Search (68 results, page 1 of 4)

0.062176228 = product of:
  0.31088114 = sum of:
    0.31088114 = sum of:
      0.25428268 = weight(_text_:mind in 3406) [ClassicSimilarity], result of:
        0.25428268 = score(doc=3406,freq=4.0), product of:
          0.2607373 = queryWeight, product of:
            6.241566 = idf(docFreq=233, maxDocs=44218)
            0.04177434 = queryNorm
          0.9752447 = fieldWeight in 3406, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            6.241566 = idf(docFreq=233, maxDocs=44218)
            0.078125 = fieldNorm(doc=3406)
      0.056598466 = weight(_text_:22 in 3406) [ClassicSimilarity], result of:
        0.056598466 = score(doc=3406,freq=2.0), product of:
          0.14628662 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04177434 = queryNorm
          0.38690117 = fieldWeight in 3406, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=3406)
  0.2 = coord(1/5)

Date

30. 5.2010 16:22:35

Object

Mind Manager
Visual Mind

0.055800322 = product of:
  0.09300053 = sum of:
    0.008315044 = product of:
      0.041575223 = sum of:
        0.041575223 = weight(_text_:problem in 2595) [ClassicSimilarity], result of:
          0.041575223 = score(doc=2595,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.23447686 = fieldWeight in 2595, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2595)
      0.2 = coord(1/5)
    0.02111482 = weight(_text_:of in 2595) [ClassicSimilarity], result of:
      0.02111482 = score(doc=2595,freq=28.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32322758 = fieldWeight in 2595, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2595)
    0.06357067 = product of:
      0.12714134 = sum of:
        0.12714134 = weight(_text_:mind in 2595) [ClassicSimilarity], result of:
          0.12714134 = score(doc=2595,freq=4.0), product of:
            0.2607373 = queryWeight, product of:
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.04177434 = queryNorm
            0.48762235 = fieldWeight in 2595, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2595)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Purpose - This paper aims to explore the theory and practice of knowledge organization and its necessary connection to human perception, and shows a solution of the potential ones. Design/methodology/approach - The author attempts to survey the problem of concept-building and extension, as well as the determination of semantics in different aspects. The purpose is to find criteria for the choice of the solution that best incorporates users into the design cycles of knowledge organization systems. Findings - It is widely agreed that cognition provides the basis for concept-building; however, at the next stage of processing there is a debate. Fundamentally, what is the connection between perception and the superior cognitive processes? The perceptual method does not separate these two but rather considers them united, with perception permeating cognition. By contrast, the linguistic method considers perception as an information-receiving system. Separate from, and following, perception, the cognitive subsystems then perform information and data processing, leading to both knowledge organization and representation. We assume by that model that top-level concepts emerge from knowledge organization and representation. This paper points obvious connection of visual imagery and the internet; perceptual access of knowledge organization and information retrieval. There are some practical and characteristic solutions for the visualization of information without demand of completeness. Research limitations/implications - Librarians need to identify those semantic characteristics which stimulate a similar conceptual image both in the mind of the librarian and in the mind of the user. Originality/value - For a fresh perspective, an understanding of perception is required as well.

0.030303081 = product of:
  0.050505135 = sum of:
    0.004989027 = product of:
      0.024945134 = sum of:
        0.024945134 = weight(_text_:problem in 1197) [ClassicSimilarity], result of:
          0.024945134 = score(doc=1197,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.14068612 = fieldWeight in 1197, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1197)
      0.2 = coord(1/5)
    0.01854536 = weight(_text_:of in 1197) [ClassicSimilarity], result of:
      0.01854536 = score(doc=1197,freq=60.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.28389403 = fieldWeight in 1197, product of:
          7.745967 = tf(freq=60.0), with freq of:
            60.0 = termFreq=60.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1197)
    0.026970748 = product of:
      0.053941496 = sum of:
        0.053941496 = weight(_text_:mind in 1197) [ClassicSimilarity], result of:
          0.053941496 = score(doc=1197,freq=2.0), product of:
            0.2607373 = queryWeight, product of:
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.04177434 = queryNorm
            0.20688063 = fieldWeight in 1197, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1197)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

