Topic-Centric Querying of Web Information Resourcest
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27625
This paper deals with the problem of modeling web information resources using expert knowledge and personalized user information, and querying them in terms of topics and topic relationships. We propose a model for web information resources, and a query language SQL-TC (Topic-Centric SQL) to query the model. The model is composed of web-based information resources (XML or HTML documents on the web), expert advice repositories (domain-expert-specified metadata for information resources), and personalized information about users (captured as user profiles, that indicate users’ preferences as to which expert advice they would like to follow, and which to ignore, etc). The query language SQL-TC makes use of the metadata information provided in expert advice repositories and embedded in information resources, and employs user preferences to further refine the query output. Query output objects/tuples are ranked with respect to the (expert-judged and user- preference-revised) importance values of requested topics/metalinks, and the query output is limited by either top n-ranked objects/tuples, or objects/tuples with importance values above a given threshold, or both. © Springer-Verlag Berlin Heidelberg 2001.
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