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Gathering Lexical Linked Data and Knowledge Patterns from FrameNet

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  • 1. (2) Dipartimento di Scienze dell’Informazione, Università di Bologna(1) Semantic Technology Laboratory ISTC-CNR Gathering Lexical Linked Data and Knowledge Patterns from FrameNet Andrea Giovanni Nuzzolese (1,2) andrea.nuzzolese@istc.cnr.it Aldo Gangemi (1) aldo.gangemi@cnr.it Valentina Presutti (1) valentina.presutti@cnr.it K-CAP 2011 Banff, AL, Canada 27 June 2011
  • 2. Outline • Motivations • Semantic issues • Transformation method • Ongoing work • Conclusions
  • 3. Premise • Work after request from Berkeley FrameNet group for a Semantic Web version of FrameNet 1.5 • Previous work had various limitations, mainly data incompleteness and implicit semantics – E.g. Scheczyk et al., Narayanan et al. • Decided to go for a dual transformation – RDF for a complete porting to Linked Open Data, similarly to W3C WordNet RDF porting – (customizable) OWL for a focused porting to knowledge patterns reusable for ontology design or for creating views over linked data
  • 4. Motivations • The web of data is exploding and NLP techniques accompany this explosion • Hybridizing natural language processing and semantic web techniques shows to be a promising approach • Part of the exploitation of LOD data, is carried out by means of lexical resources that are represented directly as linked data • Bring lexical resource on linked data (favor hybridization) – benefit from linking all lexical resources and have an homogenous more powerful one • Link lexical knowledge to domain knowledge – linked data ground to lexical knowledge and textual documents
  • 5. DBpedia Lexvo lingvoj RDF WordNet 3.0 RDF FrameNet 1.5 RDF VerbNet 3.1 RDF Italian MultiWordNet WordNet Domains WordNet Supersenses WordNet Formal Glosses VerbOcean
  • 6. Several semantic issues in reusable linguistic data • Semantics induced by the data structure, e.g. RDB, XML, etc. • Semantics from the linguistic model adopted • Semantics of the corpus (e.g. sentences) • Semantics needed for querying • Semantics needed for reasoning
  • 7. FrameNet • A lexical knowledge base – cognitive soundness – grounded in a large corpus • Consists of a set of frames, which have – frame elements – lexical units, which pair words (lexemes) to frames – relations to corpus elements • Each frame can be interpreted as a class of situations
  • 8. An example of frame
  • 9. FrameNet as LOD
  • 10. FrameNet as LOD
  • 11. FrameNet as ontologies
  • 12. Structural Schema Linguistic Schema Linguistic Data Corpus Data Referential Data Linguistic transformation architecture
  • 13. Transformation approach • We pulled out the semantics of FrameNet and its data by using Semion, • Semion is a tool grounded on a method with two main steps – a syntactic and completely automatic transformation of the data source to RDF datasets according to an OWL ontology that represents the data source structure – a semantic rule-based refactoring that allows to express the RDF triples according to specific domain ontologies e.g. SKOS, DOLCE, FOAF, LMM, or anything indicated by the user.
  • 14. Reengineering Syntactic transformation to RDF triples <frame name="Abounding_with" ... ID="262"> ... <frameRelation type="Inherits from"> <relatedFrame> Locative_relation </relatedFrame> </frameRelation> ... </frame>
  • 15. Refactoring • aims to add semantics to data • is performed by means of set of rules – i.e. SPARQL CONSTRUCT
  • 16. ABox Refactoring The ABox refactoring is the process of gathering RDF data (Abox) Rule-based Customizable or based on recipes
  • 17. ABox Refactoring (data)
  • 18. TBox Refactoring • The TBox refactoring is the process of gathering a new ontology schema (a TBox) from data (ABox)
  • 19. TBox Refactoring
  • 20. Ongoing work • Linking – WordNet, WN Domains, MultiWordNet, VerbNet, FrameNet, VerbOcean (P. Pantel) • Basic linking uses SKOS – exactMatch, closeMatch – links partly present in Colorado bank, partly in WordNet mappings, part are newly created • More reasoning requires some expressivity – semiotics.owl knowledge pattern, D&S – property chains
  • 21. Conclusion • issues related to the conversion of lexical resources – more specifically to semantic issued of FrameNet conversion • a method to solve those issues (supported by a tool) • a conversion of FrameNet to RDF published as a dataset in the LOD • a method to convert FrameNet data into knowledge patterns
  • 22. Thank you Andrea Nuzzolese - STLab, ISTC-CNR & Dipartimento di Scienze dell’Informazione University of Bologna Italy
  • 23. 23 Semantic issues: objects • Semantic frames/verb classes as twofold creatures – intensional polymorphic relations (aka descriptions) + situation types – Desiring(?experiencer, ?theme, ?time, ?loc, ?...) • Frame elements/VN arguments as complex creatures – (semantic) roles + concepts • Semantic types are a mixture – concepts, grammatical types, etc. • Lexical units/VN class members as hybrid creatures – lexically-oriented semantic frames – bridges between semantic frames and word senses – FN lex units belong to diverse parts of speech • Annotated sentences contain syntactical realizations of semantic frames (“exemplifications”) – syntactic frames in VN, valences in FN 23
  • 24. 24 Semantic issues: relations • Inheritance in FN and VN is classic, can hold for situation types safely – needs to be treated jointly with semantic role representation – subFe also classic • Subframes in FN are conceptual compositions (“parts of descriptions” in D&S), intensional in nature – similarly for “excludes” and “requires” holding for FE • Frame “usage” in FN is partial inheritance, hard to digest for situation types • Selectional restrictions in VN maybe too tough for situation types • Selectional preferences absent in resources, but probability would be an added value • Core vs. peripheral vs. unexpressed are interesting but tough: “characteristic”, hidden optionality, etc. 24
  • 25. Why a KP? – a multidimensional context model able to capture descriptive, informational, situational, social, and formal characters of knowledge.