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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | No |
| Title | How Similar Is It? Towards Personalized Similarity Measures in Ontologies |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Page Range | 1347 - 1366 |
| Event Title | 7. Internationale Tagung Wirtschaftsinformatik |
| Abstract Text | Finding a good similarity assessment algorithm for the use in ontologies is central to the functioning of techniques such as retrieval, matchmaking, clustering, data-mining, ontology translations, automatic database schema matching, and simple object comparisons. This paper assembles a catalogue of ontology based similarity measures, which are experimentally compared with a �similarity gold standard� obtained by surveying 50 human subjects. Results show that human and algorithmic similarity predications varied substantially, but could be grouped into cohesive clusters. Addressing this variance we present a personalized similarity assessment procedure, which uses a machine learning component to predict a subject�s cluster membership, providing an excellent prediction of the gold standard. We conclude by hypothesizing ontology dependent similarity measures. |
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