User interfaces for digital information discovery often require users to click around and read a lot of text in order to find the text they want to read-a process that is often frustrating and tedious. This is exacerbated because of the limited amount of text that can be displayed on a computer screen. To improve the user experience of computer mediated information discovery, information visualization techniques are applied to the digital library context, while retaining traditional information organization concepts. In this article, the "virtual (book) spine" and the virtual spine viewer are introduced. The virtual spine viewer is an application which allows users to visually explore large information spaces or collections while also allowing users to hone in on individual resources of interest. The virtual spine viewer introduced here is an alpha prototype, presented to promote discussion and further work. Information discovery changed radically with the introduction of computerized library access catalogs, the World Wide Web and its search engines, and online bookstores. Yet few instances of these technologies provide a user experience analogous to walking among well-organized, well-stocked bookshelves-which many people find useful as well as pleasurable. To put it another way, many of us have heard or voiced complaints about the paucity of "online browsing"-but what does this really mean? In traditional information spaces such as libraries, often we can move freely among the books and other resources. When we walk among organized, labeled bookshelves, we get a sense of the information space-we take in clues, perhaps unconsciously, as to the scope of the collection, the currency of resources, the frequency of their use, etc. We also enjoy unexpected discoveries such as finding an interesting resource because library staff deliberately located it near similar resources, or because it was miss-shelved, or because we saw it on a bookshelf on the way to the water fountain.
When our experience of information discovery is mediated by a computer, we neither move ourselves nor the monitor. We have only the computer's monitor to view, and the keyboard and/or mouse to manipulate what is displayed there. Computer interfaces often reduce our ability to get a sense of the contents of a library: we don't perceive the scope of the library: its breadth, (the quantity of materials/information), its density (how full the shelves are, how thorough the collection is for individual topics), or the general audience for the materials (e.g., whether the materials are appropriate for middle school students, college professors, etc.). Additionally, many computer interfaces for information discovery require users to scroll through long lists, to click numerous navigational links and to read a lot of text to find the exact text they want to read. Text features of resources are almost always presented alphabetically, and the number of items in these alphabetical lists sometimes can be very long. Alphabetical ordering is certainly an improvement over no ordering, but it generally has no bearing on features with an inherent non-alphabetical ordering (e.g., dates of historical events), nor does it necessarily group similar items together. Alphabetical ordering of resources is analogous to one of the most familiar complaints about dictionaries: sometimes you need to know how to spell a word in order to look up its correct spelling in the dictionary. Some have used technology to replicate the appearance of physical libraries, presenting rooms of bookcases and shelves of book spines in virtual 3D environments. This approach presents a problem, as few book spines can be displayed legibly on a monitor screen. This article examines the role of book spines, call numbers, and other traditional organizational and information discovery concepts, and integrates this knowledge with information visualization techniques to show how computers and monitors can meet or exceed similar information discovery methods. The goal is to tap the unique potentials of current information visualization approaches in order to improve information discovery, offer new services, and most important of all, improve user satisfaction. We need to capitalize on what computers do well while bearing in mind their limitations. The intent is to design GUIs to optimize utility and provide a positive experience for the user.

0.020879019 = product of:
  0.052197546 = sum of:
    0.021054404 = weight(_text_:of in 3884) [ClassicSimilarity], result of:
      0.021054404 = score(doc=3884,freq=174.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32230273 = fieldWeight in 3884, product of:
          13.190906 = tf(freq=174.0), with freq of:
            174.0 = termFreq=174.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.015625 = fieldNorm(doc=3884)
    0.03114314 = product of:
      0.06228628 = sum of:
        0.06228628 = weight(_text_:mind in 3884) [ClassicSimilarity], result of:
          0.06228628 = score(doc=3884,freq=6.0), product of:
            0.2607373 = queryWeight, product of:
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.04177434 = queryNorm
            0.2388852 = fieldWeight in 3884, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.015625 = fieldNorm(doc=3884)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Footnote

