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An ontology based approach to measuring the semantic similarity between information objects in personal information collections

Shi, Lei and Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 2010. An ontology based approach to measuring the semantic similarity between information objects in personal information collections. Presented at: KES 2010: Knowledge-Based and Intelligent Information and Engineering Systems, Cardiff, UK, 8-10 September 2010. Published in: Setchi, Rossitza M. ORCID: https://orcid.org/0000-0002-7207-6544 and Jordanov, Ivan eds. Knowledge-Based and Intelligent Information and Engineering Systems:14th International Conference, KES 2010, Cardiff, UK, September 8-10, 2010, Proceedings, Part I. Lecture Notes in Computer Science. Lecture Notes in Computer Science, Vol. 6276 , vol.6276 Springer Verlag, pp. 617-626. 10.1007/978-3-642-15387-7_65

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Abstract

This paper introduces a semantic approach to personal information management, which employs natural language processing, ontologies and a vector space model to measure the semantic similarity between information objects in personal information collections. The approach involves natural language processing, named entity recognition, and information object integration. In particular, natural language processing is used to detect meaningful and semantically distinguishable information objects within collections of personal information. Then, the named entities are extracted from these information objects and their features (such as weight and category) are used to measure the semantic similarity between them. Further research includes using the semantic similarity measure developed to index and retrieve information objects in a semantic based system for personal information management.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Uncontrolled Keywords: ontology; named entity recognition; semantic similarity; personal information management; information object
Publisher: Springer Verlag
ISBN: 97873642153860
ISSN: 0302-9743
Last Modified: 06 Jul 2023 10:16
URI: https://orca.cardiff.ac.uk/id/eprint/21307

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