(2) Dipartimento di Scienze dell’Informazione, Università di Bologna(1) Semantic Technology Laboratory ISTC-CNR
Gathering ...
Outline
• Motivations
• Semantic issues
• Transformation method
• Ongoing work
• Conclusions
Premise
• Work after request from Berkeley FrameNet group
for a Semantic Web version of FrameNet 1.5
• Previous work had v...
Motivations
• The web of data is exploding and NLP techniques accompany
this explosion
• Hybridizing natural language proc...
DBpedia
Lexvo
lingvoj
RDF
WordNet
3.0
RDF
FrameNet
1.5
RDF
VerbNet
3.1
RDF
Italian
MultiWordNet
WordNet
Domains
WordNet
Su...
Several semantic issues
in reusable linguistic data
• Semantics induced by the data structure, e.g.
RDB, XML, etc.
• Seman...
FrameNet
• A lexical knowledge base
– cognitive soundness
– grounded in a large corpus
• Consists of a set of frames, whic...
An example of frame
FrameNet as LOD
FrameNet as LOD
FrameNet as ontologies
Structural
Schema
Linguistic
Schema
Linguistic
Data
Corpus
Data
Referential
Data
Linguistic
transformation
architecture
Transformation approach
• We pulled out the semantics of FrameNet and its
data by using Semion,
• Semion is a tool grounde...
Reengineering
Syntactic transformation to RDF triples
<frame name="Abounding_with" ... ID="262">
...
<frameRelation type="...
Refactoring
• aims to add semantics to data
• is performed by means of set of rules
– i.e. SPARQL CONSTRUCT
ABox Refactoring
The ABox refactoring is the
process of gathering RDF
data (Abox)
Rule-based
Customizable or based on
reci...
ABox Refactoring (data)
TBox Refactoring
• The TBox refactoring is the process of
gathering a new ontology schema (a
TBox) from data (ABox)
TBox Refactoring
Ongoing work
• Linking
– WordNet, WN Domains, MultiWordNet, VerbNet,
FrameNet, VerbOcean (P. Pantel)
• Basic linking uses ...
Conclusion
• issues related to the conversion of lexical
resources
– more specifically to semantic issued of FrameNet
conv...
Thank you
Andrea Nuzzolese
-
STLab, ISTC-CNR
&
Dipartimento di Scienze dell’Informazione
University of Bologna
Italy
23
Semantic issues: objects
• Semantic frames/verb classes as twofold creatures
– intensional polymorphic relations (aka d...
24
Semantic issues: relations
• Inheritance in FN and VN is classic, can hold for situation types safely
– needs to be tre...
Why a KP?
– a multidimensional
context model able to
capture descriptive,
informational, situational,
social, and formal
c...
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Gathering Lexical Linked Data and Knowledge Patterns from FrameNet

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Gathering Lexical Linked Data and Knowledge Patterns from FrameNet

  1. 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. 2. Outline • Motivations • Semantic issues • Transformation method • Ongoing work • Conclusions
  3. 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. 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. 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. 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. 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. 8. An example of frame
  9. 9. FrameNet as LOD
  10. 10. FrameNet as LOD
  11. 11. FrameNet as ontologies
  12. 12. Structural Schema Linguistic Schema Linguistic Data Corpus Data Referential Data Linguistic transformation architecture
  13. 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. 14. Reengineering Syntactic transformation to RDF triples <frame name="Abounding_with" ... ID="262"> ... <frameRelation type="Inherits from"> <relatedFrame> Locative_relation </relatedFrame> </frameRelation> ... </frame>
  15. 15. Refactoring • aims to add semantics to data • is performed by means of set of rules – i.e. SPARQL CONSTRUCT
  16. 16. ABox Refactoring The ABox refactoring is the process of gathering RDF data (Abox) Rule-based Customizable or based on recipes
  17. 17. ABox Refactoring (data)
  18. 18. TBox Refactoring • The TBox refactoring is the process of gathering a new ontology schema (a TBox) from data (ABox)
  19. 19. TBox Refactoring
  20. 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. 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. 22. Thank you Andrea Nuzzolese - STLab, ISTC-CNR & Dipartimento di Scienze dell’Informazione University of Bologna Italy
  23. 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. 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. 25. Why a KP? – a multidimensional context model able to capture descriptive, informational, situational, social, and formal characters of knowledge.
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