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Allocca, Carlo
(2012).
DOI: https://doi.org/10.21954/ou.ro.0000ffcb
Abstract
Semantic Web can be seen as the web scale knowledge base where ontologies are intended to provide knowledge engineering and artificial intelligence support for modelling intelligent applications. Semantic Web Search Engine (SWSE), such as Watson, Swoogle and Sindice represent the state of the art of computer based system to support users in finding and therefore in reusing, the formalised knowledge and semantic data (ontologies) available on line. However, to find ontologies is a complex process. It generally requires formulating multiple queries, browsing many pages of results and assessing the returned ontologies against each other to obtain a relevant and adequate subset for the intended use. In this thesis we mainly focused on the investigation of the following research hypothesis: part of the issue with searching ontologies through SWSE systems comes from the lack of structure in the search results, where ontologies that are implicitly related to each other are presented as disconnected. Making explicit relationships between ontologies in the results of SWSEs improves their efficiency and the user satisfaction in searching ontologies. We use an ontology-based framework in order to both identify the characteristic of such relationships and design mechanisms for detecting and managing them in large scale ontology repository. We show how this framework is used on top of the repository of the Watson semantic web search engine, and integrated with its interface, in order to provide explicit information regarding the various types of relationships ontologies share in the results from Watson. We evaluate the benefit of this approach to ontology search in a user study, showing through multiple indicators that finding ontologies is made easier and more efficient when information regarding relationships between ontologies is used to provide links and structuring mechanisms in the results of the search engine.
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About
- Item ORO ID
- 65483
- Item Type
- PhD Thesis
- Academic Unit or School
- Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
- Copyright Holders
- © 2011 Carlo Allocca
- Depositing User
- ORO Import