Search (10 results, page 1 of 1)

0.025416005 = product of:
  0.05083201 = sum of:
    0.05083201 = product of:
      0.10166402 = sum of:
        0.10166402 = weight(_text_:web in 231) [ClassicSimilarity], result of:
          0.10166402 = score(doc=231,freq=22.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.018582305 = product of:
  0.03716461 = sum of:
    0.03716461 = product of:
      0.07432922 = sum of:
        0.07432922 = weight(_text_:web in 982) [ClassicSimilarity], result of:
          0.07432922 = score(doc=982,freq=6.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.015927691 = product of:
  0.031855382 = sum of:
    0.031855382 = product of:
      0.063710764 = sum of:
        0.063710764 = weight(_text_:web in 2796) [ClassicSimilarity], result of:
          0.063710764 = score(doc=2796,freq=6.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = 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.

0.013004904 = product of:
  0.026009807 = sum of:
    0.026009807 = product of:
      0.052019615 = sum of:
        0.052019615 = weight(_text_:web in 2857) [ClassicSimilarity], result of:
          0.052019615 = score(doc=2857,freq=4.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.009195855 = product of:
  0.01839171 = sum of:
    0.01839171 = product of:
      0.03678342 = sum of:
        0.03678342 = weight(_text_:web in 3112) [ClassicSimilarity], result of:
          0.03678342 = score(doc=3112,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.008823298 = product of:
  0.017646596 = sum of:
    0.017646596 = product of:
      0.03529319 = sum of:
        0.03529319 = weight(_text_:22 in 1778) [ClassicSimilarity], result of:
          0.03529319 = score(doc=1778,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = 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)
  0.5 = coord(1/2)

Date

8. 4.2015 16:22:13

0.007663213 = product of:
  0.015326426 = sum of:
    0.015326426 = product of:
      0.030652853 = sum of:
        0.030652853 = weight(_text_:web in 2376) [ClassicSimilarity], result of:
          0.030652853 = score(doc=2376,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.007663213 = product of:
  0.015326426 = sum of:
    0.015326426 = product of:
      0.030652853 = sum of:
        0.030652853 = weight(_text_:web in 419) [ClassicSimilarity], result of:
          0.030652853 = score(doc=419,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = 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.

0.007663213 = product of:
  0.015326426 = sum of:
    0.015326426 = product of:
      0.030652853 = sum of:
        0.030652853 = weight(_text_:web in 3088) [ClassicSimilarity], result of:
          0.030652853 = score(doc=3088,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = 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)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.0038316066 = product of:
  0.007663213 = sum of:
    0.007663213 = product of:
      0.015326426 = sum of:
        0.015326426 = weight(_text_:web in 1211) [ClassicSimilarity], result of:
          0.015326426 = score(doc=1211,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.09014259 = fieldWeight in 1211, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.