HCHIRSIMEX: An extended method for domain ontology learning based on conditional mutual information

Author

El Idrissi, Omar ; Frikh, Bouchra ; Ouhbi, Brahim

Author_Institution

LM2I Lab., Moulay Ismail Univ., Meknès, Morocco

fYear

2014

fDate

20-22 Oct. 2014

Firstpage

91

Lastpage

95

Abstract

This paper presents HCHIRSIMEX, an extended version of our previous algorithm HCHIRSIM for building domain ontology from web corpus. The new version introduces a novel measure based on the Conditional Mutual Information (CMI) statistic method to define the taxonomic relations and the similarity between selected concepts. By using this method, the ontology extracted by HCHIRSIMEX is more concise and contains a richer concept knowledge base compared with the previous version HCHIRSIM. To evaluate our new algorithm effectiveness, we apply the two algorithms and Sanchez et al. algorithm in Finance domain ontology constructed from the web. Then, we compare the obtained concepts with those on the “Financial glossary” provided by Yahoo.com.

Keywords

financial data processing; learning (artificial intelligence); ontologies (artificial intelligence); CMI statistic method; HCHIRSIMEX method; Web corpus; conditional mutual information; domain ontology learning; finance domain ontology; financial glossary; knowledge base; taxonomic relations; Algorithm design and analysis; Data mining; Finance; Mutual information; Ontologies; Semantics; Terminology; CHIR-statistic; Conditional Mutual Information; Domain Ontology; Finance Ontology; Machine Learning; Ontology learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Technology (CIST), 2014 Third IEEE International Colloquium in

Conference_Location

Tetouan

Print_ISBN

978-1-4799-5978-5

Type

conf

DOI

10.1109/CIST.2014.7016600

Filename

7016600