Knowledge capturing tools for domain experts: Exploiting named entity recognition and n-ary relation discovery for knowledge capturing in e-science

The success of the Semantic Web depends on the availability of content marked up using its description languages. Although the idea has been around for nearly a decade, the amount of Semantic Web content available is still fairly small. This is despite the existence of many digital archives containing lots of high quality collections which would, appropriately marked up, greatly enhance the reach of the Semantic Web. The archives themselves would benefit as well, by improved opportunities for semantic search, navigation and interconnection with other archives. The main challenge lies in the fact that ontology creation at the moment is a very detailed and complicated process. It mostly requires the service of an ontology engineer, who designs the ontology in accordance with domain experts. The software tools available, be it from the text engineering or the ontology creation disciplines, reflect this: they are built for engineers, not for domain experts. In order to real ly tap the potential of the digital collections, tools are needed that support the domain experts in marking up the content they understand better than anyone else. This paper presents an integrated approach to knowledge capturing and subsequent ontology creation, called WIKINGER, that aims at empowering domain experts to prepare their content for inclusion into the Semantic Web. This is done by largely automating the process through the use of named entity recognition and relation discovery.