Information retrieval by constrained spreading activation in semantic networks
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
Application of Spreading Activation Techniques in InformationRetrieval
Artificial Intelligence Review
A hybrid approach for searching in the semantic web
Proceedings of the 13th international conference on World Wide Web
Exploratory search: from finding to understanding
Communications of the ACM - Supporting exploratory search
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
An Explorative Association-Based Search for the Semantic Web
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
The KGRAM Abstract Machine for Knowledge Graph Querying
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
dbrec: music recommendations using DBpedia
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Finding co-solvers on twitter, with a little help from linked data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Linked open data to support content-based recommender systems
Proceedings of the 8th International Conference on Semantic Systems
Knowledge-based music retrieval for places of interest
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Discovery hub: a discovery engine on the top of DBpedia
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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Exploratory search systems are built specifically to help the user in his cognitive consuming search tasks like learning or topic investigation. Some of these systems are built on the top of linked data and use semantics to provide cognitively-optimized search experiences. Thanks to their richness and to their connected nature linked data datasets can serve as a ground for advanced exploratory search. We propose to address the case of mixed interests' exploration in the form of composite queries (several unitary interests combined) e.g. exploring results and make discoveries related to both The Beatles and Ken Loach.. The main contribution of this paper is the proposition of a novel method that processes linked-data for exploratory search purpose. It makes use of a semantic spreading activation algorithm coupled with a sampling technique. Its particularity is to not require any results preprocessing. Consequently this method offers a high level of flexibility for querying and allows, among others, the expression of composite interests' queries on remote linked data sources. This paper also details the analysis of the algorithm behavior over DBpedia and describes an implementation: the Discovery Hub application. It is an exploratory search engine that notably supports composite queries. Finally the results of a user evaluation are presented.