Search (34 results, page 1 of 2)

0.018461186 = product of:
  0.0861522 = sum of:
    0.057825863 = weight(_text_:web in 231) [ClassicSimilarity], result of:
      0.057825863 = score(doc=231,freq=22.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.59793836 = fieldWeight in 231, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=231)
    0.013347364 = weight(_text_:information in 231) [ClassicSimilarity], result of:
      0.013347364 = score(doc=231,freq=14.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.256578 = fieldWeight in 231, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=231)
    0.014978974 = weight(_text_:retrieval in 231) [ClassicSimilarity], result of:
      0.014978974 = score(doc=231,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 231, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=231)
  0.21428572 = coord(3/14)

Abstract

As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

Source

Proceeding ISWC'07/ASWC'07 : Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Ed.: K. Aberer et al

Theme

Semantic Web

0.016191853 = product of:
  0.056671485 = sum of:
    0.016068742 = weight(_text_:wide in 1211) [ClassicSimilarity], result of:
      0.016068742 = score(doc=1211,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = 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)
    0.008717576 = weight(_text_:web in 1211) [ClassicSimilarity], result of:
      0.008717576 = score(doc=1211,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = 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)
    0.010701699 = weight(_text_:information in 1211) [ClassicSimilarity], result of:
      0.010701699 = score(doc=1211,freq=36.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.20572007 = fieldWeight in 1211, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
    0.021183468 = weight(_text_:retrieval in 1211) [ClassicSimilarity], result of:
      0.021183468 = score(doc=1211,freq=16.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23632148 = fieldWeight in 1211, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.2857143 = coord(4/14)

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.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.016174633 = product of:
  0.07548162 = sum of:
    0.042278 = weight(_text_:web in 982) [ClassicSimilarity], result of:
      0.042278 = score(doc=982,freq=6.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.43716836 = fieldWeight in 982, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0546875 = fieldNorm(doc=982)
    0.012233062 = weight(_text_:information in 982) [ClassicSimilarity], result of:
      0.012233062 = score(doc=982,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23515764 = fieldWeight in 982, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=982)
    0.020970564 = weight(_text_:retrieval in 982) [ClassicSimilarity], result of:
      0.020970564 = score(doc=982,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23394634 = fieldWeight in 982, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=982)
  0.21428572 = coord(3/14)

Abstract

Metadata is designed to improve information organization and information retrieval effectiveness and efficiency on the Internet. The way web publishers respond to metadata and the way they use it when publishing their web pages, however, is still a mystery. The authors of this paper aim to solve this mystery by defining different professional publisher groups, examining the behaviors of these user groups, and identifying the characteristics of their metadata use. This study will enhance the current understanding of metadata application behavior and provide evidence useful to researchers, web publishers, and search engine designers.

Source

Information processing and management. 42(2006) no.4, S.1099-1122

0.015341733 = product of:
  0.07159475 = sum of:
    0.03856498 = weight(_text_:wide in 3112) [ClassicSimilarity], result of:
      0.03856498 = score(doc=3112,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.29372054 = fieldWeight in 3112, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=3112)
    0.020922182 = weight(_text_:web in 3112) [ClassicSimilarity], result of:
      0.020922182 = score(doc=3112,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 3112, 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=3112)
    0.012107591 = weight(_text_:information in 3112) [ClassicSimilarity], result of:
      0.012107591 = score(doc=3112,freq=8.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23274569 = fieldWeight in 3112, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=3112)
  0.21428572 = coord(3/14)

Abstract

Concerns about health issues cover a wide spectrum. Consumer health information, which has become more available on the Internet, plays an extremely important role in addressing these concerns. A subject directory as an information organization and browsing mechanism is widely used in consumer health-related Websites. In this study we employed the information visualization technique Self-Organizing Map (SOM) in combination with a new U-matrix algorithm to analyze health subject clusters through a Web transaction log. An experimental study was conducted to test the proposed methods. The findings show that the clusters identified from the same cells based on path-length-1 outperformed both the clusters from the adjacent cells based on path-length-1 and the clusters from the same cells based on path-length-2 in the visual SOM display. The U-matrix method successfully distinguished the irrelevant subjects situated in the adjacent cells with different colors in the SOM display. The findings of this study lead to a better understanding of the health-related subject relationship from the users' traversal perspective.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.1977-1994

