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
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