Search (107 results, page 1 of 6)

0.040320244 = product of:
  0.060480364 = sum of:
    0.026672786 = weight(_text_:on in 2755) [ClassicSimilarity], result of:
      0.026672786 = score(doc=2755,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 2755, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.078125 = fieldNorm(doc=2755)
    0.03380758 = product of:
      0.06761516 = sum of:
        0.06761516 = weight(_text_:22 in 2755) [ClassicSimilarity], result of:
          0.06761516 = score(doc=2755,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.38690117 = fieldWeight in 2755, 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=2755)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Date

1. 2.2016 18:25:22

Source

Semantic keyword-based search on structured data sources: First COST Action IC1302 International KEYSTONE Conference, IKC 2015, Coimbra, Portugal, September 8-9, 2015. Revised Selected Papers. Eds.: J. Cardoso et al

0.028611436 = product of:
  0.042917155 = sum of:
    0.02263261 = weight(_text_:on in 1289) [ClassicSimilarity], result of:
      0.02263261 = score(doc=1289,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.20619515 = fieldWeight in 1289, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=1289)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 1289) [ClassicSimilarity], result of:
          0.040569093 = score(doc=1289,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
  0.6666667 = coord(2/3)

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".

0.023842867 = product of:
  0.0357643 = sum of:
    0.01886051 = weight(_text_:on in 4641) [ClassicSimilarity], result of:
      0.01886051 = score(doc=4641,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.1718293 = fieldWeight in 4641, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4641)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 4641) [ClassicSimilarity], result of:
          0.03380758 = score(doc=4641,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 4641, 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=4641)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Studies on the state and structure of digital knowledge concerning science generally relate to macro and meso scales. Supported by visualizations, these studies can deliver knowledge about emerging scientific fields or collaboration between countries, scientific centers, or groups of researchers. Analyses of individual activities or single scientific career paths are rarely presented and discussed. The authors decided to fill this gap and developed a web application for visualizing the scientific output of particular researchers. This free software based on bibliographic data from local databases, provides six layouts for analysis. Researchers can see the dynamic characteristics of their own writing activity, the time and place of publication, and the thematic scope of research problems. They can also identify cooperation networks, and consequently, study the dependencies and regularities in their own scientific activity. The current article presents the results of a study of the application's usability and functionality as well as attempts to define different user groups. A survey about the interface was sent to select researchers employed at Nicolaus Copernicus University. The results were used to answer the question as to whether such a specialized visualization tool can significantly augment the individual information space of the contemporary researcher.

Date

21.12.2018 17:22:13

0.021334989 = product of:
  0.032002483 = sum of:
    0.01847945 = weight(_text_:on in 2659) [ClassicSimilarity], result of:
      0.01847945 = score(doc=2659,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.16835764 = fieldWeight in 2659, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=2659)
    0.013523032 = product of:
      0.027046064 = sum of:
        0.027046064 = weight(_text_:22 in 2659) [ClassicSimilarity], result of:
          0.027046064 = score(doc=2659,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
  0.6666667 = coord(2/3)

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

0.021334989 = product of:
  0.032002483 = sum of:
    0.01847945 = weight(_text_:on in 1444) [ClassicSimilarity], result of:
      0.01847945 = score(doc=1444,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.16835764 = fieldWeight in 1444, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=1444)
    0.013523032 = product of:
      0.027046064 = sum of:
        0.027046064 = weight(_text_:22 in 1444) [ClassicSimilarity], result of:
          0.027046064 = score(doc=1444,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.15476047 = fieldWeight in 1444, 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=1444)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Acquiring knowledge in any field involves information retrieval, i.e. searching the available documents to identify answers to the queries concerning the selected objects. Knowing the keywords which are names of the objects will enable situating the user's query in the information space organized as a thesaurus or faceted classification. Objectives: Identification the areas in the information space which correspond to gaps in the user's personal knowledge or in the domain knowledge might become useful in theory or practice. The aim of this paper is to present a realistic information-space model of a self-authored full-text document on information culture, indexed by the author of this article. Methodology: Having established the relations between the terms, particular modules (sets of terms connected by relations used in facet classification) are situated on a plain, similarly to a communication map. Conclusions drawn from the "journey" on the map, which is a visualization of the knowledge contained in the analysed document, are the crucial part of this paper. Results: The direct result of the research is the created model of information space visualization of a given document (book, article, website). The proposed procedure can practically be used as a new form of representation in order to map the contents of academic books and articles, beside the traditional index form, especially as an e-book auxiliary tool. In teaching, visualization of the information space of a document can be used to help students understand the issues of: classification, categorization and representation of new knowledge emerging in human mind.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.020160122 = product of:
  0.030240182 = sum of:
    0.013336393 = weight(_text_:on in 4573) [ClassicSimilarity], result of:
      0.013336393 = score(doc=4573,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 4573, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4573)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 4573) [ClassicSimilarity], result of:
          0.03380758 = score(doc=4573,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 4573, 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=4573)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Date

