containing a command meta-description, into a relational BNF containing recognisable SUBD command meta-description. A text semantic rule containing a command usage restriction is converted to a relational semantic rule containing a recognisable SUBD command usage restriction. A command is identified and a basic rule is assigned for the identified command, wherein the basic semantic rule consists of a plurality of relational semantic rules. A resultant dynamic structure is formed for the identified command. Elements of the basic semantic rule are identified for the identified command and all elements of all relational semantic rules are applied to the identified command. A syntactically and semantically correct command is then generated.

EFFECT: automation and high accuracy of generating SUBD commands and less amount of computations required to generate SUBD commands.

38 cl, 18 dwg

FIELD: information technology.

SUBSTANCE: method for automatic semantic indexing of natural language text comprises segmenting the text into elementary first level units (words) and sentences; forming second level units (standardised word forms); calculating the frequency of occurrence of each first level unit for adjacent first level units and merging the sequence of words into third level units (stable word combinations); identifying in each sentence a semantically significant entity and an attribute thereof (fourth level units); identifying in each sentence semantically significant relationships between semantically significant entities and between semantically significant entities and attributes; determining the frequency of occurrence of second level and third level units; forming, for each semantically significant relationship, a plurality of triads (fifth level units); on the plurality of the formed triads, separately indexing all semantically significant entities linked by semantically significant relationships with their frequency of occurrence, all attributes with their frequency of occurrence and all formed triads.

EFFECT: high accuracy of indexing natural language texts.

6 cl, 2 dwg, 23 tbl

FIELD: information technology.

SUBSTANCE: programming language parsing method is based on table LR parsing. Canonical LR tables of a parser are dynamically rearranged during compilation using grammar extension directives given separately for each hierarchy level of nesting grammatical rules of the programming language, said directives being intended for inputting new grammatical structures. The compiler continues parsing of the program using the rearranged LR tables.

EFFECT: enabling dynamic modification of compilation tables which form the basis for a parser by extending the grammar of the programming language.

5 cl

FIELD: information technology.

SUBSTANCE: method includes a step for syntax analysis of text. A step for extracting text components and relationships thereof in the text is then executed. A graph or graphic representation of the text is generated or used as representation of the meaning of the text independent of the language. That graph or graphic representation is used to perform modelling, knowledge presentation and processing in a language processing system. A judgment of the representation in the model of the semantic realm is made during the processing step, thereby checking consistency of the extracted text semantics.

EFFECT: improvement and further advancement of the method of processing natural language which enables to properly process text semantics or other data.

29 cl, 15 dwg

FIELD: information technology.

SUBSTANCE: method of classifying documents by categories includes constructing ontology in form of a set of categories. For each category, terms, i.e. sequences of words typical for texts in said category, are identified and the weight of each of the identified terms is determined when reading electronic versions of the documents from a training collection of documents. A profile is formed for each of the categories in form of a list of all terms in all ontology categories with indication of the weight of each term in said category. A list of possible combinations word forms of said term is compiled for each term. Identified terms are selected in each document to be classified when reading an electronic version thereof, considering only word forms from the compiled list. For each document to be classified, a profile is formed for each category based on the selected terms. Relevance of said document to each category is determined by comparing profiles of said document with profiles of categories in the ontology. A classification spectrum of the document is constructed in form of a set of categories with relevance found for each of them.

EFFECT: high rate of classification and reduced size of consumed memory.

7 cl

FIELD: information technologies.

SUBSTANCE: method is realised for building of semantic relations between elements extracted from document content, in order to generate semantic representation of content. Semantic representations may contain elements identified or analysed in the text part of the content, elements of which may be associated with other elements, which jointly use semantic relations, such as relations of an agent, a location or a topic. Relations may also be built by means of association of one element, which is connected to another element or is near, thus allowing for quick and efficient comparison of associations found in the semantic representation, with associations received from requests. Semantic relations may be defined on the basis of semantic information, such as potential values and grammatical functions of each element within the text part of the content.

EFFECT: provision of quick detection of most relevant results.

21 cl, 11 dwg

FIELD: information technology.

SUBSTANCE: method of constructing a semantic model of a document consists of two basic steps. At the first step, ontology is extracted from external information resources that contain descriptions of separate objects of the object region. At the second step, text information of the document is tied to ontology concepts and a semantic model of the document is constructed. The information sources used are electronic resources, both tied and untied to the structure of hypertext links. First, all terms of the document are separated and tied to ontology concepts such that each term corresponds to a single concept which is its value, and values of terms are then ranked according to significance for the document.

EFFECT: enabling enrichment of document with metadata, which enable to improve and increase the rate of comprehension of basic information, and which enable to determine and highlight key terms in the text, which speeds up reading and improves understanding.

15 cl, 6 dwg

FIELD: computer science.

SUBSTANCE: method includes text messages from data channel, linguistic words processing is performed, thesaurus of each text message is formed, statistical processing of words in thesaurus is performed, text message and thesaurus are stored in storage. Membership of text message in one of categories from the list is determined, starting data value of text message is determined, stored in storage with text message, data value values are periodically updated with consideration of time passed since their appearance and text messages with data value below preset threshold are erased, during processing of each message values of categories classification signs are updated.

EFFECT: higher efficiency.

1 dwg

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