DocumentCode :
495271
Title :
Extracting Attributes from Deep Web Interface Using Instances
Author :
Liang, Hao ; Ren, Fei ; Zuo, Wanli ; He, Fengling ; Wang, Junhua
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Volume :
5
fYear :
2009
fDate :
March 31 2009-April 2 2009
Firstpage :
692
Lastpage :
696
Abstract :
There is myriad high quality information in the Deep Web and the feasible method to access the Deep Web is through the query interface of the Deep Web. Itpsilas necessary to extract abundant attributes and semantic relation description from the query interface. Automatic extracting attributes from the query interface and automatically translating a query is a solvable way for addressing the current limitations in accessing Deep Web data sources. We design a framework to automatically extract the attributes and instances from the query interface using the WordNet as a kind of ontology technique to enrich the semantic description of the attributes. Each attribute is extended into a candidate attribute set in the form of a hierarchy tree. At the same time, the hierarchy tree generated by ontology describes the semantic relation of the attributes in the same query interface. We carry out our experiments in the real-world domain. The results of the experiments showed the validation of query translation framework.
Keywords :
Internet; ontologies (artificial intelligence); query processing; WordNet; attribute extraction; deep Web data sources; deep Web interface; high quality information; ontology technique; query interface; semantic description; semantic relation description; Computer interfaces; Computer science; Data mining; Databases; Educational institutions; HTML; Knowledge engineering; Laboratories; Ontologies; Programming profession; Deep Web; Surface Web; WordNet; ontology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3507-4
Type :
conf
DOI :
10.1109/CSIE.2009.93
Filename :
5170622
Link To Document :
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