Semantic interoperability is essential on the Semantic Web to enable different information systems to exchange data. Ontology matching has been recognised as a means to achieve semantic interoperability on the Web by identifying similar information in heterogeneous ontologies. Existing ontology matching approaches have two major limitations. The first limitation relates to similarity metrics, which provide a pessimistic value when considering complex objects such as strings and conceptual entities. The second limitation relates to the role of description logic reasoning. In particular, most approaches disregard implicit information about entities as a source of background knowledge. In this thesis, we first present a new similarity function, called the degree of commonality coefficient, to compute the overlap between two sets based on the similarity between their elements. The results of our evaluations show that the degree of commonality performs better than traditional set similarity metrics in the ontology matching task. Secondly, we have developed the Knowledge Organisation System Implicit Mapping (KOSIMap) framework, which differs from existing approaches by using description logic reasoning (i) to extract implicit information as background knowledge for every entity, and (ii) to remove inappropriate correspondences from an alignment. The results of our evaluation show that the use of Description Logic in the ontology matching task can increase coverage. We identify people interested in ontology matching and reasoning techniques as the target audience of this work
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:558665 |
| Date | January 2012 |
| Creators | Reul, Quentin H. |
| Contributors | Sleeman, Derek; Pan, Jeff |
| Publisher | University of Aberdeen |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=186278 |
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