0.0113271745 = product of:
  0.07929022 = sum of:
    0.019976506 = weight(_text_:information in 7711) [ClassicSimilarity], result of:
      0.019976506 = score(doc=7711,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.3840108 = fieldWeight in 7711, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=7711)
    0.05931371 = weight(_text_:retrieval in 7711) [ClassicSimilarity], result of:
      0.05931371 = score(doc=7711,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.6617001 = fieldWeight in 7711, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.109375 = fieldNorm(doc=7711)
  0.14285715 = coord(2/14)

Source

Information processing and management. 37(2001) no.4, S.639-657

0.009286508 = product of:
  0.065005556 = sum of:
    0.011415146 = weight(_text_:information in 3916) [ClassicSimilarity], result of:
      0.011415146 = score(doc=3916,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.21943474 = fieldWeight in 3916, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=3916)
    0.05359041 = weight(_text_:retrieval in 3916) [ClassicSimilarity], result of:
      0.05359041 = score(doc=3916,freq=10.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.59785134 = fieldWeight in 3916, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=3916)
  0.14285715 = coord(2/14)

Abstract

This article presents a visualization tool for information retrieval. Some retrieval evaluation models are interpreted in the two-dimensional space comprising direction and distance. The two different similarity measures-angle and distance-are displayed in the visual space. A new retrieval means based on the visual retrieval tool, the controlling bar, is developed for a search

Source

Journal of the American Society for Information Science. 50(1999) no.9, S.779-787

0.0081256945 = product of:
  0.05687986 = sum of:
    0.009988253 = weight(_text_:information in 1166) [ClassicSimilarity], result of:
      0.009988253 = score(doc=1166,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.1920054 = fieldWeight in 1166, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1166)
    0.046891607 = weight(_text_:retrieval in 1166) [ClassicSimilarity], result of:
      0.046891607 = score(doc=1166,freq=10.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.5231199 = fieldWeight in 1166, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1166)
  0.14285715 = coord(2/14)

Abstract

The text retrieval method using latent semantic indexing (LSI) technique with truncated singular value decomposition (SVD) has been intensively studied in recent years. The SVD reduces the noise contained in the original representation of the term-document matrix and improves the information retrieval accuracy. Recent studies indicate that SVD is mostly useful for small homogeneous data collections. For large inhomogeneous datasets, the performance of the SVD based text retrieval technique may deteriorate. We propose to partition a large inhomogeneous dataset into several smaller ones with clustered structure, on which we apply the truncated SVD. Our experimental results show that the clustered SVD strategies may enhance the retrieval accuracy and reduce the computing and storage costs.

Source

Information processing and management. 41(2005) no.5, S.1051-1064

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0068057464 = product of:
  0.03176015 = sum of:
    0.010089659 = weight(_text_:information in 1778) [ClassicSimilarity], result of:
      0.010089659 = score(doc=1778,freq=8.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.19395474 = fieldWeight in 1778, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1778)
    0.014978974 = weight(_text_:retrieval in 1778) [ClassicSimilarity], result of:
      0.014978974 = score(doc=1778,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 1778, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1778)
    0.0066915164 = product of:
      0.020074548 = sum of:
        0.020074548 = weight(_text_:22 in 1778) [ClassicSimilarity], result of:
          0.020074548 = score(doc=1778,freq=2.0), product of:
            0.103770934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029633347 = queryNorm
            0.19345059 = fieldWeight in 1778, 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=1778)
      0.33333334 = coord(1/3)
  0.21428572 = coord(3/14)

Abstract

Purpose - The purpose of this paper is to introduce a new similarity method to gauge the differences between two subject hierarchical structures. Design/methodology/approach - In the proposed similarity measure, nodes on two hierarchical structures are projected onto a two-dimensional space, respectively, and both structural similarity and subject similarity of nodes are considered in the similarity between the two hierarchical structures. The extent to which the structural similarity impacts on the similarity can be controlled by adjusting a parameter. An experiment was conducted to evaluate soundness of the measure. Eight experts whose research interests were information retrieval and information organization participated in the study. Results from the new measure were compared with results from the experts. Findings - The evaluation shows strong correlations between the results from the new method and the results from the experts. It suggests that the similarity method achieved satisfactory results. Practical implications - Hierarchical structures that are found in subject directories, taxonomies, classification systems, and other classificatory structures play an extremely important role in information organization and information representation. Measuring the similarity between two subject hierarchical structures allows an accurate overarching understanding of the degree to which the two hierarchical structures are similar. Originality/value - Both structural similarity and subject similarity of nodes were considered in the proposed similarity method, and the extent to which the structural similarity impacts on the similarity can be adjusted. In addition, a new evaluation method for a hierarchical structure similarity was presented.

