Search (10 results, page 1 of 1)

0.06315294 = product of:
  0.09472941 = sum of:
    0.044042703 = weight(_text_:wide in 79) [ClassicSimilarity], result of:
      0.044042703 = score(doc=79,freq=2.0), product of:
        0.22492146 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.050763648 = queryNorm
        0.1958137 = fieldWeight in 79, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.03125 = fieldNorm(doc=79)
    0.05068671 = product of:
      0.10137342 = sum of:
        0.10137342 = weight(_text_:web in 79) [ClassicSimilarity], result of:
          0.10137342 = score(doc=79,freq=36.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.6119082 = fieldWeight in 79, product of:
              6.0 = tf(freq=36.0), with freq of:
                36.0 = termFreq=36.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=79)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

With the terrific growth of data volume and data being produced every second on millions of devices across the globe, there is a desperate need to manage the unstructured data available on web pages efficiently. Semantic Web or also known as Web of Trust structures the scattered data on the Internet according to the needs of the user. It is an extension of the World Wide Web (WWW) which focuses on manipulating web data on behalf of Humans. Due to the ability of the Semantic Web to integrate data from disparate sources and hence makes it more user-friendly, it is an emerging trend. Tim Berners-Lee first introduced the term Semantic Web and since then it has come a long way to become a more intelligent and intuitive web. Data Visualization plays an essential role in explaining complex concepts in a universal manner through pictorial representation, and the Semantic Web helps in broadening the potential of Data Visualization and thus making it an appropriate combination. The objective of this chapter is to provide fundamental insights concerning the semantic web technologies and in addition to that it also elucidates the issues as well as the solutions regarding the semantic web. The purpose of this chapter is to highlight the semantic web architecture in detail while also comparing it with the traditional search system. It classifies the semantic web architecture into three major pillars i.e. RDF, Ontology, and XML. Moreover, it describes different semantic web tools used in the framework and technology. It attempts to illustrate different approaches of the semantic web search engines. Besides stating numerous challenges faced by the semantic web it also illustrates the solutions.

Theme

Semantic Web

0.027994838 = product of:
  0.041992255 = sum of:
    0.033032026 = weight(_text_:wide in 1197) [ClassicSimilarity], result of:
      0.033032026 = score(doc=1197,freq=2.0), product of:
        0.22492146 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.050763648 = queryNorm
        0.14686027 = fieldWeight in 1197, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1197)
    0.008960229 = product of:
      0.017920459 = sum of:
        0.017920459 = weight(_text_:web in 1197) [ClassicSimilarity], result of:
          0.017920459 = score(doc=1197,freq=2.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.108171105 = fieldWeight in 1197, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1197)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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.

0.023329033 = product of:
  0.034993548 = sum of:
    0.027526692 = weight(_text_:wide in 1211) [ClassicSimilarity], result of:
      0.027526692 = score(doc=1211,freq=2.0), product of:
        0.22492146 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.050763648 = queryNorm
        0.122383565 = fieldWeight in 1211, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
    0.007466858 = product of:
      0.014933716 = sum of:
        0.014933716 = weight(_text_:web in 1211) [ClassicSimilarity], result of:
          0.014933716 = score(doc=1211,freq=2.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.09014259 = fieldWeight in 1211, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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.

0.018351128 = product of:
  0.055053383 = sum of:
    0.055053383 = weight(_text_:wide in 3888) [ClassicSimilarity], result of:
      0.055053383 = score(doc=3888,freq=2.0), product of:
        0.22492146 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.050763648 = queryNorm
        0.24476713 = fieldWeight in 3888, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3888)
  0.33333334 = coord(1/3)

Abstract

We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.

0.006877774 = product of:
  0.020633321 = sum of:
    0.020633321 = product of:
      0.041266643 = sum of:
        0.041266643 = weight(_text_:22 in 1289) [ClassicSimilarity], result of:
          0.041266643 = score(doc=1289,freq=2.0), product of:
            0.17776565 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050763648 = 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)
  0.33333334 = coord(1/3)

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".