9.12.2018 16:22:25

0.018689949 = product of:
  0.028034922 = sum of:
    0.01789265 = weight(_text_:on in 3035) [ClassicSimilarity], result of:
      0.01789265 = score(doc=3035,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.16301158 = fieldWeight in 3035, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3035)
    0.010142273 = product of:
      0.020284547 = sum of:
        0.020284547 = weight(_text_:22 in 3035) [ClassicSimilarity], result of:
          0.020284547 = score(doc=3035,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
  0.6666667 = coord(2/3)

Content

Bill Howe and his colleagues at the University of Washington, in Seattle, decided to find out. First, they trained a computer algorithm to distinguish between various sorts of figures-which they defined as diagrams, equations, photographs, plots (such as bar charts and scatter graphs) and tables. They exposed their algorithm to between 400 and 600 images of each of these types of figure until it could distinguish them with an accuracy greater than 90%. Then they set it loose on the more-than-650,000 papers (containing more than 10m figures) stored on PubMed Central, an online archive of biomedical-research articles. To measure each paper's influence, they calculated its article-level Eigenfactor score-a modified version of the PageRank algorithm Google uses to provide the most relevant results for internet searches. Eigenfactor scoring gives a better measure than simply noting the number of times a paper is cited elsewhere, because it weights citations by their influence. A citation in a paper that is itself highly cited is worth more than one in a paper that is not.
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.

0.017875835 = product of:
  0.053627502 = sum of:
    0.053627502 = product of:
      0.107255004 = sum of:
        0.107255004 = weight(_text_:demand in 2595) [ClassicSimilarity], result of:
          0.107255004 = score(doc=2595,freq=2.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.3445655 = fieldWeight in 2595, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2595)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

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.

0.011269193 = product of:
  0.03380758 = sum of:
    0.03380758 = product of:
      0.06761516 = sum of:
        0.06761516 = weight(_text_:22 in 3406) [ClassicSimilarity], result of:
          0.06761516 = score(doc=3406,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

30. 5.2010 16:22:35

0.010725501 = product of:
  0.032176502 = sum of:
    0.032176502 = product of:
      0.064353004 = sum of:
        0.064353004 = weight(_text_:demand in 4268) [ClassicSimilarity], result of:
          0.064353004 = score(doc=4268,freq=2.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.2067393 = fieldWeight in 4268, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.0234375 = fieldNorm(doc=4268)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Computers have become our companions in many of the activities we pursue in our life. They assist us, in particular, in searching relevant information that is needed to perform a variety of tasks, from professional usage to personal entertainment. They hold this information in a huge number of heterogeneous sources, either dedicated to a specific user community (e.g., enterprise databases) or maintained for the general public (e.g., websites and digital libraries). Whereas progress in basic information technology is nowadays capable of guaranteeing effective information management, information retrieval and dissemination has become a core issue that needs further accomplishments to achieve user satisfaction. The research communities in databases, information retrieval, information visualization, and human-computer interaction have already largely investigated these domains. However, the technical environment has so dramatically evolved in recent years, inducing a parallel and very significant evolution in user habits and expectations, that new approaches are definitely needed to meet current demand. One of the most evident and significant changes is the human-computer interaction paradigm. Traditional interactions relayed an programming to express user information requirements in formal code and an textual output to convey to users the information extracted by the system. Except for professional data-intensive application frameworks, still in the hands of computer speciahsts, we have basically moved away from this pattern both in terms of expressing information requests and conveying results. The new goal is direct interaction with the final user (the person who is looking for information and is not necessarily familiar with computer technology). The key motto to achieve this is "go visual." The well-known high bandwidth of the human-vision channel allows both recognition and understanding of large quantities of information in no more than a few seconds. Thus, for instance, if the result of an information request can be organized as a visual display, or a sequence of visual displays, the information throughput is immensely superior to the one that can be achieved using textual support. User interaction becomes an iterative query-answer game that very rapidly leads to the desired final result. Conversely, the system can provide efficient visual support for easy query formulation. Displaying a visual representation of the information space, for instance, lets users directly point at the information they are looking for, without any need to be trained into the complex syntax of current query languages. Alternatively, users can navigate in the information space, following visible paths that will lead them to the targeted items. Again, thanks to the visual support, users are able to easily understand how to formulate queries and they are likely to achieve the task more rapidly and less prone to errors than with traditional textual interaction modes.

