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Ontology learning (ontology extraction, 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 those concepts 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〔A. Maedche and S. Staab. (Learning ontologies for the semantic web ). In Semantic Web Worskhop 2001. 〕 or symbolic 〔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. 〕〔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.〕 techniques are used to extract relation signatures, often based on pattern-based〔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. 〕 or definition-based〔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.〕 hypernym extraction techniques. == Procedure == Ontology learning is used to (semi-)automatically extract whole ontologies from natural language text.〔Cimiano, Philipp; Völker, Johanna; Studer, Rudi (2006). "Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text", ''Information, Wissenschaft und Praxis'', 57, p. 315 - 320, http://people.aifb.kit.edu/pci/Publications/iwp06.pdf (retrieved: 18.06.2012).〕〔Wong, W., Liu, W. & Bennamoun, M. (2012), "Ontology Learning from Text: A Look back and into the Future". ACM Computing Surveys, Volume 44, Issue 4, Pages 20:1-20:36.〕 The process is usually split into the following eight tasks, which are not all necessarily applied in every ontology learning system. # Domain terminology extraction # Concept discovery # Concept hierarchy derivation # Learning of non-taxonomic relations # Rule discovery # Ontology population # Concept hierarchy extension # Frame and event detection 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Ontology learning」の詳細全文を読む スポンサード リンク
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