An algorithm based on counterfactuals for concept learning in the Semantic Web

被引：71

作者：

Iannone, Luigi ^{[1
]}

Palmisano, Ignazio ^{[1
]}

Fanizzi, Nicola ^{[1
]}

机构：

[1] Univ Bari, Dipartimento Informat, I-70125 Bari, Italy

来源：

APPLIED INTELLIGENCE | 2007年 / 26卷 / 02期

关键词：

ontology learning; refinement operators; inductive reasoning; machine learning; knowledge management; ontology evolution;

D O I：

10.1007/s10489-006-0011-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the line of realizing the Semantic-Web by means of mechanized practices, we tackle the problem of building ontologies, assisting the knowledge engineers' job by means of Machine Learning techniques. In particular, we investigate on solutions for the induction of concept descriptions in a semi-automatic fashion. In particular, we present an algorithm that is able to infer definitions in the ALC. Description Logic (a sub-language of OWL-DL) from instances made available by domain experts. The effectiveness of the method with respect to past algorithms is also empirically evaluated with an experimentation in the document image understanding domain.

引用

页码：139 / 159

页数：21

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