Abstract
Searching relevant information from Web may be a very tedious task. Usually Web search engines return search results in a global ranking making it difficult to the users to browse in different topics or subtopics that they query. If people cannot navigate through the Web site, they will quickly leave. Thus, designing effective navigation strategies on Web sites is crucial. In this paper we provide and implement centrality indices to guide the user for an effective navigation of Web pages. Such indices support users gaining more relevant results to their query and then group the search results into categories according to the different meanings of this query. We get inspiration from well-know location family problems to compute the center of a graph: a joint use of such indices guarantees the automatic selection of the best starting point for each cluster. To validate our approach, we have developed a system that implements the techniques described in this paper on top of an engine for keyword-based search over RDF data. Such system exploits an interactive front-end to support the user in the visualization of both annotations and corresponding Web pages. Experiments over widely used benchmarks have shown very good results.
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De Virgilio, R. (2013). Centrality Indices for Web Search Engine Results Understanding. In: Cuzzocrea, A., Maabout, S. (eds) Model and Data Engineering. MEDI 2013. Lecture Notes in Computer Science, vol 8216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41366-7_5
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DOI: https://doi.org/10.1007/978-3-642-41366-7_5
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