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.
