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Finding co-solvers on Twitter, with a little help from linked data

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date29/05/2012
Host publicationThe semantic web: research and applications 9th Extended Semantic Web Conference, ESWC 2012, Heraklion, Crete, Greece, May 27-31, 2012. Proceedings
EditorsElena Simperl, Philipp Cimiano, Axel Polleres, Oscar Corcho, Valentina Presutti
Place of publicationBerlin
PublisherSpringer Verlag
Pages39-55
Number of pages7
ISBN (Print)978-3-642-30283-1
Original languageEnglish

Conference

ConferenceExtended Semantic Web Conference
CountryGreece
Period27/05/1231/05/12

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7295
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceExtended Semantic Web Conference
CountryGreece
Period27/05/1231/05/12

Abstract

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.