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Exploiting Large Scale Web Semantics Prof Enrico Motta,  PhD Knowledge Media Institute The Open University Milton Keynes, UK
The Semantic Web <rdf:RDF> <Feature rdf:about=&quot;http://sws.geonames.org/2638049/&quot;> <name>Shenley Church End</name> <alternateName>Shenley</alternateName> <inCountry rdf:resource=&quot;http://www.geonames.org/countries/#GB&quot;/> </rdf:RDF>
The SW as a large scale source of knowledge
Architecture of SW Apps
The Knowledge Acquisition Bottleneck Intelligent Behaviour KA  Bottleneck Large Body of Knowledge Knowledge
SW as Enabler of Intelligent Behaviour Intelligent Behaviour
Example: Using the SW as background knowledge to support the alignment of NALT and AGROVOC
External Source = SW ,[object Object],[object Object],[object Object],A B rel Semantic Web Does not rely on any pre-selected knowledge sources. M. Sabou, M. d’Aquin, E. Motta, “ Using the Semantic Web as Background Knowledge in Ontology Mapping &quot;, Ontology Mapping Workshop, ISWC’06.  Best Paper Award
Strategy 1 - Definition Find ontologies that contain equivalent classes for A and B and use their relationship in the ontologies to derive the mapping. A B rel Semantic Web A 1 ’ B 1 ’ A 2 ’ B 2 ’ A n ’ B n ’ O 1 O 2 O n For each ontology use these rules: … These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’:
Strategy 1- Examples Beef Food Semantic Web Beef RedMeat Tap Food MeatOrPoultry SR-16 FAO_Agrovoc ka2.rdf Researcher AcademicStaff Semantic Web Researcher AcademicStaff ISWC SWRC
Strategy 2 - Definition Principle:  If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping. A B rel Semantic Web A’ B C C’ B’ rel rel Details:   (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if  find relation between C and B. (b) if  find relation between C and B. Details:   (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if  find relation between C and B. (b) if  find relation between C and B.
Strategy 2 - Examples Vs. (midlevel-onto) (Tap) Ex1: Vs. Ex2: (r1) (pizza-to-go) (SUMO) (Same results for Duck, Goose, Turkey) (r1) Vs. Ex3: (pizza-to-go) (wine.owl) (r3)
Large Scale Evaluation Evaluation: 1600 mappings, two teams,  70% Precision (derived from 180 different ontologies) Matching AGROVOC (16k terms) and NALT(41k terms) M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “ Exploiting the Semantic Web for  Ontology Matching  “. In Press
 
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 

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Exploiting Large Scale Web Semantics

  • 1. Exploiting Large Scale Web Semantics Prof Enrico Motta, PhD Knowledge Media Institute The Open University Milton Keynes, UK
  • 2. The Semantic Web <rdf:RDF> <Feature rdf:about=&quot;http://sws.geonames.org/2638049/&quot;> <name>Shenley Church End</name> <alternateName>Shenley</alternateName> <inCountry rdf:resource=&quot;http://www.geonames.org/countries/#GB&quot;/> </rdf:RDF>
  • 3. The SW as a large scale source of knowledge
  • 5. The Knowledge Acquisition Bottleneck Intelligent Behaviour KA Bottleneck Large Body of Knowledge Knowledge
  • 6. SW as Enabler of Intelligent Behaviour Intelligent Behaviour
  • 7. Example: Using the SW as background knowledge to support the alignment of NALT and AGROVOC
  • 8.
  • 9. Strategy 1 - Definition Find ontologies that contain equivalent classes for A and B and use their relationship in the ontologies to derive the mapping. A B rel Semantic Web A 1 ’ B 1 ’ A 2 ’ B 2 ’ A n ’ B n ’ O 1 O 2 O n For each ontology use these rules: … These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’:
  • 10. Strategy 1- Examples Beef Food Semantic Web Beef RedMeat Tap Food MeatOrPoultry SR-16 FAO_Agrovoc ka2.rdf Researcher AcademicStaff Semantic Web Researcher AcademicStaff ISWC SWRC
  • 11. Strategy 2 - Definition Principle: If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping. A B rel Semantic Web A’ B C C’ B’ rel rel Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if find relation between C and B. (b) if find relation between C and B. Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if find relation between C and B. (b) if find relation between C and B.
  • 12. Strategy 2 - Examples Vs. (midlevel-onto) (Tap) Ex1: Vs. Ex2: (r1) (pizza-to-go) (SUMO) (Same results for Duck, Goose, Turkey) (r1) Vs. Ex3: (pizza-to-go) (wine.owl) (r3)
  • 13. Large Scale Evaluation Evaluation: 1600 mappings, two teams, 70% Precision (derived from 180 different ontologies) Matching AGROVOC (16k terms) and NALT(41k terms) M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “ Exploiting the Semantic Web for Ontology Matching “. In Press
  • 14.  
  • 15.
  • 16.