Method for synthesizing a self-learning system for extraction of knowledge from textual documents for use in search

• US 20050071150A1
• Filed: 05/28/2002
• Published: 03/31/2005
• Est. Priority Date: 05/28/2002
• Status: Abandoned Application

• Current Assignee: Vladimir Vladimirovich Nasypny
• Original Assignee: Vladimir Vladimirovich Nasypny
• Inventors: Nasypny, Vladimir Vladimirovich
• Application Number:
  US10/469,449
• Publication Number:
  US 20050071150A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/3344 using natural language analysis

Abstract

The invention relates to computer science, information-search and intelligent systems, and can be used in developing information-search and other information and intelligent systems that operate on the basis of Internet. The invention provides the possibility of automatic creation of knowledge by extraction of knowledge from textual documents in electronic form in different languages; intelligent processing of textual information and users'"'"' requests to extract knowledge in any foreign language. The claimed method provides a mechanism of self-learning in the form of a stochastically indexed system of artifical intelligence, providing automatic instruction of the system in rules of grammatical and semantic analysis. The method includes creating databases of stochastically indexed dictionaries, tables of indices of linguistic texts and knowledge bases of morphological analysis; performing morphological and syntactical analysis, and also stochastic indexing of textual documents in respect to a given theme from the search system in a given language, and creating knowledge base of syntactical analysis. Stochastically indexed textual documents pertaining to the given theme are subjected to semantic analysis, and knowledge bases of semantic analysis. A user'"'"'s request is compiled and transformed, in the stochastically indexed form, into a plurality of new requests that are equivalent to the original request; and stochastically indexed fragments of textual documents that comprise all word combinations of the transformed request are selected. A stochastically indexed structure is generated from the selected documents and basing on said structure by means of logical conclusion a brief reply of the system is generated. Relevancy of the obtained brief reply is checked by generating an interrogative sentence based on said reply, and by comparing said sentence with the request. When the user'"'"'s request is identical to the obtained interrogative sentence, the decision is made that the brief reply of the system is identical to the request, and the reply is submitted to the user.

Claims

1. A method for synthesizing a self-learning system for extraction of knowledge in a given natural language from textual documents for use in search systems, comprising the following steps:

providing a self-learning mechanism in a form of a stochastically indexed artificial intelligence system, which system is based on application of unique combinations of binary signals of stochastic information indices;

automatically instructing the system on grammatical and semantic analysis rules by using equivalent transformations of stochastically indexed text fragments and a logical conclusion, and by forming a linked semantic structures from said fragments and stochastic indexing them for representation in a form of production rules;

carrying out a morphological analysis and a stochastic indexing of linguistic documents in an electronic form in said language, with simultaneous automatic instructing the system on morphological analysis rules;

carrying out a morphological and a syntactical analysis, and a stochastic indexing of textual documents in the electronic form, pertaining to a given theme, in said language, with simultaneous automatic instructing the system on syntactical analysis rules;

carrying out a semantic analysis of the stochastically indexed textual documents in the electronic form, pertaining to the given theme, with simultaneous automatic instructing the system on semantic analysis rules;

forming a user'"'"'s request in the given natural language and transforming it in the electronic form after stochastically indexing thereof as an interrogative sentence;

transforming the user'"'"'s request in a stochastically indexed form into a set of new requests equivalent to said user'"'"'s request;

carrying out a preliminary selection, based on the user'"'"'s request, stochastically indexed fragments of textual documents in the electronic form, comprising all word combinations of said new requests;

generating a stochastically indexed semantic structure from said stochastically indexed fragments of textual documents;

basing on said structure, generating a brief reply from the system by the logical conclusion providing a link between stochastically indexed fragments of textual documents, and equivalent transformation of texts;

checking a relevancy of said brief reply to the user'"'"'s request by generating an interrogative sentence from said brief reply, and comparing generated interrogative sentence with the user'"'"'s request;

wherein when the generated interrogative sentence is identical to the user'"'"'s request, confirming the relevancy of said brief reply to the user'"'"'s request, and presenting said brief reply to the user in the given natural language.

