Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Algorithmic detection of semantic similarity
WWW '05 Proceedings of the 14th international conference on World Wide Web
Automatic computation of semantic proximity using taxonomic knowledge
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Expertise drift and query expansion in expert search
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Social Semantics and Its Evaluation by Means of Semantic Relatedness and Open Topic Models
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
dbrec: music recommendations using DBpedia
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Finding co-solvers on twitter, with a little help from linked data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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In this paper we propose an alternative method for generating topic suggestions for the needs of expert finding in Open Innovation. An important requirement of Open Innovation scenarios is to be able to identify topics lateral to a given innovation problem, and use them to broaden the broadcast of the problem without compromising on relevancy. We propose an approach based on DBpedia -- a Linked Data version of Wikipedia -- which enables us to recommend topics facilitating their proximity in the DBpedia concept graph. Relying on this source we can also filter out certain types of concepts irrelevant to industrial problem solving. We evaluate our approach against the adWords keyword suggestion system here we also show the ability of our system to predict lateral topics that appeared in the actual solutions submitted to past problem challenges. Secondly we evaluate user satisfaction with the proposed keywords from both systems, in terms of relevancy and unexpectedness. Finally we show the significant impact of the use of suggested lateral keywords to the raised awareness about the problem in a real Open Innovation problem broadcast.