Download From Keyword Search to Exploration by Max L. Wilson, Bill Kules, m.c. schraefel PDF

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By Max L. Wilson, Bill Kules, m.c. schraefel

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Subsequent studies of the Data Mountain have shown to be unproductive [42]. Another approach that has been expanded to 3D environments is the hyperbolic tree [127]. As part of a discussion on applying 3D visualizations to web search results, Benford et al. 31). Each query is manually or automatically positioned in a 3D space. Documents are positioned near the queries for which they are relevant. If a document is relevant to multiple queries, it is positioned between them. Each document’s overall relevance is shown by the size and shade of its representative icon.

Another approach is to automatically identify attributes of a collection or result set that are important, rather than explicitly producing labeled annotations. This approach is called clustering, and has shown success under popular information retrieval metrics such as precision and recall [74, 118, 189, 191] or task completion time [169] A one-level clustered overview was found helpful when the search engine failed to place desirable web pages high in the ranked results, possibly due to imprecise queries [89].

By allowing users to follow un-anticipated paths during search, the simple notion of choosing to see more similar results to a particular result also supports a number of information tactics that are not supported by standard keyword search interfaces [12], and so contributes mainly to the information-seeking level of Jarvelin and Ingwersen’s model of search. A study by Capra et al. [33] included the basic Endeca interface and, although this feature was not explicitly tested, the study involved both exploratory IS level and quick IR level tasks.

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