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Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is a subtask of information extraction. The goal of ontology learning is to semi-automatically extract relevant concepts and relations from a given corpus or other kinds of data sets to form an ontology.

The automatic creation of ontologies is a task that involves many disciplines. Typically, the process starts by extracting terms and concepts or noun phrase from plain text using a method from terminology extraction. This usually involves linguistic processors (e.g. part of speech tagging, phrase chunking). Then statistical [1] or symbolic [2] [3] techniques are used to extract relation signatures. The intentional aspects of domain are formalized by Ontology. Extensional part is commanded by the knowledge based on instances of concepts and relations on the basis of ontology. For instance, these approaches try to detect that "to eat" denotes a relation between a concept denoted by "animal" and a concept denoted by "food". Recently, a graph-based approach has been proposed which extracts a domain taxonomy - i.e., the backbone of an ontology - from scratch.[4]

See alsoEdit


  1. A. Maedche and S. Staab. Learning ontologies for the semantic web. In Semantic Web Worskhop 2001.
  2. 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.
  3. 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.
  4. R. Navigli, P. Velardi, S. Faralli. A Graph-based Algorithm for Inducing Lexical Taxonomies from Scratch. Proc. of the 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011), Barcelona, Spain, July 19-22nd, 2011.

Bibliography Edit

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