SKOS is at the intersection of three disciplines and their paradigms:
Whilst librarians, taxonomists, and specialists in the fields of text mining and entity extraction have started to embrace SKOS, especially ‘ontologists’ from artificial intelligence community still remain sceptical about the capabilities of SKOS.
With the latest release of PoolParty Thesaurus Server a full-blown ontology management facility has been introduced which can now be used to extend expressivity of SKOS knowledge models. For instance, SKOS concepts can become any other type of resource and by that schemas of additional relations and attributes can be applied to the concept.
PoolParty’s philosophy is to support users with Simple Knowledge Organization Systems (SKOS) first, to let them grow instantly by using various mechanisms like ontologies, text corpus analysis or linked data enrichment. All of them can nicely be combined. Users benefit from a step to step approach, not being bothered by an overarching approach from the very initial step. Learn more >>>
The PoolParty approach for efficient knowledge modeling is based on methods from
text analytics and text mining
linked data management
SKOS thesaurus modeling
ontology engineering and
and recombines these techniques to a unique approach to create complex knowledge models which can be further used for all of the above mentioned tasks, semantic search, and knowledge discovery in big data sets.
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