Rez. in: JASIST 56(2005) no.10, S.1126-1128 (P.K. Nayar): "How Images Think is an exercise both in philosophical meditation and critical theorizing about media, images, affects, and cognition. Burnett combines the insights of neuroscience with theories of cognition and the computer sciences. He argues that contemporary metaphors - biological or mechanical - about either cognition, images, or computer intelligence severely limit our understanding of the image. He suggests in his introduction that "image" refers to the "complex set of interactions that constitute everyday life in image-worlds" (p. xviii). For Burnett the fact that increasing amounts of intelligence are being programmed into technologies and devices that use images as their main form of interaction and communication-computers, for instance-suggests that images are interfaces, structuring interaction, people, and the environment they share. New technologies are not simply extensions of human abilities and needs-they literally enlarge cultural and social preconceptions of the relationship between body and mind. The flow of information today is part of a continuum, with exceptional events standing as punctuation marks. This flow connects a variety of sources, some of which are continuous - available 24 hours - or "live" and radically alters issues of memory and history. Television and the Internet, notes Burnett, are not simply a simulated world-they are the world, and the distinctions between "natural" and "non-natural" have disappeared. Increasingly, we immerse ourselves in the image, as if we are there. We rarely become conscious of the fact that we are watching images of events-for all perceptioe, cognitive, and interpretive purposes, the image is the event for us. The proximity and distance of viewer from/with the viewed has altered so significantly that the screen is us. However, this is not to suggest that we are simply passive consumers of images. As Burnett points out, painstakingly, issues of creativity are involved in the process of visualization-viewwes generate what they see in the images. This involves the historical moment of viewing-such as viewing images of the WTC bombings-and the act of re-imagining. As Burnett puts it, "the questions about what is pictured and what is real have to do with vantage points [of the viewer] and not necessarily what is in the image" (p. 26). In his second chapter Burnett moves an to a discussion of "imagescapes." Analyzing the analogue-digital programming of images, Burnett uses the concept of "reverie" to describe the viewing experience. The reverie is a "giving in" to the viewing experience, a "state" in which conscious ("I am sitting down an this sofa to watch TV") and unconscious (pleasure, pain, anxiety) processes interact. Meaning emerges in the not-always easy or "clean" process of hybridization. This "enhances" the thinking process beyond the boundaries of either image or subject. Hybridization is the space of intelligence, exchange, and communication.
Moving an to virtual images, Burnett posits the existence of "microcultures": places where people take control of the means of creation and production in order to makes sense of their social and cultural experiences. Driven by the need for community, such microcultures generate specific images as part of a cultural movement (Burnett in fact argues that microcultures make it possible for a "small cinema of twenty-five seats to become part of a cultural movement" [p. 63]), where the process of visualization-which involves an awareness of the historical moment - is central to the info-world and imagescapes presented. The computer becomms an archive, a history. The challenge is not only of preserving information, but also of extracting information. Visualization increasingly involves this process of picking a "vantage point" in order to selectively assimilate the information. In virtual reality systems, and in the digital age in general, the distance between what is being pictured and what is experienced is overcome. Images used to be treated as opaque or transparent films among experience, perception, and thought. But, now, images are taken to another level, where the viewer is immersed in the image-experience. Burnett argues-though this is hardly a fresh insight-that "interactivity is only possible when images are the raw material used by participants to change if not transform the purpose of their viewing experience" (p. 90). He suggests that a work of art, "does not start its life as an image ... it gains the status of image when it is placed into a context of viewing and visualization" (p. 90). With simulations and cyberspace the viewing experience has been changed utterly. Burnett defines simulation as "mapping different realities into images that have an environmental, cultural, and social form" (p. 95). However, the emphasis in Burnett is significant-he suggests that interactivity is not achieved through effects, but as a result of experiences attached to stories. Narrative is not merely the effect of technology-it is as much about awareness as it is about Fantasy. Heightened awareness, which is popular culture's aim at all times, and now available through head-mounted displays (HMD), also involves human emotions and the subtleties of human intuition.
The sixth chapter looks at this interfacing of humans and machines and begins with a series of questions. The crucial one, to my mind, is this: "Does the distinction between humans and technology contribute to a lack of understanding of the continuous interrelationship and interdependence that exists between humans and all of their creations?" (p. 125) Burnett suggests that to use biological or mechanical views of the computer/mind (the computer as an input/output device) Limits our understanding of the ways in which we interact with machines. He thus points to the role of language, the conversations (including the one we held with machines when we were children) that seem to suggest a wholly different kind of relationship. Peer-to-peer communication (P2P), which is arguably the most widely used exchange mode of images today, is the subject of chapter seven. The issue here is whether P2P affects community building or community destruction. Burnett argues that the trope of community can be used to explore the flow of historical events that make up a continuum-from 17th-century letter writing to e-mail. In the new media-and Burnett uses the example of popular music which can be sampled, and reedited to create new compositions - the interpretive space is more flexible. Private networks can be set up, and the process of information retrieval (about which Burnett has already expended considerable space in the early chapters) involves a lot more of visualization. P2P networks, as Burnett points out, are about information management. They are about the harmony between machines and humans, and constitute a new ecology of communications. Turning to computer games, Burnett looks at the processes of interaction, experience, and reconstruction in simulated artificial life worlds, animations, and video images. For Burnett (like Andrew Darley, 2000 and Richard Doyle, 2003) the interactivity of the new media games suggests a greater degree of engagement with imageworlds. Today many facets of looking, listening, and gazing can be turned into aesthetic forms with the new media. Digital technology literally reanimates the world, as Burnett demonstrates in bis concluding chapter. Burnett concludes that images no longer simply represent the world-they shape our very interaction with it; they become the foundation for our understanding the spaces, places, and historical moments that we inhabit. Burnett concludes his book with the suggestion that intelligence is now a distributed phenomenon (here closely paralleling Katherine Hayles' argument that subjectivity is dispersed through the cybernetic circuit, 1999). There is no one center of information or knowledge. Intersections of human creativity, work, and connectivity "spread" (Burnett's term) "intelligence through the use of mediated devices and images, as well as sounds" (p. 221).
Burnett's work is a useful basic primer an the new media. One of the chief attractions here is his clear language, devoid of the jargon of either computer sciences or advanced critical theory. This makes How Images Think an accessible introduction to digital cultures. Burnett explores the impact of the new technologies an not just image-making but an image-effects, and the ways in which images constitute our ecologies of identity, communication, and subject-hood. While some of the sections seem a little too basic (especially where he speaks about the ways in which we constitute an object as an object of art, see above), especially in the wake of reception theory, it still remains a starting point for those interested in cultural studies of the new media. The Gase Burnett makes out for the transformation of the ways in which we look at images has been strengthened by his attention to the history of this transformation-from photography through television and cinema and now to immersive virtual reality systems. Joseph Koemer (2004) has pointed out that the iconoclasm of early modern Europe actually demonstrates how idolatory was integral to the image-breakers' core belief. As Koerner puts it, "images never go away ... they persist and function by being perpetually destroyed" (p. 12). Burnett, likewise, argues that images in new media are reformed to suit new contexts of meaning-production-even when they appear to be destroyed. Images are recast, and the degree of their realism (or fantasy) heightened or diminished-but they do not "go away." Images do think, but-if I can parse Burnett's entire work-they think with, through, and in human intelligence, emotions, and intuitions. Images are uncanny-they are both us and not-us, ours and not-ours. There is, surprisingly, one factual error. Burnett claims that Myron Kreuger pioneered the term "virtual reality." To the best of my knowledge, it was Jaron Lanier who did so (see Featherstone & Burrows, 1998 [1995], p. 5)."

