Search (7 results, page 1 of 1)

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Content

The aim of this study is to develop a software tool, VisuaLCSH, for effective searching, browsing, and maintenance of LCSH. This tool enables visualizing subject headings and hierarchical structures implied and embedded in LCSH. A conceptual framework for converting the hierarchical structure of headings in LCSH to an explicit tree structure is proposed, described, and implemented. The highlights of VisuaLCSH are summarized below: 1) revealing multiple aspects of a heading; 2) normalizing the hierarchical relationships in LCSH; 3) showing multi-level hierarchies in LCSH sub-trees; 4) improving the navigational function of LCSH in retrieval; and 5) enabling the implementation of generic search, i.e., the 'exploding' feature, in searching LCSH.

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Abstract

The reader of a hypertext document in a web environment, if maximum use of the document is to be obtained, must visualize the overall structure of the paths through the document as well as the document space. Graphic visualization displays of this space, produced to assist in navigation, are classified into four groups, and Heo and Hirtle compare three of these classes as to their effectiveness. Distortion displays expand regions of interest while relatively diminishing the detail of the remaining regions. This technique will show both local detail and global structure. Zoom techniques use a series of increasingly focused displays of smaller and smaller areas, and can reduce cogitative overload, but do not provide an easy movement to other parts of the total space. Expanding outline displays use a tree structure to allow movement through a hierarchy of documents, but if the organization has a wide horizontal structure, or is not particularly hierarchical in nature such display can break down. Three dimensional layouts, which are not evaluated here, place objects by location in three space, providing more information and freedom. However, the space must be represented in two dimensions resulting in difficulty in visually judging depth, size and positioning. Ten students were assigned to each of eight groups composed of viewers of the three techniques and an unassisted control group using either a large (583 selected pages) or a small (50 selected pages) web space. Sets of 10 questions, which were designed to elicit the use of a visualization tool, were provided for each space. Accuracy and time spent were extracted from a log file. Users views were also surveyed after completion. ANOVA shows significant differences in accuracy and time based upon the visualization tool in use. A Tukey test shows zoom accuracy to be significantly less than expanding outline and zoom time to be significantly greater than both the outline and control groups. Size significantly affected accuracy and time, but had no interaction with tool type. While the expanding tool class out performed zoom and distortion, its performance was not significantly different from the control group.

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Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.

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Abstract

This chapter reviews visualization techniques that can be used to map the ever-growing domain structure of scientific disciplines and to support information retrieval and classification. In contrast to the comprehensive surveys conducted in traditional fashion by Howard White and Katherine McCain (1997, 1998), this survey not only reviews emerging techniques in interactive data analysis and information visualization, but also depicts the bibliographical structure of the field itself. The chapter starts by reviewing the history of knowledge domain visualization. We then present a general process flow for the visualization of knowledge domains and explain commonly used techniques. In order to visualize the domain reviewed by this chapter, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using tutorial style, we then apply various algorithms to demonstrate the visualization effectsl produced by different approaches and compare the results. The domain visualizations reveal the relationships within and between the four fields that together constitute the focus of this chapter. We conclude with a general discussion of research possibilities. Painting a "big picture" of scientific knowledge has long been desirable for a variety of reasons. Traditional approaches are brute forcescholars must sort through mountains of literature to perceive the outlines of their field. Obviously, this is time-consuming, difficult to replicate, and entails subjective judgments. The task is enormously complex. Sifting through recently published documents to find those that will later be recognized as important is labor intensive. Traditional approaches struggle to keep up with the pace of information growth. In multidisciplinary fields of study it is especially difficult to maintain an overview of literature dynamics. Painting the big picture of an everevolving scientific discipline is akin to the situation described in the widely known Indian legend about the blind men and the elephant. As the story goes, six blind men were trying to find out what an elephant looked like. They touched different parts of the elephant and quickly jumped to their conclusions. The one touching the body said it must be like a wall; the one touching the tail said it was like a snake; the one touching the legs said it was like a tree trunk, and so forth. But science does not stand still; the steady stream of new scientific literature creates a continuously changing structure. The resulting disappearance, fusion, and emergence of research areas add another twist to the tale-it is as if the elephant is running and dynamically changing its shape. Domain visualization, an emerging field of study, is in a similar situation. Relevant literature is spread across disciplines that have traditionally had few connections. Researchers examining the domain from a particular discipline cannot possibly have an adequate understanding of the whole. As noted by White and McCain (1997), the new generation of information scientists is technically driven in its efforts to visualize scientific disciplines. However, limited progress has been made in terms of connecting pioneers' theories and practices with the potentialities of today's enabling technologies. If the difference between past and present generations lies in the power of available technologies, what they have in common is the ultimate goal-to reveal the development of scientific knowledge.

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Abstract

The concept of c-space is proposed as a visualization schema relating containers of content to cataloging surrogates and classification structures. Possible applications of keyword vector clusters within c-space could include improved retrieval rates through the use of captioning within visual hierarchies, tracings of semantic bleeding among subclasses, and access to buried knowledge within subject-neutral publication containers. The Scholastica Project is described as one example, following a tradition of research dating back to the 1980's. Preliminary focus group assessment indicates that this type of classification rendering may offer digital library searchers enriched entry strategies and an expanded range of re-entry vocabularies. Those of us who work in traditional libraries typically assume that our systems of classification: Library of Congress Classification (LCC) and Dewey Decimal Classification (DDC), are descriptive rather than prescriptive. In other words, LCC classes and subclasses approximate natural groupings of texts that reflect an underlying order of knowledge, rather than arbitrary categories prescribed by librarians to facilitate efficient shelving. Philosophical support for this assumption has traditionally been found in a number of places, from the archetypal tree of knowledge, to Aristotelian categories, to the concept of discursive formations proposed by Michel Foucault. Gary P. Radford has elegantly described an encounter with Foucault's discursive formations in the traditional library setting: "Just by looking at the titles on the spines, you can see how the books cluster together...You can identify those books that seem to form the heart of the discursive formation and those books that reside on the margins. Moving along the shelves, you see those books that tend to bleed over into other classifications and that straddle multiple discursive formations. You can physically and sensually experience...those points that feel like state borders or national boundaries, those points where one subject ends and another begins, or those magical places where one subject has morphed into another..."

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Date

22. 7.2006 16:11:05

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Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas