Finding Co-solvers on Twitter, with a Little Help from Linked Data

Finding collaborators that can complement one’s competence in collaborative research and innovation projects has become important with the advent of multidisciplinary challenges and collaborative R&D in general. In this paper we propose a method for suggesting potential candidates for collaborative solving of innovation challenges online, based on their competence, similarity of interest and social proximity with the user receiving the recommendations. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com.