0.014402213 = product of:
  0.03600553 = sum of:
    0.021855915 = weight(_text_:of in 5272) [ClassicSimilarity], result of:
      0.021855915 = score(doc=5272,freq=30.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.33457235 = fieldWeight in 5272, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5272)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 5272) [ClassicSimilarity], result of:
          0.028299233 = score(doc=5272,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 5272, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5272)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature - an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

Date

22. 7.2006 16:11:05

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.359-377

0.013423842 = product of:
  0.033559605 = sum of:
    0.016630089 = product of:
      0.08315045 = sum of:
        0.08315045 = weight(_text_:problem in 1463) [ClassicSimilarity], result of:
          0.08315045 = score(doc=1463,freq=8.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.46895373 = fieldWeight in 1463, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1463)
      0.2 = coord(1/5)
    0.016929517 = weight(_text_:of in 1463) [ClassicSimilarity], result of:
      0.016929517 = score(doc=1463,freq=18.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.25915858 = fieldWeight in 1463, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1463)
  0.4 = coord(2/5)

Abstract

The use of graphical representations is very common in information technology and engineering. Although these same tools could be applied effectively in other areas, they are not used because they are hardly known or are completely unheard of. This article aims to discuss the results of the experimentation carried out on graphical approaches to knowledge representation during research, analysis and problem-solving in the health care sector. The experimentation was carried out on conceptual mapping and Petri Nets, developed collaboratively online with the aid of the CMapTool and WoPeD graphic applications. Two distinct professional communities have been involved in the research, both pertaining to the Local Health Units in Tuscany. One community is made up of head physicians and health care managers whilst the other is formed by technical staff from the Department of Nutrition and Food Hygiene. It emerged from the experimentation that concept maps arc considered more effective in analyzing knowledge domain related to the problem to be faced (description of what it is). On the other hand, Petri Nets arc more effective in studying and formalizing its possible solutions (description of what to do to). For the same reason, those involved in the experimentation have proposed the complementary rather than alternative use of the two knowledge representation methods as a support for professional problem-solving.

0.012848704 = product of:
  0.03212176 = sum of:
    0.015142222 = weight(_text_:of in 1289) [ClassicSimilarity], result of:
      0.015142222 = score(doc=1289,freq=10.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.23179851 = fieldWeight in 1289, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1289)
    0.016979538 = product of:
      0.033959076 = sum of:
        0.033959076 = weight(_text_:22 in 1289) [ClassicSimilarity], result of:
          0.033959076 = score(doc=1289,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.23214069 = fieldWeight in 1289, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1289)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

QVIZ will research and create a framework for visualizing and querying archival resources by a time-space interface based on maps and emergent knowledge structures. The framework will also integrate social software, such as wikis, in order to utilize knowledge in existing and new communities of practice. QVIZ will lead to improved information sharing and knowledge creation, easier access to information in a user-adapted context and innovative ways of exploring and visualizing materials over time, between countries and other administrative units. The common European framework for sharing and accessing archival information provided by the QVIZ project will open a considerably larger commercial market based on archival materials as well as a richer understanding of European history.

