Context-Aware Semantic Association Ranking
Discovering complex and meaningful relationships, which we call Semantic Associations, is an important challenge. Just as ranking of documents is a critical component of today's search engines, ranking of relationships will be essential in tomorrow's semantic search engines that would support discovery and mining of the Semantic Web. Building upon our recent work on specifying types of Semantic Associations in RDF graphs, which are possible to create through semantic metadata extraction and annotation, we discuss a framework where ranking techniques can be used to identify more interesting and more relevant Semantic Associations. Our techniques utilize alternative ways of specifying the context using ontology. This enables capturing users' interests more precisely and better quality results in relevance ranking.
University of Georgia LSDIS Lab Technical Report 03-010, 2003.
© Aleman-Meza, B., Halaschek, C., Arpinar, I. B., & Sheth, A. P., 2003
Aleman-Meza, B., Halaschek, C., Arpinar, I. B., & Sheth, A. P. (2003). Context-Aware Semantic Association Ranking.