Ontology learning
From Wikipedia, the free encyclopedia
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] techniques are used to extract relation signatures. 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".
[edit] See also
- Information extraction
- Semantic Web
- Computational linguistics
- Natural language processing
- Domain Ontology
- Taxonomy
- Glossary
- Text simplification
- Text mining
[edit] References
- ^ A. Maedche and S. Staab. Learning ontologies for the semantic web. In Semantic Web Worskhop 2001. [1]
- ^ 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. [2]