Content

Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".

0.012797958 = product of:
  0.031994894 = sum of:
    0.017845279 = weight(_text_:of in 1397) [ClassicSimilarity], result of:
      0.017845279 = score(doc=1397,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 1397, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1397)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 1397) [ClassicSimilarity], result of:
          0.028299233 = score(doc=1397,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 1397, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1397)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

The "Screen Design Manual" provides designers of interactive media with a practical working guide for preparing and presenting information that is suitable for both their target groups and the media they are using. It describes background information and relationships, clarifies them with the help of examples, and encourages further development of the language of digital media. In addition to the basics of the psychology of perception and learning, ergonomics, communication theory, imagery research, and aesthetics, the book also explores the design of navigation and orientation elements. Guidelines and checklists, along with the unique presentation of the book, support the application of information in practice.

Content

From the contents:.- Basics of screen design.- Navigation and orientation.- Information.- Screen layout.Interaction.- Motivation.- Innovative prospects.- Appendix.Glossary.- Literature.- Index

Date

22. 3.2008 14:29:25

0.012189303 = product of:
  0.030473256 = sum of:
    0.019153563 = weight(_text_:of in 2659) [ClassicSimilarity], result of:
      0.019153563 = score(doc=2659,freq=36.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2932045 = fieldWeight in 2659, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=2659)
    0.011319693 = product of:
      0.022639386 = sum of:
        0.022639386 = weight(_text_:22 in 2659) [ClassicSimilarity], result of:
          0.022639386 = score(doc=2659,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.15476047 = fieldWeight in 2659, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=2659)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

The Library of Congress Subject Headings (LCSH) has been developed over the course of more than a century, predating the semantic web by some time. Until the 1986, the only concept-toconcept relationship available was an undifferentiated "See Also" reference, which was used for both associative (RT) and hierarchical (BT/NT) connections. In that year, in preparation for the first release of the headings in machine readable MARC Authorities form, an attempt was made to automatically convert these "See Also" links into the standardized thesaural relations. Unfortunately, the rule used to determine the type of reference to generate relied on the presence of symmetric links to detect associatively related terms; "See Also" references that were only present in one of the related terms were assumed to be hierarchical. This left the process vulnerable to inconsistent use of references in the pre-conversion data, with a marked bias towards promoting relationships to hierarchical status. The Library of Congress was aware that the results of the conversion contained many inconsistencies, and intended to validate and correct the results over the course of time. Unfortunately, twenty years later, less than 40% of the converted records have been evaluated. The converted records, being the earliest encountered during the Library's cataloging activities, represent the most basic concepts within LCSH; errors in the syndetic structure for these records affect far more subordinate concepts than those nearer the periphery. Worse, a policy of patterning new headings after pre-existing ones leads to structural errors arising from the conversion process being replicated in these newer headings, perpetuating and exacerbating the errors. As the LCSH prepares for its second great conversion, from MARC to SKOS, it is critical to address these structural problems. As part of the work on converting the headings into SKOS, I have experimented with different visualizations of the tangled web of broader terms embedded in LCSH. This poster illustrates several of these renderings, shows how they can help users to judge which relationships might not be correct, and shows just exactly how Doorbells and Mammals are related.

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

0.011676142 = product of:
  0.029190354 = sum of:
    0.005820531 = product of:
      0.029102655 = sum of:
        0.029102655 = weight(_text_:problem in 4276) [ClassicSimilarity], result of:
          0.029102655 = score(doc=4276,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.1641338 = fieldWeight in 4276, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4276)
      0.2 = coord(1/5)
    0.023369823 = weight(_text_:of in 4276) [ClassicSimilarity], result of:
      0.023369823 = score(doc=4276,freq=70.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.35774738 = fieldWeight in 4276, product of:
          8.3666 = tf(freq=70.0), with freq of:
            70.0 = termFreq=70.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4276)
  0.4 = coord(2/5)

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.
Visualization can be classified as scientific visualization, software visualization, or information visualization. Although the data differ, the underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues to deliver patterns. They all involve understanding human perception (Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990). Because most decisions are based an unstructured information, such as text documents, Web pages, or e-mail messages, this chapter focuses an the visualization of unstructured textual documents. The chapter reviews information visualization techniques developed over the last decade and examines how they have been applied in different domains. The first section provides the background by describing visualization history and giving overviews of scientific, software, and information visualization as well as the perceptual aspects of visualization. The next section assesses important visualization techniques that convert abstract information into visual objects and facilitate navigation through displays an a computer screen. It also explores information analysis algorithms that can be applied to identify or extract salient visualizable structures from collections of information. Information visualization systems that integrate different types of technologies to address problems in different domains are then surveyed; and we move an to a survey and critique of visualization system evaluation studies. The chapter concludes with a summary and identification of future research directions.

