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Leveraging Semantic Web for Intelligent Problem Solving

Explore the use of Semantic Web as a vast knowledge source for intelligent problem-solving. Discover architecture of Semantic Web apps and overcome the Knowledge Acquisition Bottleneck. Learn how online ontologies can support intelligent behavior by deriving mappings dynamically. Case study using SW as background knowledge for ontology mappings. Strategy for ontology mapping and evaluation in real-world scenarios. Benefit from the SW in diverse applications like Semantic Web browsing and integration with folksonomies. Find solutions to large-scale knowledge challenges with Semantic Web technologies.

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Leveraging Semantic Web for Intelligent Problem Solving

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  1. Exploiting Large Scale Web Semantics Prof Enrico Motta, PhD Knowledge Media InstituteThe Open UniversityMilton Keynes, UK

  2. The Semantic Web <rdf:RDF> <Feature rdf:about="http://sws.geonames.org/2638049/"> <name>Shenley Church End</name> <alternateName>Shenley</alternateName> <inCountry rdf:resource="http://www.geonames.org/countries/#GB"/> </rdf:RDF>

  3. The SW as a large scale source of knowledge

  4. Architecture of SW Apps

  5. Knowledge Large Bodyof Knowledge The Knowledge Acquisition Bottleneck KA Bottleneck Intelligent Behaviour

  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. External Source = SW • Proposal: • rely on online ontologies (Semantic Web) to derive mappings • ontologies are dynamically discovered and combined Semantic Web Does not rely on any pre-selected knowledge sources. rel A B M. Sabou, M. d’Aquin, E. Motta, “Using the Semantic Web as Background Knowledge inOntology Mapping", Ontology Mapping Workshop, ISWC’06. Best Paper Award

  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. For each ontology use these rules: Semantic Web B1’ B2’ Bn’ … An’ A1’ A2’ O2 On O1 These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’: rel A B

  10. Food MeatOrPoultry AcademicStaff Semantic Web Semantic Web RedMeat Researcher Beef ka2.rdf Tap AcademicStaff Researcher Beef Food SWRC SR-16 FAO_Agrovoc ISWC Strategy 1- Examples

  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. 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. rel B’ C’ Semantic Web rel C B A’ rel A B

  12. Strategy 2 - Examples Ex1: Vs. (r1) (midlevel-onto) (Tap) (Same results for Duck, Goose, Turkey) Ex2: Vs. (pizza-to-go) (r1) (SUMO) Ex3: Vs. (pizza-to-go) (r3) (wine.owl)

  13. Large Scale Evaluation Matching AGROVOC (16k terms) and NALT(41k terms) (derived from 180 different ontologies) Evaluation: 1600 mappings, two teams, 70% Precision M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “Exploiting the Semantic Web for Ontology Matching “. In Press

  14. Conclusions • Our results with the NALT/AGROVOC matching problem show that the SW can be used effectively as a source of background knowledge for intelligent problem solving • The SW provides an unprecedented opportunity to address the KA bottleneck and remove one of the fundamental barriers to the large-scale diffusion of knowledge-based intelligent systems • This approach is being used in a number of other scenarios, including: • Semantic Web Browsing • Question Answering • Integration of Folksonomies with the SW

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