Can linked data assist in expert profiling?

Scientific profiling in social networks involves the determination of a canditate’s (user) generated content. To determine if this content (in this case the microblogs) have scientific relevance, thus if a twitter user is an expert in a certain domain, we link hashtags to the linked data cloud. Specifically we try to discover scientific conferences, locations, people and events. In the literature we found an important validation for this idea. The general conclusion is that there are sources available to build such a system. But they are not properly interlinked. This thesis project is an effort to provide the interlinking between several LOD sources (most importantly Colinda, GeoNames and DBPedia). Other resources can definitely enhance the possibilities of the framework. But to prove the case we strictly limit the effort to technical scientific people and we use the hypothesis that if people are attending similar scientific conferences they are a good match.

Stankovic et al. studied expert search and profiling systems. Such systems aim to identify candidate experts and rank them with respect to their estimated expertise on a given topic, using available evidence. The authors found that traditional expert search and profiling systems exploit structured data from closed systems (e.g. email program) or unstructured data from open systems (e.g. the Web). However, on today’s Web, there is a growing number of data sets published according to the Linked Data principals, the majority of them being part of the Linked Open Data (LOD) cloud. As LOD connects data and people across different platforms in a meaningful way, one can assume that expert search and profiling systems would benefit from harnessing LOD. Read more of this post