3. The method as claimed in claim 1, further comprising requesting, in the case of a failure to generate the interrogative sentence identical to the user'"'"'s request, from the search system new textual documents to search for a reply to be relevant to the user'"'"'s request.

4. The method as claimed in claim 1, further comprising, generating, by a user'"'"'s request, a complete reply comprising a more detailed information or a particular knowledge by means of the logical conclusion to form the stochastically indexed semantic structure, and necessary equivalent transformations of said textual document fragments to obtain a new stochastically indexed text providing more detailed content of said brief reply.

5. The method as claimed in claim 1, wherein the step of automatic instructing the system on morphological analysis rules includes selecting, in a stochastically indexed text, a predetermined set of word forms of each of the words, providing stochastic indices of a word stem and a predetermined set of its endings, prefixes, suffixes and prepositions randomly accessing according to said indices to the stochastically indexed linguistic texts, selecting therefrom fragments associating said set of endings, prefixes, suffixes and prepositions with a speech part corresponding to a word, as well as with a complete set of endings, prefixes, suffixes and prepositions resulting from a word declination or conjugation, transforming said fragments into the form of production rules by stochastic indexing, wherein correctness of each of the rules being provided by autonomous derivation on the basis of several fragments from corresponding linguistic texts, and obtaining a table of indices of production rules for the knowledge base of morphological analysis.

6. The method as claimed in claim 5, wherein the step of stochastic indexing of linguistic texts, after determining the speech part of each word using rules of knowledge base of morphological analysis, includes filling the stochastically indexed database of dictionaries with stochastic indices of each word stem and those of the complete set of its endings, prefixes, suffixes and prepositions.

7. The method as claimed in claim 6, wherein the step of building tables of text indices includes stochastic transforming of information and generating unique binary combinations of indices of word stems, their endings, prefixes, suffixes, prepositions, sentences, paragraphs and text titles, which indices are placed in the tables of indices of the base of stochastically indexed texts, and providing linking between said indices, which linking being specified in an original text and ensuring text recovery using the table of indices.

8. The method as claimed in claim 1, wherein the step of automatically instructing the system on rules of syntactical analysis includes searching, in the stochastically indexed linguistic texts, for fragments describing a procedure of syntactical analysis of sentences;

taking logical conclusion to obtain the stochastically indexed semantic structure defining the link between syntactic elements and structures and words'"'"' predetermined speech parts;

deriving production rules specifying the syntactical analysis of sentences in respect of morphological word characteristics, wherein correctness of each of the rules being provided by autonomous derivation based on several fragments from corresponding linguistic texts, storing the resulted rules in the knowledge base of syntactical analysis, being stochastically indexed and represented in the form of the table of indices.

9. The method as claimed in claims 1, wherein the step of automatic instructing the system on the rules of semantic analysis further includes forming a request to tables of indexes of linguistic texts with reference to stochastic indices of word stems and speech parts, sentence members not exactly defined, and obtaining a reply as a text fragment describing semantic characteristics to be possessed by the words to conform with a particular sentence member;

and, according to said reply, referring, using a stochastic index of a given word stem and required semantic characteristics, to the tables of indexes of general-use or special dictionaries and encyclopaedias; and

, by logical conclusion, making an attempt to specify the stochastically indexed semantic structure linking the given word and required semantic characteristics; and

, if the attempt is successful, deciding that said sentence member is determined exactly;

transforming the text fragment relevant to the request into the production rule, wherein correctness of each of the rules being provided by autonomous derivation based on several fragments from corresponding linguistic texts, storing said rule in the knowledge base of semantic analysis, being stochastically indexed and represented in the form of the table of indices to be used in the semantic analysis of words as sentence members, and links between word combinations.

10. The method as claimed in claim 9, further comprising, after the index table of each text has been generated and said text has been morphologically, syntactically and semantically analyzed, generating stochastic indices of speech part names, sentence members and questions to them corresponding to each word within each of the sentences and entering said indices into the tables of indices of said text to provide automatically determining, in the search for text fragments, what speech part and sentence member each of the words belongs to, and to state questions to said word.

