Discovering and ranking semantic associations over a Large RDF metabase

Authors:
Chris Halaschek;Boanerges Aleman-Meza;I. Budak Arpinar;Amit P. Sheth
Affiliations:
Large Scale Distributed Information Systems (LSDIS) Lab, Computer Science Department, University of Georgia, Athens, GA;Large Scale Distributed Information Systems (LSDIS) Lab, Computer Science Department, University of Georgia, Athens, GA;Large Scale Distributed Information Systems (LSDIS) Lab, Computer Science Department, University of Georgia, Athens, GA;Large Scale Distributed Information Systems (LSDIS) Lab, Computer Science Department, University of Georgia, Athens, GA
Venue:
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Year:
2004

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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.