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intelligent search, linked data, text extraction

Using DBpedia to generate SKOS thesauri

In recent years, we have constantly discussed the application of thesauri and other knowledge models to improve search. Many people understand that thesaurus based search is in many cases better than search algorithms purely based on statistics. Of course the big contra always was, “the costs are too high to establish a good-enough thesaurus or even a high-quality one”.

Imagine you could generate any thesaurus you would like for nearly any knowledge domain you can think of with quite a good quality! Sounds impossible? Reminds you of all the promises made by text mining software which generates “semantic nets” from scratch?

Here at the Semantic Web Company we have been working on SKOSsy for a while. I will explain what this web service can do for you:

SKOSsy generates SKOS based thesauri in German or in English for a domain you are interested in. SKOSsy extracts data from DBpedia, so it can cover anything which is in DBpedia. Thus, SKOSsy works well whenever a first seed thesaurus should be generated for a certain organisation or project. If you load the automatically generated thesaurus into an editor like PoolParty Thesaurus Manager (PPT) you can start to enrich the knowledge model by additional concepts, relations and links to other LOD sources. But you don´t have to start in the open countryside with your thesaurus project.

With SKOSsy in place custom-tailored text extractors can be produced with low effort. To sum up,

  • SKOSsy makes heavy use of Linked Data sources, especially DBpedia
  • SKOSsy can generate SKOS thesauri for virtually any domain within a few minutes
  • Such thesauri can be improved, curated and extended to one´s individual needs but they serve usually as “good-enough” knowledge models for any semantic search application you like
  • SKOSsy based semantic search usually outperform search algorithms based on statistics since they contain high-quality information about relations, labels and disambiguation
  • SKOSsy works perfectly together with PoolParty product family

Which domains are you interested in? Give it a try!

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Bridging the gap between Collective Intelligence, Expert Views and Machine Learning

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