11. The method as claimed in claim 10, further comprising, after all tables of indices of texts have been generated, generating a table of indices for a given theme, wherein rows are designated by non-repeating stochastic indices of word stems, and each column corresponds to a stochastic index of particular text;

and entering into said table stochastic indices of text paragraphs containing a word with a particular stem index, which table of indices for the given theme being used for a preliminary search for fragments comprising a predetermined set of word combinations of the user'"'"'s request.

12. The method as claimed in claim 11, wherein the step of equivalent transforming of the user'"'"'s request includes using synonyms, words having approximately the same meaning, and replacement of speech parts and sentence members with preserving the meaning of the user'"'"'s request, on the basis of stochastically indexed rules of the morphological, syntactical and semantic analysis to provide equivalent structures of word combinations of the interrogative sentence of the user'"'"'s request and to maintain the semantic relationship therebetween.

13. The method as claimed in claim 12, wherein the step of generating the semantically linked text fragments comprising all word combinations of the user'"'"'s request includes referencing, according to stochastic indices of said word stems, to the table of text indices in respect of the given theme, selecting stochastic indices of paragraphs and corresponding texts comprising all word combinations of the user'"'"'s request, referencing, according to said indices, to the table of indices of each of the selected texts;

making the logical conclusion based on the tables of indices and the equivalent transformations of texts to produce a stochastically indexed semantic structure linking indices of the word groups of the reply corresponding to the interrogative word combination of the user request, and all word combinations of the user'"'"'s request that define the semantics of the user'"'"'s request and comprised by the pre-selected paragraphs.

14. The method as claimed in claim 13, further comprising using the stochastically indexed semantic structure, successfully produced by the logical conclusion and correspondent to the user'"'"'s request, as a basis to generate, using the obtained set of text fragments, an interrogative sentence identical to the user'"'"'s request;

generating said interrogative sentence by the equivalent transformation of stochastic indices of the word stems and word endings, suffixes, prefixes and prepositions based on rules from said knowledge bases to provide required semantic characteristics of each word combination of textual fragments of the user'"'"'s request, and using the logical conclusion based on transitive relationships between word combinations to combine them into the interrogative sentence that is identical to the user'"'"'s request and comprises the word group of the replay, corresponding to the interrogative word combination of the user'"'"'s request.

15. The method as claimed in claim 14, wherein the correctness of the brief reply being ensured by generation of several identical stochastically indexed semantic structures of said reply on the basis of various pre-selected stochastically indexed fragments of textual documents.

16. The method as claimed in claim 15, further comprising, during the search process and the generation of the reply using tables of indices of textual documents, self-learning of the system by generation indexed textual elements linking the request and the relevant brief reply to produce a knowledge base comprising elements of the type “

request-reply”

, which upon stochastic indexing, is presented in the form of tables of indices and is used for grammatical and semantic analysis of sentences of the text and for generation of replies to repeated requests contained in said indexed knowledge base.

17. The method as claimed in claim 16, wherein the step of generating the complete reply containing the knowledge relevant to the user'"'"'s request on the basis of the brief reply and with the aid of a logical conclusion according to the tables of indices used when obtaining a text fragment, comprising generating a stochastically indexed semantic structure linking a word group of the replay to the stochastic indices of word stems of the sentences, and this linking maintains the transitive relationship providing complete disclosure of the brief reply within the text fragment to obtain a linked text of the complete reply using equivalent transformations of sentences on the basis of said stochastically indexed semantic structure.

18. The method as claimed in claim 17, wherein the equivalent transformation of the stochastically indexed fragments comprises representing each sentence as a set of stochastically indexed word combinations, transforming said combinations using rules stored in the knowledge bases of morphological, syntactical and semantic analyses by means of equivalent transformation of stochastic indices of common root word stems, word endings, prefixes, suffixes and prepositions to produce new speech parts or sentence members, with provision of the constancy of the links between word combinations in the stochastically indexed semantic structure of each sentence, and the concordance between sentences when new text fragments are generated.

