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Analysis of texts to logical contradictions

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  • 1. Analysis of texts to logical contradictions Ontologs LLC, Moscow, 2012
  • 2. Motivation50 years ago: 60th The exponential growth of information flows in an era of scientific and technological revolution People no longer cope with the processing Solution: machine processes dataChallenges of our time Exponential growth of knowledge available to man It’s physically impossible to just read the number of publications on the appropriate category Solution: machine must process knowledge
  • 3. Systematization of knowledgeKnowledge extraction Sources - both structured and unstructured data Unstructured data - texts in natural language Intelligent processing of texts - an extraction of knowledge from them The purpose of knowledge extraction - its automatic systematizationMethods of knowledge systematization Text classification Text summarization Copyright analysis of texts Sentiment analysis of textsProblems Symbol and lexical language levels Dependence on the language of the text Insufficient accuracy The lack of processing context
  • 4. Levels of language representationSymbolic level Lexical level Symbol classes: alphabetic characters,  Language dictionaries. spaces, punctuation, etc.  Knowledge of inflection. Use: The copyright analysis, determination of  Use: The copyright analysis, text search, etc. authorship, etc. “Brown fox jumps…”“Symbol classes: alphabetic characters, brown (adj.) fox (noun) jump (verb)spaces, punctuation, etc.”“symbolclasses” “alphabeticcharacters”“spaces” “punctuation” “etc”Syntax level Semantic level Grammars.  Elements of ontology. Congruence.  Congruence of ontology’s objects. Use: a selection of "correct" phrases,  Use: a selection of correct semantic terminological analysis structures“Brown fox jumps…” “Brown fox jumps…”brown (quality) fox (subject) jump brown (color) fox (kind of mammal) jump(predicate) (action, move)
  • 5. Analysis on semantic levelSemantic structure of texts Ontology – the formal knowledge description for human and machines The system automatically extracts knowledge from a text document and creates an ontology Ontology of text - language-independent machine representation of the semantic content of the textTools for ontology manipulation Languages: RDF (Resource Description Framework) and OWL (Web Ontology Language) Libraries: Jena - open implementation of ​RDF and OWL with the possibility of inference Ontology of a text - logical theory, written in the language OWL (RDF)
  • 6. Logical analysis of textsSemantics and language constructs Domain-based skeleton ontology - a conceptual framework Conceptual scheme - a set of classes and relationships between classes Elements of the conceptual schema associated with language expressionsKnowledge extratcion Input: a text and an ontology with language expressions The text is analyzed and objects of an ontology are allocated - instances of classes and relations of the conceptual schemeLogical analysis A set of logical rules that define the conditions for the correctness of ontology elements Logic formulas to express the issues of correctness Verification procedure for the contradictions in an ontology
  • 7. Example – ontology of eventsOntology of events Events - incidents, sports, meeting government officials, etc. List of participants of the event contains information about persons Person class objects - instance “Vladimir Putin”Language expressions for object class Person“Vladimir Putin” First Name: string Vladimir Putin Second Name: string V. Putin Birth Date: date Position: string Vladimir Vladimirovich Putin Location List: list of (place, date) President Putin Spouse: string Pesident of Russian Federation Children: list of sting [time context: 7 of May, 2000 -7 of … May, 2008, 7 of May 2012 - present]
  • 8. Objects and facts extractionWhite Papers President of Russia Vladimir Putin and German Chancellor held talks in Berlin 1 of June, 2012. Following the talks, Mr Putin Ms Merkel made press statements and answered journalists’ questions. Putin : PersonThe ontology of the text First Name: Vladimir Second Name: Putin Event class object – {“Working visit to Birth Date: 7 of October, 1952 Germany”, 01.06.2012, Participants: Position: President (Vladimir Putin, Angela Merkel), …} Location List: … Person class object – {“Putin”, Spouse: … “Vladimir”, 07.10.1952,…} Children: … …
  • 9. Identification of contradictionsIdentification of contradictions on objects extraction phase Suppose an article from the example indicated date: 1 of June 2011 Language expression ”President of Russian Vladimir Putin” is incorrect because temporal context (1 of June 2011) contradicts the content of the corresponding object class Person - Russian President [temporary context: 7 of May 2000 - 7 of May 2008, 7 of May 2012 - present]Identification of contradictions on logical analysis phase Suppose you have an article with the text “1 of June 2012 Vladimir Putin isited to inspect the bridge to the island in the Russian city of Vladivostok” Event class object is extracted. The object contradicts the fact in Event ontology: “Working visit to Germany” because time of events is the same, but places are different
  • 10. Questions and contactsContacts: Ontologs LLC, 119634, Russia, Moscow, Borovskoie shosse, 44/3 www.онтологика.рф, www.ontologs.ru, www.ontologs.com Email: info.ontologs.ru