The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authoritative ” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages ” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.

This paper presents two hyperlink analysis-based algorithms to find relevant pages for a given Web page (URL). The first algorithm comes from the extended cocitation analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of linear algebra theories to reveal deeper relationships among the Web pages and to identify relevant pages more precisely and effectively. The experimental results show the feasibility and effectiveness of the algorithms. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related researches.

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Although many publicly available search engines can retrieve relevant information reasonably well, the list of the retrieved web pages is still often too large or contains information that has no relevance to the query. Our goal is to improve the results of these search engines for queries generated by users while constructing and/or browsing concept mapbased knowledge models. By exploiting the propositional and hierarchical nature of concept maps, we have developed two algorithms, SAgent and WAgent, for filtering and ranking the results obtained by the search engines. The algorithms were implemented via mobile agents and evaluated empirically. In our experiments, six subjects submitted queries based on a concept map to publicly available search engines (Google, Altavista, Yahoo, Excite), and were asked to rank the relevance of the results; the agents also filtered and ranked the engines ’ output. The results indicated that the proposed algorithms are capable of identifying pages that the subjects considered are relevant to the context on the map Keywords Information filters, concept maps, searching, knowledge models, information retrieval, mobile agents

This paper describes the system KN(Knowledge Network) which visualizes the linkage structure of a user's personal knowledge of URLs. It is an extension the system in [3]. A personal knowledge of URL-links is drawn as a directed graph called as a Web graph. Some softwares display the static structure of a particular web site. The linkage information on WWW are also studied (e.g.[1, 4, 5]). Our database keeps not only general linkage information, but also personal knowledge. The system supports personal and progressive linkage database