Stankovic, Milan; Rowe, Matthew and Laublet, Philippe
Finding co-solvers on Twitter, with a little help from Linked Data.
In: 9th Extended Semantic Web Conference (ESWC 2012), 27-31 May 2012, Heraklion, Crete, Greece.
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In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. 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.
|External Project Funding Details:
|Funded Project Name||Project ID||Funding Body|
|Not Set||Not Set||EU-FP7 project WeGov (grant no. 248512)|
|Not Set||Not Set||EU-FP7 project Robust (grant no. 257859)|
||Knowledge Media Institute
||16 Aug 2012 14:11
||23 Oct 2012 22:35
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