Collaborative semantic association discovery from linked data

Authors:
Qingzhao Zheng;Huajun Chen;Tong Yu;Gang Pan
Affiliations:
Zhejiang University;Zhejiang University;Zhejiang University;Zhejiang University
Venue:
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Year:
2009

Citing 5
Cited 0

Ρ-Queries: enabling querying for semantic associations on the semantic web

WWW '03 Proceedings of the 12th international conference on World Wide Web
SemRank: ranking complex relationship search results on the semantic web

WWW '05 Proceedings of the 14th international conference on World Wide Web
Information Retrieval and Knowledge Discovery Utilizing a BioMedical Patent Semantic Web

IEEE Transactions on Knowledge and Data Engineering
Semantic analytics on social networks: experiences in addressing the problem of conflict of interest detection

Proceedings of the 15th international conference on World Wide Web
Linked data on the web (LDOW2008)

Proceedings of the 17th international conference on World Wide Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

The efforts of publishing and interlinking structured data on the Semantic Web will result in a global network of databases, or the Linked Data, which provides huge potential for discovering hidden relationships. We present a multi-agent framework for Semantic Associations Discovery (SAD) from distributed linked data on the Semantic Web. Here, agents collaborate in SAD by publishing inter-dependent hypotheses and evidences, giving rise to an evidentiary network that connects and ranks diverse knowledge elements. We evaluate this framework through simulation, and the results show that the framework is suitable in cross-domain relationship discovery tasks.