0.010669115 = product of:
  0.032007344 = sum of:
    0.032007344 = weight(_text_:on in 3704) [ClassicSimilarity], result of:
      0.032007344 = score(doc=3704,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.29160398 = fieldWeight in 3704, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
  0.33333334 = coord(1/3)

Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

0.010669115 = product of:
  0.032007344 = sum of:
    0.032007344 = weight(_text_:on in 2557) [ClassicSimilarity], result of:
      0.032007344 = score(doc=2557,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.29160398 = fieldWeight in 2557, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.09375 = fieldNorm(doc=2557)
  0.33333334 = coord(1/3)

0.010058938 = product of:
  0.030176813 = sum of:
    0.030176813 = weight(_text_:on in 1455) [ClassicSimilarity], result of:
      0.030176813 = score(doc=1455,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.27492687 = fieldWeight in 1455, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0625 = fieldNorm(doc=1455)
  0.33333334 = coord(1/3)

Issue

Sponsored by IEEE Computer Society Technical Committee on Visualization and Graphics.

0.010058938 = product of:
  0.030176813 = sum of:
    0.030176813 = weight(_text_:on in 5244) [ClassicSimilarity], result of:
      0.030176813 = score(doc=5244,freq=16.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.27492687 = fieldWeight in 5244, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=5244)
  0.33333334 = coord(1/3)

Abstract

Boyack, Wylie, and Davidson developed VxInsight which transforms information from documents into a landscape representation which conveys information on the implicit structure of the data as context for queries and exploration. From a list of pre-computed similarities it creates on a plane an x,y location for each item, or can compute its own similarities based on direct and co-citation linkages. Three-dimensional overlays are then generated on the plane to show the extent of clustering at particular points. Metadata associated with clustered objects provides a label for each peak from common words. Clicking on an object will provide citation information and answer sets for queries run will be displayed as markers on the landscape. A time slider allows a view of terrain changes over time. In a test on the microsystems engineering literature a review article was used to provide seed terms to search Science Citation Index and retrieve 20,923 articles of which 13,433 were connected by citation to at least one other article in the set. The citation list was used to calculate similarity measures and x.y coordinates for each article. Four main categories made up the landscape with 90% of the articles directly related to one or more of the four. A second test used five databases: SCI, Cambridge Scientific Abstracts, Engineering Index, INSPEC, and Medline to extract 17,927 unique articles by Sandia, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory, with text of abstracts and RetrievalWare 6.6 utilized to generate the similarity measures. The subsequent map revealed that despite some overlap the laboratories generally publish in different areas. A third test on 3000 physical science journals utilized 4.7 million articles from SCI where similarity was the un-normalized sum of cites between journals in both directions. Physics occupies a central position, with engineering, mathematics, computing, and materials science strongly linked. Chemistry is farther removed but strongly connected.

0.010058938 = product of:
  0.030176813 = sum of:
    0.030176813 = weight(_text_:on in 580) [ClassicSimilarity], result of:
      0.030176813 = score(doc=580,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.27492687 = fieldWeight in 580, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0625 = fieldNorm(doc=580)
  0.33333334 = coord(1/3)

Abstract

A dentograph is a visualization of a library's collection built on the idea that a classification scheme is a mathematical function mapping one set of things (books or the universe of knowledge) onto another (a set of numbers and letters). Dentographs can visualize aspects of just one collection or can be used to compare two or more collections. This article describes how to build them, with examples and code using Ruby and R, and discusses some problems and future directions.

0.009940362 = product of:
  0.029821085 = sum of:
    0.029821085 = weight(_text_:on in 3285) [ClassicSimilarity], result of:
      0.029821085 = score(doc=3285,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.271686 = fieldWeight in 3285, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3285)
  0.33333334 = coord(1/3)

Abstract

This book is the outcome of the Dagstuhl Seminar on "Information Visualization - Human-Centered Issues in Visual Representation, Interaction, and Evaluation" held at Dagstuhl Castle, Germany, from May 28 to June 1, 2007. Information Visualization (InfoVis) is a relatively new research area, which focuses on the use of visualization techniques to help people understand and analyze data.This book documents and extends the findings and discussions of the various sessions in detail. The seven contributions cover the most important topics: Part I is on general reflections on the value of information visualization; evaluating information visualizations; theoretical foundations of information visualization; teaching information visualization. Part II deals with specific aspects on creation and collaboration: engaging new audiences for information visualization; process and pitfalls in writing information visualization research papers; and visual analytics: definition, process, and challenges.

