With the advance of the Semantic Web, both the Web and Grid communities have embraced the concept of enriching distributed resources with machine-understandable semantic metadata. Semantic resource discovery is one of the emerging research areas that leverages resource metadata to reason about compatibility and functionality. Resource compatibility can be derived by reasoning about their types and relations. The OWL language semantics provides a formal model for description logic reasoning. However, under many usage scenarios the logical inference approach is often too restrictive. Many similar resources that are potentially useful are eliminated in the matching process, due to their logical non equivalence. In this thesis we propose two efficient numerical methods for calculating the similarity of OWL-described resources. By viewing OWL descriptions as RDF graphs, we base our first similarity measure on the graph edit distance technique developed for inexact graph matching. The similarity of two graphs is derived from the total cost of the edit operations. The second method transform semantic descriptions into distance constraints based on their relational structures. By deriving resource coordinates from the distance constraints, the distances between resources become a natural similarity measure. Numerical similarity measure provides a useful lightweight method to exploit the available semantic metadata. As increasing number of resources becomes publicly available on the Internet, the computationally intensive process of logical reasoning often cannot be used to achieve a satisfactory result within a reasonable timeframe. We demonstrate the use of the similarity measures as an alternative and potentially complimentary technique to the logical reasoning method.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:582553 |
| Date | January 2007 |
| Creators | Hau, Jeffrey |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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