Learning domain ontologies from document warehouses and dedicated web sites

被引：188

作者：

Navigli, R ^{[1
]}

Velardi, P ^{[1
]}

机构：

[1] Univ Roma La Sapienza, Dipartimento Informat, I-00198 Rome, Italy

来源：

COMPUTATIONAL LINGUISTICS | 2004年 / 30卷 / 02期

关键词：

D O I：

10.1162/089120104323093276

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from Web sites, and more generally from documents shared among the members of virtual organizations. OntoLearn first extracts a domain terminology from available documents. Then, complex domain terms are semantically interpreted and arranged in a hierarchical fashion. Finally, a general-purpose ontology, WordNet, is trimmed and enriched with the detected domain concepts. The major novel aspect of this approach is semantic interpretation, that is, the association of a complex concept with a complex term. This involves finding the appropriate WordNet concept for each word of a terminological string and the appropriate conceptual relations that hold among the concept components. Semantic interpretation is based on a new word sense disambiguation algorithm, called structural semantic interconnections.

引用

页码：151 / 179

页数：29

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