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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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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Something more about storing triples

6-10. RDF Graph Data Model of Triples
Image by Peter Morville via Flickr

First of all it’s to be said that all the available triplestores, both the open source packages as the commercial services do their job. (In the picture: a triple) It’s a matter of preference and what you expect from the system. The most common opensource sytems that are widely in us as off May 2009 are listed are:

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Mining microblogs using semantic technologies

Class diagram for the LOD datasets

Image via Wikipedia

The framework for the tool that I will develop fits like a puzzle piece in a bigger system that is being developed in the research group “Social Learning” at Graz University of Technology. Selver Softic from Infonova GmbH and Ebner et al. from Social Learning recently wrote a paper about their ongoing research efforts aiming at knowledge discovery. They are aiming to provide a scientific architecture paradigm for building semantic applications that rely on social data. At the moment they are focussing on data from Twitter, like me. For this purpose they have implemented a tool Grabeteer for storing and caching social data. In this paper they outlined the architecture for a system that can extract, structure and link the data grabbed from Twitter by the Grabeteer. Read more of this post