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.

http://socialweb.semanticprofiling.net/client Read more of this post

Realtime Social Semantic Web

In another similar project we are trying to bring realtime RDF annotation to the Social Web! Read more of this post

User Analysis possibilities: first simple Demo!

I implemented a first very basic demonstration to show the possibilities for analysis of the annotated tweet data. This first demo makes no use of any domain knowledge or linked data. It is just uses the annotated user data and associated tags.

It matches two users based on similar hashtags. Of course this can be done on many ways. But the semantic profiling framework (in its current state) made an implementation for the logic possible in just half an hour with 3 lines of code on top of the framework.

The next and final improvement for this simple demo will be to identify scientific conferences in the list of common tags.

Try it yourself

If you are not in the database yet, you can do it by using this link:

http://linkeddata.semanticprofiling.net/interlinking/provider.php?user=your_name

http://linkeddata.semanticprofiling.net/test/usermatch_demo.php?q=laurens_d_v&q2=selvers

First analysis demo

First analysis demo


SPARQL Endpoint set-up and load any twitter profile into the RDF Store

This weekend I optimized the triplification and annotation process for every twitter user. From now on it is possible to load any twitter user and store the annotated triples in the ARC2 TripleStore. A SPARQL endpoint allows querying

For now you can be load your own twitter account and associated tweets into the system with this url:

http://linkeddata.semanticprofiling.net/interlinking/provider.php?user=your_username

Contribute to the semantic web and do it NOW! 🙂 Any questions or extreme load times, I will be happily to look into and fix it!

The SPARQL Endpoint can be accessed on:

http://linkeddata.semanticprofiling.net/interlinking/endpoint_handler.php

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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.

Read more of this post

Extraction basically complete

 

Notation3 Logo

Notation3 Logo

All users who registered before this week on Grabeeter can get a N3/Turtle version of their profile and tweets on the following url:
http://linkeddata.semanticprofiling.net/extraction/twitter_user_n3.php?user=…

 


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