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

Real Time Interlinking of Tweets (API)

In the previous post, I described a demonstration of a HTTP Stream with realtime annotated tweets. We improved the annotation by trying to identify concepts and linking them to resources in the LOD cloud. For example DBPedia, GeoNames and FreeBase. Read more of this post

Thinking about interlinking data contexts

Time to remind the project statement. To build a semantic profiling framework to support the connection of researchers. The main use case and application that the framework has to support is illustrated by what could be called: “the conference case”. Scientists and researchers are interested in very specific topics, this is best verified by the conferences they are attending. Another trend is that they all blog and tweet about these events. This creates huge opportunities for profiling. The attendees tweet about what they notice, what they remark as interesting for their own projects. What if we could connect these users using this information? We could call an application that does just that “Scientific Profiling“. This approach comes from the concept that the data produced in social networks can have true value if properly annotated and interlinked. A second requirement is to create a suitable context in which this information can get meaning. This is very important to identify which ontologies should be used. Read more of this post

From a valid RDF/XML for Twitter users to a dynamic SPARQL Endpoint

This weekend I upgraded the semantic profiling framework. Now it annotates for every Twitter user:

  • Its profile as SIOC UserAccount
  • The timeline as SIOC(Types) MicroBlog
  • All the tweets as SIOC(Types) MicroBlogPost

It grabs the tweets from a user in Grabeeter if the user has registered there. If not they are being retrieved with the Twitter API.

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