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="
<name>Shenley Church End</name>
<alternateName>Shenley</alternateName>
<inCountry rdf:resource="
</rdf:RDF>

3. The SW as a large scale source of knowledge

4. Architecture of SW Apps

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

6. SW as Enabler of Intelligent Behaviour

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

8. 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

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

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

15. 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