Visualization of a document collection: The vibe system

Olsen, Kai A.; Korfhage, Robert R.; Sochats, Kenneth M; Spring, Michael B.; Williams, J. G.

doi:10.1016/0306-4573(93)90024-8

Cited by 200 publications

(106 citation statements)

References 8 publications

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“…Often, this will have the secondary effect of grouping documents that share this additional information. The additional information displayed highlights the relationship between the documents and the query terms [10,33]; predefined document attributes (e.g., size, date, source or popularity) [12,23]; or user-specified attributes (i.e., predefined topics) [17,24,29].…”

Section: Related Workmentioning

confidence: 99%

Grouper: a dynamic clustering interface to Web search results

1999

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Users of Web search engines are often forced to sift through the long ordered list of document "snippets" returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on most major search engines. The NorthernLight search engine organizes its output into "custom folders" based on pre-computed document labels, but does not reveal how the folders are generated or how well they correspond to users' interests.In this paper, we introduce Grouper -an interface to the results of the HuskySearch meta-search engine, which dynamically groups the search results into clusters labeled by phrases extracted from the snippets. In addition, we report on the first empirical comparison of user Web search behavior on a standard ranked-list presentation versus a clustered presentation. By analyzing HuskySearch logs, we are able to demonstrate substantial differences in the number of documents followed, and in the amount of time and effort expended by users accessing search results through these two interfaces.

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Section: Related Workmentioning

confidence: 99%

Grouper: a dynamic clustering interface to Web search results

1999

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“…One aspect of NameSieve is the visualization of the conceptual content of a retrieved set of documents. In this aspect, it is similar to other systems that attempt to visualize search results based on keyword-level content, including such classic systems as Tilebars (Hearst, 1995) and VIBE (Olsen, Korfhage, Sochats, Spring, & Williams, 1993). It was also informed by research on clustering and organization of retrieved results by their semantic similarity (Chen & Dumais, 2000;Leuski & Allan, 2004).…”

Section: Related Studiesmentioning

confidence: 96%

Semantic annotation based exploratory search for information analysts

Ahn¹,

Brusilovsky²,

Grady³

et al. 2010

Information Processing & Management

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Abstract:The system presented in this article aims to improve information access through the use of semantic annotation utilizing a non-traditional approach. Instead of applying semantic annotations to enhance the internal information access mechanisms, we use them to empower the user of an information access system through an innovative named entity-based user interface -NameSieve. NameSieve was built to support an intelligence analyst during the process of exploratory search, an advanced type of search requiring multiple iterations of retrieval interleaved with browsing and analyzing the retrieved information. The proposed approach was implemented in the NameSieve system so that the system can transparently present a summary of search results in the form of entity "clouds." Therefore, these clouds allow the analyst to further explore the results in a novel manner, acting together as a faceted browsing interface. We ran a user study (with ten subjects) to examine the effect of NameSieve, and the study results reported in the paper demonstrate that this new way of applying semantic annotation information was actively used and was evaluated positively by the subjects. It enabled the subjects to work more productively and bring back most relevant documents.

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“…Similarly, STREAMIT enables direct control over keyword weighting in a force-directed model [10]. Visualizations such as "Dust & Magnet" [11] and VIBE [12] allow users to place specific points of interest in the space, acting as anchors from which the remaining points attract and repel form.…”

Section: Related Workmentioning

confidence: 99%

“…That is, to notice the change in relative distances between documents as a result of the highlighting they did while reading, searching, etc. Such exploration can be found in other tools, such as VIBE [12] or Dust&Magnet [11], where users can place "points of interest" corresponding to keywords in specific locations, and observe how the spatial layout adjusts given those keywords and locations.…”

Section: Aiding the Sensemaking Processmentioning

confidence: 99%

Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering

Endert

Fiaux

North

2012

IEEE Trans. Visual. Comput. Graphics

120

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Abstract-Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with usersʼ analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the userʼs reasoning and intuition.

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Visualization of a document collection: The vibe system

Cited by 200 publications

References 8 publications

Grouper: a dynamic clustering interface to Web search results

Grouper: a dynamic clustering interface to Web search results

Semantic annotation based exploratory search for information analysts

Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering

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