Tag-Based Navigation and Visualization

Dimitrov, Dimităr; Helić, Denis; Strohmaier, Markus

doi:10.1007/978-3-319-90092-6_6

Cited by 12 publications

(6 citation statements)

References 73 publications

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“…Through several graphical elements, graphic-assisted textual TCs can help users capture the structural information of tags more directly as showed in Figure 2 (Fujimura et al, 2008). Two Comparative study of graphic-based tag clouds types of visualization are often applied to graphic-assisted textual TCs: connecting associated tags with line segments (Dimitrov et al, 2018) (seen as (c) in Figure 2) and clustering associated tags within graphical shadows or colors (Liu et al, 2014) (Chen et al, 2009) (see (a) and (b) in Figure 2, in which sub-figure (a) shows clustering tags by the samecolored words and (b) represents the grouping of tags by common graphical shadows). These two types of graphic-assisted visualization have preferred application circumstances.…”

Section: Graphic-assisted Textual Tcsmentioning

confidence: 99%

“…They avoid finding the tags' structure by reading the tags and understanding their content. However, as graphic-assisted textual TCs are still in the form of text, it is difficult to comprehend tag Graphics-assisted textual TCs using graphical shadows and lines (Dimitrov et al, 2018;Chen et al, 2009) OIR 44,5 meaning due to the lexical and linguistic diversity. On the other hand, line segments and graphical shadows (colors) can only visualize simple relationships among tags.…”

Section: Graphic-assisted Textual Tcsmentioning

confidence: 99%

“…Figure 2.Graphics-assisted textual TCs using graphical shadows and lines(Dimitrov et al, 2018;Chen et al, 2009) …”

mentioning

confidence: 99%

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Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

2020

OIR

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PurposeGraphic-based tag clouds aim to visually represent tag content and tag structure, and then to better represent tagged information for later search. However, few studies have clarified the features among varied visualization approaches involved in graphic-based tag clouds and compared them for the purpose of information search.Design/methodology/approachAfter reviewing four kinds of graphic-based tag clouds, an experimental demonstration was conducted in our study to verify how user performs in information search for a general seeking task by using them. Precision ratio, recall ratio, clicks on search and time for search were four variables tested in the experiment. Also, two supplementary tests were respectively carried out to manifest how graphic-based tag clouds contributed to the identification of target tags and tag clusters.FindingsThe experimental results showed that compared to tag content visual tag structure was more important to find related tags from tag clouds for information search. In addition, tag clouds that visually represented the semantic relationships within tags could make user more confident about their search result and carry out a shorter learning process during searching, which signified a tag-based information search path when visual elements were applied.Originality/valueThis research is one of the first to illustrate systematically the graphic-based tag clouds and their impacts on information search. The research findings could suggest on how to build up more effective and interactive tag clouds and make proposition for the design of search user interface by using graphic-based tag clouds.

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Section: Graphic-assisted Textual Tcsmentioning

confidence: 99%

Section: Graphic-assisted Textual Tcsmentioning

confidence: 99%

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Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

2020

OIR

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“…Tag clouds [10,11] use the most frequent tags occurring in document metadata to summarize a (usually predefined) set of documents. Tag size is used to indicate the relative importance of each tag, and sophisticated geometric arrangements are used to exploit limited space at best.…”

Section: Extended Tag Cloudsmentioning

confidence: 99%

SAES: An Introduction to Self-Adapting Exploratory Structures

Sacco¹

2019

Future Internet

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Self-adapting exploratory structures (SAESs) are the basic components of exploratory search. They are abstract structures which allow searching or querying of an information base and summarizing of results using a uniform representation. A definition and a characterization of SAES is given, as well as a discussion of structures that are SAES or can be modified in order to become SAES. These include dynamic taxonomies (also known as faceted search), tag clouds, continuous sliders, geographic maps, and dynamic clustering methods, such as Scatter-Gather. Finally, the integration of these structures into a single interface is discussed.

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“…With the introduction of collaborative tagging, social bookmarking systems, which started as a specific kind of social navigation, emerged into a new kind of social information access that can support both search and navigation. Since other chapters analyze collaborative tagging and bookmarking in detail [29,88], we will not discuss it further in this chapter.…”

Section: Sharing Destinations and Paths Via Explicit Asynchronous Tracesmentioning

confidence: 99%

Social Navigation

Farzan¹,

Brusilovsky²

2018

Social Information Access

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In this chapter, we present one of the pioneering approaches in supporting users in navigating complex information spaces: social navigation support. Social navigation support is inspired by the natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details of various approaches in implementing social navigation support in the information space, and we connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of sample applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews the more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach for various other social information access approaches, we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state.

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Tag-Based Navigation and Visualization

Cited by 12 publications

References 73 publications

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

SAES: An Introduction to Self-Adapting Exploratory Structures

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