0.009940362 = product of:
  0.029821085 = sum of:
    0.029821085 = weight(_text_:on in 10) [ClassicSimilarity], result of:
      0.029821085 = score(doc=10,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.271686 = fieldWeight in 10, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=10)
  0.33333334 = coord(1/3)

Abstract

Recent efforts in biomedical visual question answering (VQA) research rely on combined information gathered from the image content and surrounding text supporting the figure. Biomedical journals are a rich source of information for such multimodal content indexing. For multipanel figures in these journals, it is critical to develop automatic figure panel splitting and label recognition algorithms to associate individual panels with text metadata in the figure caption and the body of the article. Challenges in this task include large variations in figure panel layout, label location, size, contrast to background, and so on. In this work, we propose a deep convolutional neural network, which splits the panels and recognizes the panel labels in a single step. Visual features are extracted from several layers at various depths of the backbone neural network and organized to form a feature pyramid. These features are fed into classification and regression networks to generate candidates of panels and their labels. These candidates are merged to create the final panel segmentation result through a beam search algorithm. We evaluated the proposed algorithm on the ImageCLEF data set and achieved better performance than the results reported in the literature. In order to thoroughly investigate the proposed algorithm, we also collected and annotated our own data set of 10,642 figures. The experiments, trained on 9,642 figures and evaluated on the remaining 1,000 figures, show that combining panel splitting and panel label recognition mutually benefit each other.

0.009562228 = product of:
  0.028686684 = sum of:
    0.028686684 = product of:
      0.057373367 = sum of:
        0.057373367 = weight(_text_:22 in 3355) [ClassicSimilarity], result of:
          0.057373367 = score(doc=3355,freq=4.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.32829654 = fieldWeight in 3355, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3355)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

0.009409276 = product of:
  0.028227827 = sum of:
    0.028227827 = weight(_text_:on in 595) [ClassicSimilarity], result of:
      0.028227827 = score(doc=595,freq=14.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.25717056 = fieldWeight in 595, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=595)
  0.33333334 = coord(1/3)

Abstract

Science maps are visual representations of the structure and dynamics of scholarly knowl­edge. They aim to show how fields, disciplines, journals, scientists, publications, and scientific terms relate to each other. Science mapping is the body of methods and techniques that have been developed for generating science maps. This entry is an introduction to science maps and science mapping. It focuses on the conceptual, theoretical, and methodological issues of science mapping, rather than on the mathematical formulation of science mapping techniques. After a brief history of science mapping, we describe the general procedure for building a science map, presenting the data sources and the methods to select, clean, and pre-process the data. Next, we examine in detail how the most common types of science maps, namely the citation-based and the term-based, are generated. Both are based on networks: the former on the network of publications connected by citations, the latter on the network of terms co-occurring in publications. We review the rationale behind these mapping approaches, as well as the techniques and methods to build the maps (from the extraction of the network to the visualization and enrichment of the map). We also present less-common types of science maps, including co-authorship networks, interlocking editorship networks, maps based on patents' data, and geographic maps of science. Moreover, we consider how time can be represented in science maps to investigate the dynamics of science. We also discuss some epistemological and sociological topics that can help in the interpretation, contextualization, and assessment of science maps. Then, we present some possible applications of science maps in science policy. In the conclusion, we point out why science mapping may be interesting for all the branches of meta-science, from knowl­edge organization to epistemology.

Footnote

Beitrag in einem Special issue on 'Science and knowledge organization' mit längeren Überblicken zu wichtigen Begriffen der Wissensorgansiation.

0.009239726 = product of:
  0.027719175 = sum of:
    0.027719175 = weight(_text_:on in 2131) [ClassicSimilarity], result of:
      0.027719175 = score(doc=2131,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.25253648 = fieldWeight in 2131, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2131)
  0.33333334 = coord(1/3)

Abstract

This is a preprint to be published in the Extensions & Corrections to the UDC. The paper explains the basic functions of browsing and searching that need to be supported in relation to analytico-synthetic classifications such as Universal Decimal Classification (UDC), irrespective of any specific, real-life implementation. UDC is an example of a semi-faceted system that can be used, for instance, for both post-coordinate searching and hierarchical/facet browsing. The advantages of using a classification for IR, however, depend on the strength of the GUI, which should provide a user-friendly interface to classification browsing and searching. The power of this interface is in supporting visualisation that will 'convert' what is potentially a user-unfriendly indexing language based on symbols, to a subject presentation that is easy to understand, search and navigate. A summary of the basic functions of searching and browsing a classification that may be provided on a user-friendly interface is given and examples of classification browsing interfaces are provided.