Ontology Learning from Incomplete Semantic Web Data by BelNet

Man Zhu, Zhiqiang Gao, Jeff Z. Pan, Yuting Zhao, Ying Xu, Zhibin Quan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Recent years have seen a dramatic growth of semantic web on the data level, but unfortunately not on the schema level, which contains mostly concept hierarchies. The shortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learning from semantic web data becomes an increasingly pressing issue. In this paper we propose a novel schemas learning approach -BelNet, which combines description logics (DLs) with Bayesian networks. In this way BelNet is capable to understand and capture the semantics of the data on the one hand, and to handle incompleteness during the learning procedure on the other hand. The main contributions of this work are: (i)we introduce the architecture of BelNet, and corresponding lypropose the ontology learning techniques in it, (ii) we compare the experimental results of our approach with the state-of-the-art ontology learning approaches, and provide discussions from different aspects.
Original languageEnglish
Title of host publication2013 IEEE 25th International Conference on Tools with Artificial Intelligence
PublisherIEEE
Pages761-768
Number of pages8
ISBN (Electronic)978-1-4799-2972-6, 978-1-4799-2971-9
DOIs
Publication statusPublished - 10 Feb 2014
Event25th International Conference on Tools with Artificial Intelligence - Washington DC, United States
Duration: 4 Nov 20136 Nov 2013
Conference number: 25

Publication series

NameInternational Conference on Tools with Artificial Intelligence
PublisherIEEE
ISSN (Print)1082-3409
ISSN (Electronic)2375-0197

Conference

Conference25th International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2013
Country/TerritoryUnited States
CityWashington DC
Period4/11/136/11/13

Fingerprint

Dive into the research topics of 'Ontology Learning from Incomplete Semantic Web Data by BelNet'. Together they form a unique fingerprint.

Cite this