Source

Annual review of information science and technology. 39(2005), S.139-177

0.010464129 = product of:
  0.026160322 = sum of:
    0.008315044 = product of:
      0.041575223 = sum of:
        0.041575223 = weight(_text_:problem in 2596) [ClassicSimilarity], result of:
          0.041575223 = score(doc=2596,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.23447686 = fieldWeight in 2596, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2596)
      0.2 = coord(1/5)
    0.017845279 = weight(_text_:of in 2596) [ClassicSimilarity], result of:
      0.017845279 = score(doc=2596,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 2596, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2596)
  0.4 = coord(2/5)

Abstract

Purpose - To describe how information visualization can be used in the design of interface tools for large-scale repositories. Design/methodology/approach - One challenge for designers in the context of large-scale repositories is to create interface tools that help users find specific information of interest. In order to be most effective, these tools need to leverage the cognitive characteristics of the target users. At the Los Alamos National Laboratory, the authors' target users are scientists and engineers who can be characterized as higher-order, analytical thinkers. In this paper, the authors describe a visualization tool they have created for making the authors' large-scale digital object repositories more usable for them: SearchGraph, which facilitates data set analysis by displaying search results in the form of a two- or three-dimensional interactive scatter plot. Findings - Using SearchGraph, users can view a condensed, abstract visualization of search results. They can view the same dataset from multiple perspectives by manipulating several display, sort, and filter options. Doing so allows them to see different patterns in the dataset. For example, they can apply a logarithmic transformation in order to create more scatter in a dense cluster of data points or they can apply filters in order to focus on a specific subset of data points. Originality/value - SearchGraph is a creative solution to the problem of how to design interface tools for large-scale repositories. It is particularly appropriate for the authors' target users, who are scientists and engineers. It extends the work of the first two authors on ActiveGraph, a read-write digital library visualization tool.

0.010083349 = product of:
  0.025208373 = sum of:
    0.019548526 = weight(_text_:of in 1789) [ClassicSimilarity], result of:
      0.019548526 = score(doc=1789,freq=150.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2992506 = fieldWeight in 1789, product of:
          12.247449 = tf(freq=150.0), with freq of:
            150.0 = termFreq=150.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.0056598466 = product of:
      0.011319693 = sum of:
        0.011319693 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
          0.011319693 = score(doc=1789,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.07738023 = fieldWeight in 1789, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.015625 = fieldNorm(doc=1789)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

0.010048111 = product of:
  0.025120277 = sum of:
    0.009978054 = product of:
      0.04989027 = sum of:
        0.04989027 = weight(_text_:problem in 1056) [ClassicSimilarity], result of:
          0.04989027 = score(doc=1056,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.28137225 = fieldWeight in 1056, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=1056)
      0.2 = coord(1/5)
    0.015142222 = weight(_text_:of in 1056) [ClassicSimilarity], result of:
      0.015142222 = score(doc=1056,freq=10.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.23179851 = fieldWeight in 1056, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1056)
  0.4 = coord(2/5)

Abstract

The authors introduce an information visualization model, WebStar, for hyperlink-based information systems. Hyperlinks within a hyperlink-based document can be visualized in a two-dimensional visual space. All links are projected within a display sphere in the visual space. The relationship between a specified central document and its hyperlinked documents is visually presented in the visual space. In addition, users are able to define a group of subjects and to observe relevance between each subject and all hyperlinked documents via movement of that subject around the display sphere center. WebStar allows users to dynamically change an interest center during navigation. A retrieval mechanism is developed to control retrieved results in the visual space. Impact of movement of a subject on the visual document distribution is analyzed. An ambiguity problem caused by projection is discussed. Potential applications of this visualization model in information retrieval are included. Future research directions on the topic are addressed.