19. The method as claimed in claim 18, further comprising, when a new word emerges in the indexed text in the process of stochastic indexing of textual documents, which word is not contained in the dictionary of stochastically indexed words or in the linguistic texts, retrieving a common root word with respect to the new word in the dictionary and a rule for the equivalent transformation of said common root word into the new word in the knowledge base of morphological analysis;

determining, by an equivalent transformation type, the speech part which the new word belongs to and all its word forms produced by declination or conjugation,

and if no common root words found in the dictionary, selecting from the text a particular set of word forms of the new word, and determining based on endings, suffixes and prefixes of said word forms, using the stochastically indexed dictionary or products rules of the morphological analysis, the speech part which said new word belongs to, and the complete set of its word forms produced by declination or conjugation.

20. The method as claimed in claim 19, further comprising simultaneous extracting of knowledge from the textual documents in given foreign languages, said simultaneous extracting includes

automatic instructing the system in the rules of the morphological, syntactical and semantic analyses with respect to the given base language;

building a database of stochastically indexed dictionary and knowledge bases of morphological, syntactical and semantic analysis using stochastically indexed linguistic texts in a given base language;

automatic generating, using said bases, requests for automatic instruction of the system in any of given foreign languages,

preliminary selecting, according to said requests, linguistic texts fragments in the base language, which fragments contain the knowledge necessary for learning said foreign language,

performing equivalent transformation of said texts;

generating stochastically indexed semantic structures and making logical conclusions on said structures to generate replies relevant to the automatically generated requests,

using said replies for generating knowledge base of morphological, syntactical and semantic analyses for any of the given foreign languages, ensuring extraction of knowledge from textual documents in a given foreign language.

2. A method for synthesizing a self-learning system for extraction of knowledge in any given natural language from textual documents for use in search systems, comprising the following steps:

providing a self-learning mechanism in a form of a stochastically indexed artificial intelligence system, which system is based on application of unique combinations of binary signals of stochastic information indices for stochastic indexing and search for linguistic texts fragments in a given base language, comprising description of grammatical and semantic analysis procedures, and automatically instructing the system on grammatical and semantic analysis rules by using equivalent transformations of stochastically indexed linguistic text fragments and a logical conclusion, and by forming linked semantic structures from said fragments and stochastic indexing said structures for representation in a form of production rules;

carrying out a morphological analysis and a stochastic indexing of linguistic documents in an electronic form in the given base language, while simultaneous automatic instructing the system on morphological analysis rules, building a database of stochastically indexed dictionaries and tables of linguistic text indices for each given foreign language, and a knowledge base of morphological analysis, containing production rules for the base language and each given foreign language;

carrying out a morphological and a syntactical analysis, and a stochastic indexing of textual documents in the electronic form, on a given theme, in each given foreign language, from the search system, representing said documents as tables of indices of textual documents and storing said documents in bases of stochastically indexed texts, while simultaneous automatically instructing the system on syntactical analysis rules using the stochastically indexed linguistic texts in the base language, and building a knowledge base of syntactical analysis for the base language and each given foreign language;

carrying out a semantic analysis of said stochastically indexed textual documents in the electronic form, on the given theme, with simultaneous automatically instructing the system on semantic analyses rules, and building a knowledge base of semantic analysis for the base language and each given foreign language;

forming a user'"'"'s request in a natural foreign language and transforming it in the electronic form after the stochastic indexing thereof as an interrogative sentence including an interrogative word combination and word combinations determining semantics of the user'"'"'s request;

transforming the user'"'"'s request in a stochastically indexed form into a set of new requests equivalent to said user'"'"'s request;

carrying out a preliminary selection, based on the user'"'"'s request, stochastically indexed fragments of textual documents in the electronic form, comprising all word combinations of said new requests;

generating a stochastically indexed semantic structure from said stochastically indexed fragments of textual documents;

basing on said structure, generating a brief reply from the system by the logical conclusion providing a link between stochastically indexed fragments of textual documents, and equivalent transformation of the text, which reply contains stochastically indexed word combinations defining the user request semantics, and a reply word group, corresponding to the interrogative word combination of the user request;

checking a relevancy of said brief reply to the user'"'"'s request by replacing the reply word group by the corresponding stochastically indexed interrogative word combination, and comparing a generated interrogative sentence with the user'"'"'s request;

wherein when the generated interrogative sentence is identical to the user'"'"'s request, confirming the relevancy of said brief reply to the user'"'"'s request, and presenting said brief reply to the user in the given foreign language.