Discovering Relevant Topics Using DBPedia: Providing Non-obvious Recommendations

In this paper we propose an alternative method for generating topic suggestions to be used in an Open Innovation based expert identification system. An important requirement within this challenge is the identification of 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, using which we can recommend topics on certain proximity in the DBPedia concept graph. We show the important impact of the use of suggested lateral keywords to the raised awareness about the problem in a real problem broadcast.