0.009988829 = product of:
  0.02497207 = sum of:
    0.0070555503 = product of:
      0.03527775 = sum of:
        0.03527775 = weight(_text_:problem in 1202) [ClassicSimilarity], result of:
          0.03527775 = score(doc=1202,freq=4.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.19896023 = fieldWeight in 1202, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1202)
      0.2 = coord(1/5)
    0.01791652 = weight(_text_:of in 1202) [ClassicSimilarity], result of:
      0.01791652 = score(doc=1202,freq=56.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2742677 = fieldWeight in 1202, product of:
          7.483315 = tf(freq=56.0), with freq of:
            56.0 = termFreq=56.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1202)
  0.4 = coord(2/5)

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..."
But what happens to this awareness in a digital library? Can discursive formations be represented in cyberspace, perhaps through diagrams in a visualization interface? And would such a schema be helpful to a digital library user? To approach this question, it is worth taking a moment to reconsider what Radford is looking at. First, he looks at titles to see how the books cluster. To illustrate, I scanned one hundred books on the shelves of a college library under subclass HT 101-395, defined by the LCC subclass caption as Urban groups. The City. Urban sociology. Of the first 100 titles in this sequence, fifty included the word "urban" or variants (e.g. "urbanization"). Another thirty-five used the word "city" or variants. These keywords appear to mark their titles as the heart of this discursive formation. The scattering of titles not using "urban" or "city" used related terms such as "town," "community," or in one case "skyscrapers." So we immediately see some empirical correlation between keywords and classification. But we also see a problem with the commonly used search technique of title-keyword. A student interested in urban studies will want to know about this entire subclass, and may wish to browse every title available therein. A title-keyword search on "urban" will retrieve only half of the titles, while a search on "city" will retrieve just over a third. There will be no overlap, since no titles in this sample contain both words. The only place where both words appear in a common string is in the LCC subclass caption, but captions are not typically indexed in library Online Public Access Catalogs (OPACs). In a traditional library, this problem is mitigated when the student goes to the shelf looking for any one of the books and suddenly discovers a much wider selection than the keyword search had led him to expect. But in a digital library, the issue of non-retrieval can be more problematic, as studies have indicated. Micco and Popp reported that, in a study funded partly by the U.S. Department of Education, 65 of 73 unskilled users searching for material on U.S./Soviet foreign relations found some material but never realized they had missed a large percentage of what was in the database.

0.008916343 = product of:
  0.022290856 = sum of:
    0.0066520358 = product of:
      0.033260178 = sum of:
        0.033260178 = weight(_text_:problem in 2643) [ClassicSimilarity], result of:
          0.033260178 = score(doc=2643,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.1875815 = fieldWeight in 2643, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03125 = fieldNorm(doc=2643)
      0.2 = coord(1/5)
    0.01563882 = weight(_text_:of in 2643) [ClassicSimilarity], result of:
      0.01563882 = score(doc=2643,freq=24.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.23940048 = fieldWeight in 2643, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=2643)
  0.4 = coord(2/5)

Abstract

Selection of search terms in an online search environment can be facilitated by the visual display of a knowledge map showing the various concepts and their links. This paper reports an a preliminary research aimed at designing a prototype knowledge map using DDC and its visual display. The prototype knowledge map created using the Protégé and TGViz freeware has been demonstrated, and further areas of research in this field are discussed.

Content

1. Introduction Web search engines and digital libraries usually expect the users to use search terms that most accurately represent their information needs. Finding the most appropriate search terms to represent an information need is an age old problem in information retrieval. Keyword or phrase search may produce good search results as long as the search terms or phrase(s) match those used by the authors and have been chosen for indexing by the concerned information retrieval system. Since this does not always happen, a large number of false drops are produced by information retrieval systems. The retrieval results become worse in very large systems that deal with millions of records, such as the Web search engines and digital libraries. Vocabulary control tools are used to improve the performance of text retrieval systems. Thesauri, the most common type of vocabulary control tool used in information retrieval, appeared in the late fifties, designed for use with the emerging post-coordinate indexing systems of that time. They are used to exert terminology control in indexing, and to aid in searching by allowing the searcher to select appropriate search terms. A large volume of literature exists describing the design features, and experiments with the use, of thesauri in various types of information retrieval systems (see for example, Furnas et.al., 1987; Bates, 1986, 1998; Milstead, 1997, and Shiri et al., 2002).

Source

Knowledge organization and the global information society: Proceedings of the 8th International ISKO Conference 13-16 July 2004, London, UK. Ed.: I.C. McIlwaine

0.008635346 = product of:
  0.021588365 = sum of:
    0.0072010416 = product of:
      0.036005206 = sum of:
        0.036005206 = weight(_text_:problem in 1211) [ClassicSimilarity], result of:
          0.036005206 = score(doc=1211,freq=6.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.20306295 = fieldWeight in 1211, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.2 = coord(1/5)
    0.0143873235 = weight(_text_:of in 1211) [ClassicSimilarity], result of:
      0.0143873235 = score(doc=1211,freq=52.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.22024246 = fieldWeight in 1211, product of:
          7.2111025 = tf(freq=52.0), with freq of:
            52.0 = termFreq=52.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.4 = coord(2/5)

Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.

