TY - GEN
T1 - An ontology-based query system for digital libraries
AU - Xu, Xiaomei
AU - Zhang, Feifei
AU - Niu, Zhendong
PY - 2008
Y1 - 2008
N2 - With the development of Internet, more and more on-line information has become precious wealth that we can access to. High quality information is often stored in dedicated digital libraries. However, query system of most digital libraries based on keyword matching couldn't make users satisfied. This paper presents an ontology-based digital library query system, whose capability goes far beyond simple key word based query. There are two important components in our system: query expansion and ontology extraction. In the proposed query system, we integrate 'WorldNet' as the query expansion module to broaden the query scope, which leads to better recall rate for retrieval. A novel ontology inference algorithm was also proposed, which automatically constructs the ontology of the knowledge base for a specific query domain. With this domain specific ontology based knowledge representation, our query system is capable of human-like general semantic reasoning to disambiguate the ambiguous words and improve the precision of retrieval. We evaluate the proposed system on a large set of documents from 10 different subject domains. Experimental results demonstrate the effectiveness of our proposed system. Our system achieves on average 5% improvement compared with the traditional key word based search method.
AB - With the development of Internet, more and more on-line information has become precious wealth that we can access to. High quality information is often stored in dedicated digital libraries. However, query system of most digital libraries based on keyword matching couldn't make users satisfied. This paper presents an ontology-based digital library query system, whose capability goes far beyond simple key word based query. There are two important components in our system: query expansion and ontology extraction. In the proposed query system, we integrate 'WorldNet' as the query expansion module to broaden the query scope, which leads to better recall rate for retrieval. A novel ontology inference algorithm was also proposed, which automatically constructs the ontology of the knowledge base for a specific query domain. With this domain specific ontology based knowledge representation, our query system is capable of human-like general semantic reasoning to disambiguate the ambiguous words and improve the precision of retrieval. We evaluate the proposed system on a large set of documents from 10 different subject domains. Experimental results demonstrate the effectiveness of our proposed system. Our system achieves on average 5% improvement compared with the traditional key word based search method.
UR - https://www.scopus.com/pages/publications/63149153403
U2 - 10.1109/PACIIA.2008.360
DO - 10.1109/PACIIA.2008.360
M3 - Conference contribution
AN - SCOPUS:63149153403
SN - 9780769534909
T3 - Proceedings - 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008
SP - 222
EP - 226
BT - Proceedings - 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008
T2 - 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008
Y2 - 19 December 2008 through 20 December 2008
ER -