@article{maedche2005ontology,
  abstract = {he Semantic Web relies heavily on formal ontologies to structure data for comprehensive and transportable machine understanding. Thus, the proliferation of ontologies factors largely in the Semantic Web’s success. Ontology learning greatly helps ontology engineers construct ontologies. The vision of ontology learning that we propose includes a number of complementary disciplines that feed on different types of unstructured, semistructured, and fully structured data to support semiautomatic, cooperative ontology engineering. Our ontology- learning framework proceeds through ontology import, extraction, pruning, refinement, and evaluation, giving the ontology engineer coordinated tools for ontology modeling. Besides the general framework and architecture, this article discusses techniques in the ontology-learning cycle that we implemented in our ontology-learning environment, such as ontology learning from free text, dictionaries, and legacy ontologies. We also refer to other techniques for future implementation, such as reverse engineering of ontologies from database schemata or learning from XML documents.},
  author = {Maedche, A. and Staab, S.},
  file = {maedche2005ontology.pdf:maedche2005ontology.pdf:PDF},
  groups = {public},
  interhash = {77b7223b737581bba0f4819b1de46b73},
  intrahash = {29f44c4032ba381ec36fb5d0f36a1955},
  issn = {1541-1672},
  journal = {Intelligent Systems, IEEE},
  journalpub = {1},
  number = 2,
  pages = {72--79},
  publisher = {IEEE},
  timestamp = {2010-11-10 10:43:24},
  title = {Ontology learning for the semantic web},
  url = {http://scholar.google.de/scholar.bib?q=info:4sWpt0uwOjkJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0},
  username = {dbenz},
  volume = 16,
  year = 2005
}