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

The SW as a large scale source of knowledge

Architecture of SW Apps

The Knowledge Acquisition Bottleneck Large Body of Knowledge KA Bottleneck Intelligent Behaviour

SW as Enabler of Intelligent Behaviour

Example: Using the SW as background knowledge to support the alignment of NALT and AGROVOC

rely on online ontologies (Semantic Web) to derive mappings 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 in Ontology Mapping", 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. 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

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

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

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

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