Graph-based visualization and exploration of results of a single Web search is becoming very popular with the wonderwheel of Google (http://www.googlewonderwheel.com/). In this chapter, the authors propose a novel multi-granular framework for visualization and exploration of the results of a complex search process, performed by a user by submitting several queries to possibly distinct search engines. The primary aim of the approach is to supply users with summaries, with distinct levels of details, of the results for a search process. It applies dynamic clustering to the results in each ordered list retrieved by a search engine evaluating a user’s query. The single retrieved items, the clusters so identified, and the single retrieved lists, are considered as dealing with topics at distinct levels of granularity, from the finest level to the coarsest one, respectively. Implicit topics are revealed by associating labels with the retrieved items, the clusters, and the retrieved lists. Then, some manipulation operators, defined in this chapter, are applied to each pair of retrieved lists, clusters, and single items, to reveal their implicit relationships. These relationships have a semantic nature, since they are labeled to approximately represent the shared documents and the shared sub-topics between each pair of combined elements. Finally, both the topics retrieved by the distinct searches and their relationships are represented through multi-granular graphs, that represent the retrieved topics at three distinct levels of granularity. The exploration of the results can be performed by expanding the graphs nodes to see their contents, and by expanding the edges to see their shared contents and their common sub-topics.

Web Search Results Discovery by Multi-Granular Graphs

CAMPI, ALESSANDRO;PSAILA, GIUSEPPE;RONCHI, STEFANIA
2011-01-01

Abstract

Graph-based visualization and exploration of results of a single Web search is becoming very popular with the wonderwheel of Google (http://www.googlewonderwheel.com/). In this chapter, the authors propose a novel multi-granular framework for visualization and exploration of the results of a complex search process, performed by a user by submitting several queries to possibly distinct search engines. The primary aim of the approach is to supply users with summaries, with distinct levels of details, of the results for a search process. It applies dynamic clustering to the results in each ordered list retrieved by a search engine evaluating a user’s query. The single retrieved items, the clusters so identified, and the single retrieved lists, are considered as dealing with topics at distinct levels of granularity, from the finest level to the coarsest one, respectively. Implicit topics are revealed by associating labels with the retrieved items, the clusters, and the retrieved lists. Then, some manipulation operators, defined in this chapter, are applied to each pair of retrieved lists, clusters, and single items, to reveal their implicit relationships. These relationships have a semantic nature, since they are labeled to approximately represent the shared documents and the shared sub-topics between each pair of combined elements. Finally, both the topics retrieved by the distinct searches and their relationships are represented through multi-granular graphs, that represent the retrieved topics at three distinct levels of granularity. The exploration of the results can be performed by expanding the graphs nodes to see their contents, and by expanding the edges to see their shared contents and their common sub-topics.
Quantitative Semantics and Soft Computing Methods for the Web: Perspectives and Applications
9781609608811
9781609608828
graph; search
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/605096
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