Discovering and Ranking Semantic Associations over a Large RDF Metabase

TitleDiscovering and Ranking Semantic Associations over a Large RDF Metabase
Publication TypeConference Paper
Year of Publication2004
AuthorsAmit Sheth, C. Halaschek, Boanerges Aleman-Meza, Ismailcem Budak Arpinar
Conference NameDiscovering and Ranking Semantic Associations over a Large RDF Metabase
Pagination1317-1320
Date Published09/2004
Conference Location30th International Conference on Very Large Data Bases
KeywordsOntology, ranking, semantic associations, Semantic Web, SWETO test-bed
Abstract

Information retrieval over semantic metadata has recently received a great amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this data is becoming an active research topic. Just as ranking of documents is a critical component of today's search engines, the ranking of relationships will be essential in tomorrow's semantic analytics engines. Building upon our recent work on specifying these semantic relationships, which we refer to as Semantic Associations, we demonstrate a system where these associations are discovered among a large semantic metabase represented in RDF. Additionally we employ ranking techniques to provide users with the most interesting and relevant results.

Full Text

C. Halaschek, B. Aleman-Meza, I. B. Arpinar, and A. Sheth, “Discovering and Ranking Semantic Associations over a Large RDF Metabase,”in Proceedings of the 30th International Conference on Very Large Data Bases, Toronto, Canada, August 31-September 3, 2004, Mario A. Nascimento et al. (Eds.), Morgan Kauffman, 2004, pp. 1317–1320.

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