Searching for experts on the Web: A review of contemporary expertise locator systems
ACM Transactions on Internet Technology (TOIT)
Using web 2.0 to locate expertise
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Quality-driven information filtering using the WIQA policy framework
Web Semantics: Science, Services and Agents on the World Wide Web
Telling experts from spammers: expertise ranking in folksonomies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Data summaries for on-demand queries over linked data
Proceedings of the 19th international conference on World wide web
Summary models for routing keywords to linked data sources
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Concept extraction applied to the task of expert finding
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
Using domain ontologies for finding experts in corporate wikis
Proceedings of the 7th International Conference on Semantic Systems
Tool support for technology scouting using online sources
ER'11 Proceedings of the 30th international conference on Advances in conceptual modeling: recent developments and new directions
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Benchmarking domain-specific expert search using workshop program committees
Proceedings of the 2013 workshop on Computational scientometrics: theory & applications
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As more and more user traces become available as Linked Data Web, using those traces for expert finding becomes an interesting challenge, especially for the open innovation platforms. The existing expert search approaches are mostly limited to one corpus and one particular type of trace - sometimes even to a particular domain. We argue that different expert communities use different communication channels as their primary mean for communicating and disseminating knowledge, and thus different types of traces would be relevant for finding experts on different topics. We propose an approach for adapting the expert search process (choosing the right type of trace and the right expertise hypothesis) to the given topic of expertise, by relying on Linked Data metrics. In a gold standard-based experiment, we have shown that there is a significant positive correlation between the values of our metrics and the precision and recall of expert search. We also present hy.SemEx, a system that uses our Linked Data metrics to recommend the expert search approach to serve for finding experts in an open innovation scenario at hypios. The evaluation of the users' satisfaction with the system's recommendations is presented as well.