2005 LearningaDomainOntofromHieStructTexts
- (Mokagonov et al., 2005) ⇒ Pavel Makagonov, Ruiz Figueroa A, Konstantin Sboychakov, and Alexander Gelbukh. (2005). “Learning a Domain Ontology from Hierarchically Structured Texts.” In: Proceedings of the 22nd International Conference on Machine Learning (ICML 2005).
Subject Headings: Ontology Learning.
Notes
Cited By
Quotes
Abstract
Any scientific or technical document is organized hierarchically: some sections of the text (such as the abstract or conclusions) summarize the contents of the main text; sections have titles describing their contents in general words; chapter titles describe the contents of a set of sections; book title describes the contexts of all chapters, etc. Moreover, whole collections of scientific documents are usually organized hierarchically: e.g., papers are organized in journals, conferences, etc., which in turn have their own titles. We exploit this hierarchical structure to learn a lexical ontology, in which subordination relationships roughly mirror those between the texts and titles in which these words occur: words occurring in more general titles subordinate the words occurring in the texts described by these titles.
References
,
| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2005 LearningaDomainOntofromHieStructTexts | Pavel Makagonov Ruiz Figueroa A Konstantin Sboychakov Alexander Gelbukh | Learning a Domain Ontology from Hierarchically Structured Texts | http://www.gelbukh.com/CV/Publications/2005/ICML-2005-ontology.pdf |