Abstract:
One of the major Semantic Web challenges is the knowledge acquisition bottleneck. New content on the web is produced much faster than the respective machine readable annotations, while a scalable knowledge extraction from the legacy resources is still largely an open problem. This poster presents an ongoing research on an empirical knowledge representation and reasoning framework, which is tailored to robust and meaningful processing of emergent, automatically learned ontologies. According to the preliminary results of our EUREEKA1 prototype, the proposed framework can substantially improve the applicability of the rather messy emergent knowledge and thus facilitate the knowledge acquisition in an unprecedented way.
