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. Architecture of the Analyzer Preprocessor Input Text Syntactic Analyzer Text Meaning Representation (TMR) Grammar: Ecology Morphology Syntax Lexicon and Onomasticon Static Knowledge Resources Semantic Analyzer Ontology and Fact Repository

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 He asked the UN to authorize the 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 authorize ORGANIZATION-71 HAS-NAME United-Nations BENEFICIARY-OF REQUEST-ACTION-69 SOURCE-ROOT-WORD UN HUMAN-72 HAS-NAMEColin Powell AGENT-OF REQUEST-ACTION-69 SOURCE-ROOT-WORD he; reference resolution has been carried out WAR-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 PROPERTY FILLER 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 be instance, a Concept, literal, number ● Example: earth............. number-of-moons VALUE 1.............. [Sergei Nirenburg, Ontology Tutorial, ILIT UMBC]

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

14. Types of Facets RELAXABLE-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 FACETS DEFAULT ● Refers to the most frequent or expected constraint on the property ● Example PAY THEME DEFAULT MONEY

16. TYPES OF FACETS Other 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 Work Converting 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 Work OOP, Frame Systems and DL vocabulary [Ora lassila, Deborah McGuiness The Role of Frame-Based Representation on Semantic Web]

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

22. Related Work Mapping Mikrokosmos concepts to DL Classes CN IS-A VALUE C i (From Spencer notation of Mikrokosmos) Class-def(primitive | defined CN subclass-of Ci,......Cn slot-constraint 1 slot-constraint 2........................ slot-constraint n Information about classes and subclasses is stored in RECORDs using IS-A Slots

23. Related Work Mapping Mikrokosmos slot constraints to DL CN SN FACET C (From Spencer notation of Mikrokosmos) Class-def(primitive | defined CN subclass-of Ci,......Cn slot-constraint 1 slot-constraint 2........................ slot-constraint n Information about slot constraints is stored in RECORDs where slots are PROPERTIES

24. Related Work Mapping Mikrokosmos FACETs to DL

25. Related Work Mapping Mikrokosmos ONTOLOGY-SLOTs to DL

26. Related Work Building DL relations SN SLOT FACET X (From Spencer notation of Mikrokosmos) 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) Addional information in PROPERTYs that cannot be mapped easily is stored in CLASS-.

27. Related Work Mikrokosmos OWL Protege plugin

28. Application Scenarios Augmenting OntoSem FR with Semantic Web data Tim Finin Tim Finin Tim ………………………………………… 1949-08-04 ENTP http://www.cs.umbc.edu/~finin/schedule.html http://www.cs.umbc.edu/%7Efinin/cv/index.shtml#publ ications timFinin 49953f47b9c33484a753eaf14102af56c0148d37 …………………………………………………… OntoSem Fact Rep Store FOAF data as Facts in OntoSem’s Fact Repository.

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