Automatic Knowledge Base Evolution by Learning Instances
  Knowledge base is the way to store structured and unstructured data throughout the web. Since the size of the web is increasing rapidly, there are huge needs to structure the knowledge in a fully automated way. However fully-automated knowledge-base evolution on the Semantic Web is a major challenges, although there are many ontology evolution techniques available. Therefore learning ontology automatically can contribute to the semantic web society significantly. In this paper, we propose full-automated ontology learning algorithm to generate refined knowledge base from incomplete knowledge base and rdf-triples. Our algorithm is data-driven approach which is based on the property of each instance. Ontology class is being elaborated by generalizing frequent property of its instances. By using that developed class information, each instance can find its most relatively matching class. By repeating these two steps, we achieve fully-automated ontology evolution from incomplete basic knowledge base. 
Sundong Kim
cs.AI