0.005973486 = product of:
  0.017920459 = sum of:
    0.017920459 = product of:
      0.035840917 = sum of:
        0.035840917 = weight(_text_:web in 2906) [ClassicSimilarity], result of:
          0.035840917 = score(doc=2906,freq=2.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.21634221 = fieldWeight in 2906, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=2906)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The use of research impact metrics and analytics has become an integral component to many aspects of institutional assessment. Many platforms currently exist to provide such analytics, both proprietary and open source; however, the functionality of these systems may not always overlap to serve uniquely specific needs. In this paper, I describe a novel web-based platform, named Manifold, that I built to serve custom research impact assessment needs in the University of Minnesota Medical School. Built on a standard LAMP architecture, Manifold automatically pulls publication data for faculty from Scopus through APIs, calculates impact metrics through automated analytics, and dynamically generates report-like profiles that visualize those metrics. Work on this project has resulted in many lessons learned about challenges to sustainability and scalability in developing a system of such magnitude.

0.005973486 = product of:
  0.017920459 = sum of:
    0.017920459 = product of:
      0.035840917 = sum of:
        0.035840917 = weight(_text_:web in 3205) [ClassicSimilarity], result of:
          0.035840917 = score(doc=3205,freq=2.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.21634221 = fieldWeight in 3205, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=3205)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The goal of Open Knowledge Maps is to create a visual interface to the world's scientific knowledge. The base for this visual interface consists of so-called knowledge maps, which enable the exploration of existing knowledge and the discovery of new knowledge. Our open source knowledge mapping software applies a mixture of summarization techniques and similarity measures on article metadata, which are iteratively chained together. After processing, the representation is saved in a database for use in a web visualization. In the future, we want to create a space for collective knowledge mapping that brings together individuals and communities involved in exploration and discovery. We want to enable people to guide each other in their discovery by collaboratively annotating and modifying the automatically created maps.

0.0056318576 = product of:
  0.016895572 = sum of:
    0.016895572 = product of:
      0.033791143 = sum of:
        0.033791143 = weight(_text_:web in 4746) [ClassicSimilarity], result of:
          0.033791143 = score(doc=4746,freq=4.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.2039694 = fieldWeight in 4746, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=4746)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable.

0.004977905 = product of:
  0.014933716 = sum of:
    0.014933716 = product of:
      0.029867431 = sum of:
        0.029867431 = weight(_text_:web in 4837) [ClassicSimilarity], result of:
          0.029867431 = score(doc=4837,freq=2.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.18028519 = fieldWeight in 4837, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4837)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Icon system could represent an efficient solution for collective iconic categorization of knowledge by providing graphical interpretation. Their pictorial characters assist visualizing the structure of text to become more understandable beyond vocabulary obstacle. In this paper we are proposing a Knowledge Engineering (KM) based iconic representation approach. We assume that these systematic icons improve collective knowledge management. Meanwhile, text (constructed under our knowledge management model - Hypertopic) helps to reduce the diversity of graphical understanding belonging to different users. This "position paper" also prepares to demonstrate our hypothesis by an "iconic social tagging" experiment which is to be accomplished in 2011 with UTT students. We describe the "socio semantic web" information portal involved in this project, and a part of the icons already designed for this experiment in Sustainability field. We have reviewed existing theoretical works on icons from various origins, which can be used to lay the foundation of robust "icons systems".

0.003438887 = product of:
  0.010316661 = sum of:
    0.010316661 = product of:
      0.020633321 = sum of:
        0.020633321 = weight(_text_:22 in 3035) [ClassicSimilarity], result of:
          0.020633321 = score(doc=3035,freq=2.0), product of:
            0.17776565 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050763648 = queryNorm
            0.116070345 = fieldWeight in 3035, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0234375 = fieldNorm(doc=3035)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Content

As the team describe in a paper posted (http://arxiv.org/abs/1605.04951) on arXiv, they found that figures did indeed matter-but not all in the same way. An average paper in PubMed Central has about one diagram for every three pages and gets 1.67 citations. Papers with more diagrams per page and, to a lesser extent, plots per page tended to be more influential (on average, a paper accrued two more citations for every extra diagram per page, and one more for every extra plot per page). By contrast, including photographs and equations seemed to decrease the chances of a paper being cited by others. That agrees with a study from 2012, whose authors counted (by hand) the number of mathematical expressions in over 600 biology papers and found that each additional equation per page reduced the number of citations a paper received by 22%. This does not mean that researchers should rush to include more diagrams in their next paper. Dr Howe has not shown what is behind the effect, which may merely be one of correlation, rather than causation. It could, for example, be that papers with lots of diagrams tend to be those that illustrate new concepts, and thus start a whole new field of inquiry. Such papers will certainly be cited a lot. On the other hand, the presence of equations really might reduce citations. Biologists (as are most of those who write and read the papers in PubMed Central) are notoriously mathsaverse. If that is the case, looking in a physics archive would probably produce a different result.