TY - GEN
T1 - Ontology Learning from Incomplete Semantic Web Data by BelNet
AU - Zhu, Man
AU - Gao, Zhiqiang
AU - Pan, Jeff Z.
AU - Zhao, Yuting
AU - Xu, Ying
AU - Quan, Zhibin
N1 - This work is partially funded by the National Science Foundation of China under grant 61170165, the EU IAPP K-Drive project (286348) and the EPSRC WhatIf project (EP/J014354/1).
PY - 2013
Y1 - 2013
N2 - 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. Theshortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learningfrom 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 andcapture the semantics of the data on the one hand, and tohandle incompleteness during the learning procedure on theother hand. The main contributions of this work are: (i)we introduce the architecture of BelNet, and correspondinglypropose 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.
AB - 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. Theshortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learningfrom 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 andcapture the semantics of the data on the one hand, and tohandle incompleteness during the learning procedure on theother hand. The main contributions of this work are: (i)we introduce the architecture of BelNet, and correspondinglypropose 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.
KW - Ontology learning
KW - probabilistic graphical model
KW - Semantic web
UR - http://www.scopus.com/inward/record.url?scp=84897690235&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2013.117
DO - 10.1109/ICTAI.2013.117
M3 - Published conference contribution
AN - SCOPUS:84897690235
SP - 761
EP - 768
BT - 2013 IEEE 25th International Conference on Tools with Artificial Intelligence
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013
Y2 - 4 November 2013 through 6 November 2013
ER -