Date

8. 4.2015 16:22:13

0.00639995 = product of:
  0.04479965 = sum of:
    0.036238287 = weight(_text_:web in 2796) [ClassicSimilarity], result of:
      0.036238287 = score(doc=2796,freq=6.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.37471575 = fieldWeight in 2796, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2796)
    0.00856136 = weight(_text_:information in 2796) [ClassicSimilarity], result of:
      0.00856136 = score(doc=2796,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16457605 = fieldWeight in 2796, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2796)
  0.14285715 = coord(2/14)

Abstract

Session characteristics taken from large transaction logs of three Web search environments (academic Web site, public search engine, consumer health information portal) were modeled using cluster analysis to determine if coherent session groups emerged for each environment and whether the types of session groups are similar across the three environments. The analysis revealed three distinct clusters of session behaviors common to each environment: hit and run sessions on focused topics, relatively brief sessions on popular topics, and sustained sessions using obscure terms with greater query modification. The findings also revealed shifts in session characteristics over time for one of the datasets, away from hit and run sessions toward more popular search topics. A better understanding of session characteristics can help system designers to develop more responsive systems to support search features that cater to identifiable groups of searchers based on their search behaviors. For example, the system may identify struggling searchers based on session behaviors that match those identified in the current study to provide context sensitive help.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.5, S.896-910

0.005361108 = product of:
  0.037527755 = sum of:
    0.012107591 = weight(_text_:information in 1056) [ClassicSimilarity], result of:
      0.012107591 = score(doc=1056,freq=8.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23274569 = fieldWeight in 1056, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1056)
    0.025420163 = weight(_text_:retrieval in 1056) [ClassicSimilarity], result of:
      0.025420163 = score(doc=1056,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.2835858 = fieldWeight in 1056, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1056)
  0.14285715 = coord(2/14)

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.

Source

Information processing and management. 41(2005) no.4, S.1003-1018

0.0050917473 = product of:
  0.03564223 = sum of:
    0.029588435 = weight(_text_:web in 2857) [ClassicSimilarity], result of:
      0.029588435 = score(doc=2857,freq=4.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.3059541 = fieldWeight in 2857, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2857)
    0.0060537956 = weight(_text_:information in 2857) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=2857,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 2857, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2857)
  0.14285715 = coord(2/14)

Abstract

Bibliometric analysis based on literature in the Web of Science (WOS) has become an increasingly popular method for visualizing the structure of scientific fields. Keywords Plus and Author Keywords are commonly selected as units of analysis, despite the limited research evidence demonstrating the effectiveness of Keywords Plus. This study was conceived to evaluate the efficacy of Keywords Plus as a parameter for capturing the content and scientific concepts presented in articles. Using scientific papers about patient adherence that were retrieved from WOS, a comparative assessment of Keywords Plus and Author Keywords was performed at the scientific field level and the document level, respectively. Our search yielded more Keywords Plus terms than Author Keywords, and the Keywords Plus terms were more broadly descriptive. Keywords Plus is as effective as Author Keywords in terms of bibliometric analysis investigating the knowledge structure of scientific fields, but it is less comprehensive in representing an article's content.

Object

Web of Science

Source

Journal of the Association for Information Science and Technology. 67(2016) no.4, S.967-972

0.0048545036 = product of:
  0.033981524 = sum of:
    0.00856136 = weight(_text_:information in 1963) [ClassicSimilarity], result of:
      0.00856136 = score(doc=1963,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16457605 = fieldWeight in 1963, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1963)
    0.025420163 = weight(_text_:retrieval in 1963) [ClassicSimilarity], result of:
      0.025420163 = score(doc=1963,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.2835858 = fieldWeight in 1963, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1963)
  0.14285715 = coord(2/14)

Abstract

Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector-based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.1, S.3-11

0.0037468998 = product of:
  0.026228298 = sum of:
    0.0050448296 = weight(_text_:information in 5238) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=5238,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 5238, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5238)
    0.021183468 = weight(_text_:retrieval in 5238) [ClassicSimilarity], result of:
      0.021183468 = score(doc=5238,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23632148 = fieldWeight in 5238, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5238)
  0.14285715 = coord(2/14)

Abstract

Wolfram and Zhang are interested in the effect of different indexing exhaustivity, by which they mean the number of terms chosen, and of different index term distributions and different term weighting methods on the resulting document cluster organization. The Distance Angle Retrieval Environment, DARE, which provides a two dimensional display of retrieved documents was used to represent the document clusters based upon a document's distance from the searcher's main interest, and on the angle formed by the document, a point representing a minor interest, and the point representing the main interest. If the centroid and the origin of the document space are assigned as major and minor points the average distance between documents and the centroid can be measured providing an indication of cluster organization. in the form of a size normalized similarity measure. Using 500 records from NTIS and nine models created by intersecting low, observed, and high exhaustivity levels (based upon a negative binomial distribution) with shallow, observed, and steep term distributions (based upon a Zipf distribution) simulation runs were preformed using inverse document frequency, inter-document term frequency, and inverse document frequency based upon both inter and intra-document frequencies. Low exhaustivity and shallow distributions result in a more dense document space and less effective retrieval. High exhaustivity and steeper distributions result in a more diffuse space.

Source

Journal of the American Society for Information Science and Technology. 53(2002) no.11, S.944-952

0.0035099457 = product of:
  0.02456962 = sum of:
    0.017435152 = weight(_text_:web in 2376) [ClassicSimilarity], result of:
      0.017435152 = score(doc=2376,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.18028519 = fieldWeight in 2376, 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=2376)
    0.0071344664 = weight(_text_:information in 2376) [ClassicSimilarity], result of:
      0.0071344664 = score(doc=2376,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.13714671 = fieldWeight in 2376, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2376)
  0.14285715 = coord(2/14)

Abstract

A multidimensional-scaling approach is used to analyze frequently used medical-topic terms in queries submitted to a Web-based consumer health information system. Based on a year-long transaction log file, five medical focus keywords (stomach, hip, stroke, depression, and cholesterol) and their co-occurring query terms are analyzed. An overlap-coefficient similarity measure and a conversion measure are used to calculate the proximity of terms to one another based on their co-occurrences in queries. The impact of the dimensionality of the visual configuration, the cutoff point of term co-occurrence for inclusion in the analysis, and the Minkowski metric power k on the stress value are discussed. A visual clustering of groups of terms based on the proximity within each focus-keyword group is also conducted. Term distributions within each visual configuration are characterized and are compared with formal medical vocabulary. This investigation reveals that there are significant differences between consumer health query-term usage and more formal medical terminology used by medical professionals when describing the same medical subject. Future directions are discussed.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.12, S.1933-1947

0.0035099457 = product of:
  0.02456962 = sum of:
    0.017435152 = weight(_text_:web in 419) [ClassicSimilarity], result of:
      0.017435152 = score(doc=419,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.18028519 = fieldWeight in 419, 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=419)
    0.0071344664 = weight(_text_:information in 419) [ClassicSimilarity], result of:
      0.0071344664 = score(doc=419,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.13714671 = fieldWeight in 419, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=419)
  0.14285715 = coord(2/14)

Abstract

The findings of a recent study on digital archival representation raise some ethical concerns about how digital archival materials are organized, described, and made available for use on the Web. Archivists have a fundamental obligation to preserve and protect the authenticity and integrity of records in their holdings and, at the same time, have the responsibility to promote the use of records as a fundamental purpose of the keeping of archives (SAA 2005 Code of Ethics for Archivists V & VI). Is it an ethical practice that digital content in digital archives is deeply embedded in its contextual structure and generally underrepresented in digital archival systems? Similarly, is it ethical for archivists to detach digital items from their archival context in order to make them more "digital friendly" and more accessible to meet needs of some users? Do archivists have an obligation to bring the two representation systems together so that the context and content of digital archives can be better represented and archival materials "can be located and used by anyone, for any purpose, while still remaining authentic evidence of the work and life of the creator"? (Millar 2010, 157) This paper discusses the findings of the study and their ethical implications relating to digital archival description and representation.

