Linked Data Metrics for Flexible Expert Search on the open Web
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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.
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