From the webpage:
SKOSsy extracts data from LOD sources like DBpedia (and basically from any RDF based knowledge base you like) and works well for automatic text mining and whenever a seed thesaurus should be generated for a certain domain, organisation or a project.
If automatically generated thesauri are loaded 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. With SKOSsy, thesaurus projects you don´t have to be started in the open countryside anymore. See also how SKOSsy is integrated into PPT.
- 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 thesauri serve as a basis for domain specific text extraction and knowledge enrichment
- SKOSsy based semantic search usually outperform search algorithms based on pure statistics since they contain high-quality information about relations, labels and disambiguation
- SKOSsy works perfectly together with PoolParty product family
DBpedia is probably closer to some user’s vocabulary than most formal ones. 😉
I have the sense that rather than asking experts for their semantics (and how to represent them), we are about to turn to users to ask about their semantics (and choose simple ways to represent them).
If results that are useful to the average user are the goal, it is a move in the right direction.