Acquiring Complex Cross-Ontological Relationships

1. Acquiring Complex Cross-Ontological Relationships Justin Gray 28 January 2009

2. Introduction • An ontology is a partial, explicit, formal specification of a shared conceptualization • Standards for ontology representation such as the Web Ontology Language (OWL) has led to tremendous growth in ontologies exposed on the Web • Proliferation of knowledge sharing on the Web has resulted in a growing need for integration of knowledge • “As different parties generate ontologies independently, the level of heterogeneity across platforms increases” [Euzenat & Shvaiko 2007] • Ontology Mapping is a process of acquiring relationships between ontological entities

3. Gaps in Capability • Significant body of work in the area of schema matching and ontology alignment • 50+ approaches published to date • None of the approaches exploit the characteristics of OWL in ontology matching [Euzenat et al. 2006] • None of the approaches exploit upper ontologies • Nearly all approaches seek to learn equivalence relations • Only a few acquire other relationships [Palopoli et al. 2003], [Kotis et al. 2006], [Kim et al. 2005] • Relationships restricted to hypernymy, hyponymy, inclusion, subsumption • Most approaches apply alignment for data integration, transformation and query answering • Very few focus on Web service composition [Hibner and Zielinski 2007] [Corcho et al. 2003] and mediation

4. Research Objectives • Contribute new evidence for ontology alignment: use of the semantics of OWL and upper ontologies • Contribute algorithms to acquire new information in ontology alignment: relationships beyond equivalence • Demonstrate new application of ontology alignment: Web service composition and mediation Advance the state of the art in ontology alignment

5. Relationships to Acquire Hyponymy, or subclass relation “Relations in R”, generic relations contained within the ontologies to align Hypernymy, or superclass relation Meronymy, to include partOf, hasPart Disjointness, relation in which no instances are shared between classes

6. Evaluation • Ontologies chosen from web in domain of academic conferences, students, etc. • Reference alignment generated semi-automatically • Precision and recall measured against reference alignment • Ontology pairs selected to test as many pattern combinations as possible

7. Summary and Next Steps • We propose to advance the state of the art in ontology mapping • Contribute new evidence, algorithms and applications • Semantics of OWL, WordNet and OpenCyc have been applied to acquire hyponymy and generic “Relations in R” • Preliminary results are promising in a simple domain • Next steps include: • Test ontology mapping in bioinformatics domain • Identify new relations to acquire and new types of evidence • Apply machine learning to the problem to optimize application of evidence • Apply alignment for web service composition and mediation Contribute to state of the art in ontology alignment