Ontology learning

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Ontology learning (ontology extraction,ontology augmentation generation, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process.

Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical[1] or symbolic[2][3] techniques are used to extract relation signatures, often based on pattern-based[4] or definition-based[5] hypernym extraction techniques.

  1. ^ A. Maedche and S.Staab. Learning ontologies for the semantic web.In Semantic Web Worskhop 2001.
  2. ^ Roberto Navigli and Paola Velardi. Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites, Computational Linguistics,30(2), MIT Press, 2004, pp.151-179.
  3. ^ P.Velardi, S.Faralli, R.Navigli. OntoLearn Reloaded: A Graph-based Algorithm for Taxonomy Induction. Computational Linguistics, 39(3), MIT Press,2013, pp.665-707.
  4. ^ Marti A. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the Fourteenth International Conference on Computational Linguistics, pages 539--545, Nantes, France, July 1992.
  5. ^ R.Navigli, P. Velardi. Learning Word-Class Lattices for Definition and Hypernym Extraction.Proc.of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), Uppsala, Sweden, July 11–16, 2010, pp.1318-1327.