Content

Beitrag aus einem Themenheft zu den Proceedings of the 2nd Milwaukee Conference on Ethics in Information Organization, June 15-16, 2012, School of Information Studies, University of Wisconsin-Milwaukee. Hope A. Olson, Conference Chair. Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_39_2012_5_d.pdf.

0.003211426 = product of:
  0.022479981 = sum of:
    0.017435152 = weight(_text_:web in 3088) [ClassicSimilarity], result of:
      0.017435152 = score(doc=3088,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.18028519 = fieldWeight in 3088, 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=3088)
    0.0050448296 = weight(_text_:information in 3088) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=3088,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 3088, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3088)
  0.14285715 = coord(2/14)

Source

Journal of the Association for Information Science and Technology. 67(2016) no.9, S.2166-2180

0.0031590632 = product of:
  0.02211344 = sum of:
    0.0071344664 = weight(_text_:information in 1044) [ClassicSimilarity], result of:
      0.0071344664 = score(doc=1044,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.13714671 = fieldWeight in 1044, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1044)
    0.014978974 = weight(_text_:retrieval in 1044) [ClassicSimilarity], result of:
      0.014978974 = score(doc=1044,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 1044, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1044)
  0.14285715 = coord(2/14)

Abstract

In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be "important publications" for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency-inverted document frequency (tf-idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1852-1863

0.0028605436 = product of:
  0.020023804 = sum of:
    0.0050448296 = weight(_text_:information in 5210) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=5210,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 5210, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5210)
    0.014978974 = weight(_text_:retrieval in 5210) [ClassicSimilarity], result of:
      0.014978974 = score(doc=5210,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 5210, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5210)
  0.14285715 = coord(2/14)

Abstract

Zang and Wolfram compute the discrimination value for terms as the difference between the centroid value of all terms in the corpus and that value without the term in question, and suggest selection be made by comparing density changes with a visualization tool. The Distance Angle Retrieval Environment (DARE) visually projects a document or term space by presenting distance similarity on the X axis and angular similarity on the Y axis. Thus a document icon appearing close to the X axis would be relevant to reference points in terms of a distance similarity measure, while those close to the Y axis are relevant to reference points in terms of an angle based measure. Using 450 Associated Press news reports indexed by 44 distinct terms, the removal of the term ``Yeltsin'' causes the cluster to fall on the Y axis indicating a good discriminator. For an angular measure, cosine say, movement along the X axis to the left will signal good discrimination, as movement to the right will signal poor discrimination. A term density space could also be used. Most terms are shown to be indifferent discriminators. Different measures result in different choices as good and poor discriminators, as does the use of a term space rather than a document space. The visualization approach is clearly feasible, and provides some additional insights not found in the computation of a discrimination value.

Source

Journal of the American Society for Information Science and technology. 52(2001) no.8, S.615-627

0.001008966 = product of:
  0.014125523 = sum of:
    0.014125523 = weight(_text_:information in 4652) [ClassicSimilarity], result of:
      0.014125523 = score(doc=4652,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.27153665 = fieldWeight in 4652, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=4652)
  0.071428575 = coord(1/14)

Source

Journal of information science. 30(2005) no.4, S.310-

8.153676E-4 = product of:
  0.011415146 = sum of:
    0.011415146 = weight(_text_:information in 6403) [ClassicSimilarity], result of:
      0.011415146 = score(doc=6403,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.21943474 = fieldWeight in 6403, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=6403)
  0.071428575 = coord(1/14)

Abstract

Analyses graphic and tabular displays under a common, unified form - relational information displays (RIDs) which are displays that represent relations between dimensions. A representational taxonomy is developed that classifies all RIDs and serves as a framework for systematic studies of RIDs. Develops a taxonomy of RIDs which can classifiy the majority of dimension based display tasks and analyzes the relation between representations of displays and structures of tasks in terms of a mapping principle