Terminological ontology learning and population using latent Dirichlet allocation

被引：27

作者：

Colace, Francesco ^{[1
]}

De Santo, Massimo ^{[1
]}

Greco, Luca ^{[1
]}

Amato, Flora ^{[2
]}

Moscato, Vincenzo ^{[2
]}

Picariello, Antonio ^{[2
]}

机构：

[1] Univ Salerno, Dipartimento Ingn Informaz Ingn Elect & Matmat Ap, Salerno, Italy

[2] Univ Naples Federico II, Dipartimento Ingn Elect & Tecnol Informaz, Naples, Italy

来源：

JOURNAL OF VISUAL LANGUAGES AND COMPUTING | 2014年 / 25卷 / 06期

关键词：

Ontologies; Ontology learning; Ontology population; Latent Dirichlet allocation;

D O I：

10.1016/j.jvlc.2014.11.001

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：818 / 826

页数：9

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