Search (9 results, page 1 of 1)

0.03128866 = product of:
  0.06257732 = sum of:
    0.06257732 = sum of:
      0.027226217 = weight(_text_:systems in 1778) [ClassicSimilarity], result of:
        0.027226217 = score(doc=1778,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.1697705 = fieldWeight in 1778, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1778)
      0.0353511 = weight(_text_:22 in 1778) [ClassicSimilarity], result of:
        0.0353511 = score(doc=1778,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = 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)
  0.5 = coord(1/2)

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

0.02521183 = product of:
  0.05042366 = sum of:
    0.05042366 = product of:
      0.10084732 = sum of:
        0.10084732 = weight(_text_:systems in 3867) [ClassicSimilarity], result of:
          0.10084732 = score(doc=3867,freq=14.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.6288387 = fieldWeight in 3867, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3867)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This entry describes two important classes of systems that facilitate the sharing of recommendations and expertise. Recommender systems suggest items of potential interest to individuals who do not have personal experience with the items. Expert locator systems, an important subset of recommender systems, help find people with the appropriate skills, knowledge, or expertise to meet a particular need. Research related to each of these systems is relatively new and extremely active. The use of these systems is likely to continue increasing as more and more activity is implicitly captured online, making it possible to automatically identify experts, and capture preferences that can be used to recommend items.

0.009625921 = product of:
  0.019251842 = sum of:
    0.019251842 = product of:
      0.038503684 = sum of:
        0.038503684 = weight(_text_:systems in 419) [ClassicSimilarity], result of:
          0.038503684 = score(doc=419,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.24009174 = fieldWeight in 419, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0390625 = fieldNorm(doc=419)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.009529176 = product of:
  0.019058352 = sum of:
    0.019058352 = product of:
      0.038116705 = sum of:
        0.038116705 = weight(_text_:systems in 3984) [ClassicSimilarity], result of:
          0.038116705 = score(doc=3984,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.23767869 = fieldWeight in 3984, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3984)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Network structure analysis plays an important role in characterizing complex systems. Different from previous network centrality measures, this article proposes the topological centrality measure reflecting the topological positions of nodes and edges as well as influence between nodes and edges in general network. Experiments on different networks show distinguished features of the topological centrality by comparing with the degree centrality, closeness centrality, betweenness centrality, information centrality, and PageRank. The topological centrality measure is then applied to discover communities and to construct the backbone network. Its characteristics and significance is further shown in e-Science applications.

0.008167865 = product of:
  0.01633573 = sum of:
    0.01633573 = product of:
      0.03267146 = sum of:
        0.03267146 = weight(_text_:systems in 1056) [ClassicSimilarity], result of:
          0.03267146 = score(doc=1056,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.2037246 = fieldWeight in 1056, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=1056)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

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

0.008167865 = product of:
  0.01633573 = sum of:
    0.01633573 = product of:
      0.03267146 = sum of:
        0.03267146 = weight(_text_:systems in 2275) [ClassicSimilarity], result of:
          0.03267146 = score(doc=2275,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.2037246 = fieldWeight in 2275, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=2275)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

We investigate the influence of culture and identity (geographic location) on the constitution of a specific research field. Using as case study the Sloan Digital Sky Survey (SDSS) project in the Astronomy field, we analyzed texts from bibliographic records of publications along three cultural and geographic axes: US only publications, non-US publications and international collaboration. Using three text mining systems (CiteSpace, TermWatch and PEx), we were able to automatically identify the topics specific to each cultural and geographic region as well as isolate the core research topics common to all geographic zones. The results tended to show that US-only and non-US research in this field shared more commonalities with international collaboration than with one another, thus indicating that the former two (US-only and non-US) research focused on rather distinct topics.

0.008167865 = product of:
  0.01633573 = sum of:
    0.01633573 = product of:
      0.03267146 = sum of:
        0.03267146 = weight(_text_:systems in 2796) [ClassicSimilarity], result of:
          0.03267146 = score(doc=2796,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.2037246 = fieldWeight in 2796, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=2796)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.0068065543 = product of:
  0.013613109 = sum of:
    0.013613109 = product of:
      0.027226217 = sum of:
        0.027226217 = weight(_text_:systems in 1044) [ClassicSimilarity], result of:
          0.027226217 = score(doc=1044,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.1697705 = fieldWeight in 1044, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1044)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.0058946493 = product of:
  0.011789299 = sum of:
    0.011789299 = product of:
      0.023578597 = sum of:
        0.023578597 = weight(_text_:systems in 1211) [ClassicSimilarity], result of:
          0.023578597 = score(doc=1211,freq=6.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.14702557 = fieldWeight in 1211, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

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.