Content

The JAVA applet is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. A prototype of this interface has been developed and is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. The D-Lib search interface is available at <http://www.dlib.org/Architext/AT-dlib2query.html>.

0.0049966783 = product of:
  0.024983391 = sum of:
    0.024983391 = weight(_text_:of in 1313) [ClassicSimilarity], result of:
      0.024983391 = score(doc=1313,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.38244802 = fieldWeight in 1313, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1313)
  0.2 = coord(1/5)

Abstract

This study proposes a new methodology that allows for the generation of scientograms of major scientific domains, constructed on the basis of cocitation of Institute of Scientific Information categories, and pruned using PathfinderNetwork, with a layout determined by algorithms of the spring-embedder type (Kamada-Kawai), then corroborated structurally by factor analysis. We present the complete scientogram of the world for the Year 2002. It integrates the natural sciences, the social sciences, and arts and humanities. Its basic structure and the essential relationships therein are revealed, allowing us to simultaneously analyze the macrostructure, microstructure, and marrow of worldwide scientific output.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.14, S.2167-2179

0.0049966783 = product of:
  0.024983391 = sum of:
    0.024983391 = weight(_text_:of in 3659) [ClassicSimilarity], result of:
      0.024983391 = score(doc=3659,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.38244802 = fieldWeight in 3659, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3659)
  0.2 = coord(1/5)

Abstract

This paper seeks to provide a brief overview of those developments which have taken the theory and practice of image and video retrieval into the digital age. Drawing on a voluminous literature, the context in which visual information retrieval takes place is followed by a consideration of the conceptual and practical challenges posed by the representation and recovery of visual material on the basis of its semantic content. An historical account of research endeavours in content-based retrieval, directed towards the automation of these operations in digital image scenarios, provides the main thrust of the paper. Finally, a look forwards locates visual information retrieval research within the wider context of content-based multimedia retrieval.

0.004691646 = product of:
  0.02345823 = sum of:
    0.02345823 = weight(_text_:of in 1718) [ClassicSimilarity], result of:
      0.02345823 = score(doc=1718,freq=24.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.3591007 = fieldWeight in 1718, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1718)
  0.2 = coord(1/5)

Abstract

This review describes experimental designs (users, search tasks, measures, etc.) used by 31 controlled user studies of information visualization (IV) tools for textual information retrieval (IR) and a meta-analysis of the reported statistical effects. Comparable experimental designs allow research designers to compare their results with other reports, and support the development of experimentally verified design guidelines concerning which IV techniques are better suited to which types of IR tasks. The studies generally use a within-subject design with 15 or more undergraduate students performing browsing to known-item tasks on sets of at least 1,000 full-text articles or Web pages on topics of general interest/news. Results of the meta-analysis (N = 8) showed no significant effects of the IV tool as compared with a text-only equivalent, but the set shows great variability suggesting an inadequate basis of comparison. Experimental design recommendations are provided which would support comparison of existing IV tools for IR usability testing.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.6, S.1012-1024

0.0045145387 = product of:
  0.022572692 = sum of:
    0.022572692 = weight(_text_:of in 82) [ClassicSimilarity], result of:
      0.022572692 = score(doc=82,freq=32.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.34554482 = fieldWeight in 82, product of:
          5.656854 = tf(freq=32.0), with freq of:
            32.0 = termFreq=32.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=82)
  0.2 = coord(1/5)

Abstract

Aggregated journal-journal citation networks based on the Journal Citation Reports 2004 of the Science Citation Index (5,968 journals) and the Social Science Citation Index (1,712 journals) are made accessible from the perspective of any of these journals. A vector-space model Is used for normalization, and the results are brought online at http://www.leydesdorff.net/jcr04 as input files for the visualization program Pajek. The user is thus able to analyze the citation environment in terms of links and graphs. Furthermore, the local impact of a journal is defined as its share of the total citations in the specific journal's citation environments; the vertical size of the nodes is varied proportionally to this citation impact. The horizontal size of each node can be used to provide the same information after correction for within-journal (self-)citations. In the "citing" environment, the equivalents of this measure can be considered as a citation activity index which maps how the relevant journal environment is perceived by the collective of authors of a given journal. As a policy application, the mechanism of Interdisciplinary developments among the sciences is elaborated for the case of nanotechnology journals.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.1, S.25-38