Analysis of texts to logical contradictions


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Analysis of texts to logical contradictions

  1. 1. Analysis of texts to logical contradictions Ontologs LLC, Moscow, 2012
  2. 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. 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. 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. 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. 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. 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. 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. 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. 10. Questions and contactsContacts: Ontologs LLC, 119634, Russia, Moscow, Borovskoie shosse, 44/3 www.онтологика.рф,, Email: