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Understanding Ontological Semantics: A Language-independent NLP Approach

Explore the features of OntoSem ontology, its applications in NLP, and the process of mapping OntoSem2OWL for knowledge representation. Learn about text meaning extraction and the rich OntoSem world model, supported by extensive resources for various languages.

michaelchristensen
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Understanding Ontological Semantics: A Language-independent NLP Approach

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  1. OntoSem2OWL

  2. Plan of the talk • OntoSem Overview • Features of OntoSem Ontology • Mapping OntoSem2OWL • Motivation • Possible Application Scenarios

  3. About OntoSem • Ontological Semantics (OntoSem) is a theory of meaning in natural language. [Sergei Nirenburg and Victor Raskin, Ontological Semantics, Formal Ontology and Ambiguity] • Aims to extract and represent the meaning in text in a language independent form. • It supports practical, large scale NLP applications such as MT, QA, Information Extraction, NLG. • Supported by a constructed world model encoded in a rich Ontology. [Sergei Nirenburg and Victor Raskin, Ontological Semantics, MIT Press, Forthcoming]

  4. Basic Components • Preprocessor • Converts the natural language text to Text Meaning Representation (TMR) • Static Knowledge Source • Ontology (language independent) • Lexicon (for each language) • Ontomasticon (to store proper names) • Fact repository (stores learnt instances of concepts and TMRs)

  5. Text MeaningRepresentation (TMR) Input Text SyntacticAnalyzer SemanticAnalyzer Preprocessor Grammar: Ecology MorphologySyntax Lexicon and Onomasticon Ontology and Fact Repository Static Knowledge Resources Architecture of the Analyzer

  6. Static Knowledge Sources • Ontology 6000 concepts • English Lexicon 45000 entries • Spanish Lexicon 40000 entries • Chinese Lexicon 3000 entries • Fact repository 20000 facts [Sergei Nirenburg, Ontological Semantics: Overview, Presentation CLSP JHU, Spring 2003]

  7. Text Meaning Representations Heaskedthe UNto authorizethe war. REQUEST-ACTION-69   AGENT HUMAN-72 THEME ACCEPT-70   BENEFICIARY ORGANIZATION-71   SOURCE-ROOT-WORD ask TIME (< (FIND-ANCHOR-TIME)) ACCEPT-70  THEME WAR-73   THEME-OF REQUEST-ACTION-69   SOURCE-ROOT-WORD authorizeORGANIZATION-71   HAS-NAME United-Nations  BENEFICIARY-OF REQUEST-ACTION-69  SOURCE-ROOT-WORD UNHUMAN-72   HAS-NAME Colin Powell  AGENT-OF REQUEST-ACTION-69 SOURCE-ROOT-WORD he ; reference resolution has been carried outWAR-73   THEME-OF ACCEPT-70  SOURCE-ROOT-WORD war Example from [Marjorie McShane, Sergei Nirenburg, Stephen Beale, Margalit Zabludowski, The Cross Lingual Reuse and Extension of knowledge Resources in Ontological Semantics]

  8. The OntoSem Ontology Concept ::= root | object-or-event | property property ::= relation | attribute | ontology-slot Slot = PROPERTY + FACET + FILLER

  9. The OntoSem Ontology FILLER PROPERTY FACET

  10. Example frame from the Ontology Example from [P.J Beltran-Ferruz, P.A Gonzalez-Calero, P. Gervas Converting Mikrokosmos frames into Description Logics.]

  11. Types of Slots SLOTs are essentially PROPERTIES • ATTRIBUTE Maps a concept or a set of concepts to values (numerical/ literals) • RELATION Property that connects two or more concepts. • ONTOLOGY-SLOT Describe the ontology.

  12. Types of Facets VALUE • FACET is used to restrict the values that may be stored. • filler is the actual value • May beinstance, a Concept, literal, number • Example: earth ............. number-of-moons VALUE 1 .............. [Sergei Nirenburg, Ontology Tutorial, ILIT UMBC]

  13. Types of FacetsSEM • Filler may be violated in certain cases. • Most commonly used Facet. • Example: CONCEPT: EVENT AGENT SEM ANIMAL NATION ORGANIZATION PLANT

  14. Types of FacetsRELAXABLE-TO • Indicates “Typical violations” of the constraints listed in SEM Facets. • Example: CONCEPT: EVENT AGENT SEM ANIMAL NATION ORGANIZATION PLANT RELAXABLE-TO DEITY

  15. Types of FACETSDEFAULT • Refers to the most frequent or expected constraint on the property • Example PAY THEME DEFAULT MONEY

  16. TYPES OF FACETSOther FACETS... • NOT: specifies that the given filler(s) must be excluded from the set of acceptable fillers. • DEFAULT-MEASURE: specifies measuring unit for the numerical range that fills VALUE, DEFAULT or SEM. • INV: Indicates that there exists an inverse property.

  17. Fact Repository • Stores instances of real-world facts • Represents instances of ontological concepts.

  18. OntoSem2OWL Motivation • This project is investigating the feasibility of developing a system to translate ontologies and data between ontosem and OWL. • Will facillitate sharing a rich, extensive language independent ontology with other Semantic Web applications. • Additionally, if an OWL2OntoSem equevalent mapping can be made the OntoSem Ontology and Fact repository can be augmented by reusing existing ontologies on the Semantic Web.

  19. Related WorkConverting Mikrokosmos frames into Description Logic • Microkosmos Ontology: • A precursor to OntoSem • Originally used for MT [ Kavi Mahesh and Sergei Nirenburg, Meaning Representation for Knowledge Sharing in Practical Machine Translation J.E Lonergan, Lexical Knowledge Engineering: Mikrokosmos Revisited] • Propose a translation of frame based representation of Mikrokosmos to SHIQ and OWL. [P.J Beltran-Ferruz, P.A Gonzalez-Calero, P. Gervas Converting Mikrokosmos frames into Description Logics. P.J Beltran-Ferruz, P.A Gonzalez-Calero, P. Gervas Converting frames into OWL: Preparing Mikrokosmos for Linguistic Creativity]

  20. Related WorkOOP, Frame Systems and DL vocabulary [Ora lassila, Deborah McGuiness The Role of Frame-Based Representation on Semantic Web]

  21. Related WorkMapping Mikrokosmos to SHIQ • Unary Predicates Map into DL Classes • Binary Predicate Map into DL relation** ** check if its slot constraint?? • Special Case

  22. Related WorkMapping Mikrokosmos concepts to DL Classes <RECORD> <CONCEPT>CN</CONCEPT> <SLOT>IS-A</SLOT> <FACET>VALUE</FACET> <FILLER>Ci</FILLER> </RECORD> (From Spencer notation of Mikrokosmos) Class-def(primitive | defined CN subclass-of Ci,......Cn slot-constraint1 slot-constraint2 ........................ slot-constraintn Information about classes and subclasses is stored in RECORDs using IS-A Slots

  23. Related WorkMapping Mikrokosmos slot constraints to DL <RECORD> <CONCEPT>CN</CONCEPT> <SLOT>SN</SLOT> <FACET>FACET</FACET> <FILLER>C</FILLER> </RECORD> (From Spencer notation of Mikrokosmos) Class-def(primitive | defined CN subclass-of Ci,......Cn slot-constraint1 slot-constraint2 ........................ slot-constraintn Information about slot constraints is stored in RECORDs where slots are PROPERTIES

  24. Related WorkMapping Mikrokosmos FACETs to DL

  25. Related WorkMapping Mikrokosmos ONTOLOGY-SLOTs to DL

  26. Related WorkBuilding DL relations Information requred for DL relations is encoded in records with ONTOLOGY-SLOTs in their SLOT field: INVERSE slot-def SN inverses X DOMAIN, RANGE slot-def SN domain disjoint X1.....Xn slot-def SN range disjoint X1.....Xn MEASURED-IN slot-def SN range X (treated like range) <RECORD> <CONCEPT>SN</CONCEPT> <SLOT>SLOT</SLOT> <FACET>FACET</FACET> <FILLER>X</FILLER> </RECORD> (From Spencer notation of Mikrokosmos) Addional information in PROPERTYs that cannot be mapped easily is stored in CLASS-<PROPERTY-NAME>.

  27. Related WorkMikrokosmos OWL Protege plugin

  28. Application ScenariosAugmenting OntoSem FR with Semantic Web data <foaf:Person> <foaf:name>Tim Finin</foaf:name> <foaf:firstName>Tim</foaf:firstName> <foaf:surname>Finin</foaf:surname> <foaf:nick>Tim</foaf:nick> ………………………………………… <foaf:birthDate>1949-08-04</foaf:birthDate> <foaf:myersBriggs>ENTP</foaf:myersBriggs> <foaf:plan>http://www.cs.umbc.edu/~finin/schedule.html</foaf:plan> <foaf:publications>http://www.cs.umbc.edu/%7Efinin/cv/index.shtml#publications</foaf:publications> <foaf:weblog rdf:resource="http://ebiquity.umbc.edu/v2.1/blogger/" /> <foaf:aimChatID>timFinin</foaf:aimChatID> <foaf:mbox_sha1sum>49953f47b9c33484a753eaf14102af56c0148d37</foaf:mbox_sha1sum> <foaf:homepage rdf:resource="http://umbc.edu/~finin/"/> <foaf:depiction rdf:resource="http://umbc.edu/~finin/passport.gif"/> <foaf:phone rdf:resource="tel:+1-410-455-3522"/> <foaf:workplaceHomepage rdf:resource="http://umbc.edu/"/> <foaf:workInfoHomepage rdf:resource="http://umbc.edu/~finin/"/> <foaf:schoolHomepage rdf:resource="http://web.mit.edu/"/> …………………………………………………… OntoSem Fact Rep Store FOAF data as Facts in OntoSem’s Fact Repository.

  29. Application ScenariosReference Resolution • Ontological-Semantics reference resolution Not only deals with relating differnet references to the same individual in text but also mapping them to the real-world model. • Augment OntoSem with FOAF data to resolve ambiguity in reference resolution. [Beale S., M Mc. Shane, S.Nirenburg, Ontological Semantics Reference Resolution: Setting the Stage]

  30. Application ScenariosReference Resolution A Joshi is an Associate Professor in the Computer Science department at UMBC. A Joshi, UMBC => Anupam Joshi A Joshi, Random => A….. Joshi OntoSem FOAF file Anupam Joshi A Joshi is a Philosophy student at RandomUniversity. FOAF file A Joshi

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