摘要
Deep Web sources contain a large of high-quality and query-related structured date. One of the challenges in the Deep Web is extracting result schemas of Deep Web sources. To address this challenge, this paper describes a novel approach that extracts both result data and the result schema of a Web database. The approach first models the query interface of a Deep Web source and fills in it with a specifically query instance. Then the result pages of the Deep Web sources are formatted in the tree structure to retrieve subtrees that contain elements of the query instance, Next, result schema of the Deep Web source is extracted by matching the subtree＇ nodes with the query instance, in which, a two-phase schema extraction method is adopted for obtaining more accurate result schema. Finally, experiments on real Deep Web sources show the utility of our approach, which provides a high precision and recall.
Deep Web sources contain a large of high-quality and query-related structured date. One of the challenges in the Deep Web is extracting result schemas of Deep Web sources. To address this challenge, this paper describes a novel approach that extracts both result data and the result schema of a Web database. The approach first models the query interface of a Deep Web source and fills in it with a specifically query instance. Then the result pages of the Deep Web sources are formatted in the tree structure to retrieve subtrees that contain elements of the query instance, Next, result schema of the Deep Web source is extracted by matching the subtree＇ nodes with the query instance, in which, a two-phase schema extraction method is adopted for obtaining more accurate result schema. Finally, experiments on real Deep Web sources show the utility of our approach, which provides a high precision and recall.
基金
Supported by the National Natural Science Foundation of China (60673139, 60473073, 60573090)
