Search (232 results, page 1 of 12)

0.035024326 = product of:
  0.08756082 = sum of:
    0.01563882 = weight(_text_:of in 436) [ClassicSimilarity], result of:
      0.01563882 = score(doc=436,freq=6.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.23940048 = fieldWeight in 436, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=436)
    0.071922 = product of:
      0.143844 = sum of:
        0.143844 = weight(_text_:mind in 436) [ClassicSimilarity], result of:
          0.143844 = score(doc=436,freq=2.0), product of:
            0.2607373 = queryWeight, product of:
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.04177434 = queryNorm
            0.5516817 = fieldWeight in 436, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.0625 = fieldNorm(doc=436)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

This paper discusses the use of wiki technology to provide a navigation structure for a collection of newspaper clippings. We overview the architecture of the wiki, discuss the navigation structure and pose the question: is the navigation structure an index, and if so, what type, or is it just a linkage structure or topic map. Does such a distinction really matter? Are these definitions in reality function based?

0.033874195 = product of:
  0.08468549 = sum of:
    0.02111482 = weight(_text_:of in 2257) [ClassicSimilarity], result of:
      0.02111482 = score(doc=2257,freq=28.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32322758 = fieldWeight in 2257, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2257)
    0.06357067 = product of:
      0.12714134 = sum of:
        0.12714134 = weight(_text_:mind in 2257) [ClassicSimilarity], result of:
          0.12714134 = score(doc=2257,freq=4.0), product of:
            0.2607373 = queryWeight, product of:
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.04177434 = queryNorm
            0.48762235 = fieldWeight in 2257, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2257)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

The Future of Information Architecture examines issues surrounding why information is processed, stored and applied in the way that it has, since time immemorial. Contrary to the conventional wisdom held by many scholars in human history, the recurrent debate on the explanation of the most basic categories of information (eg space, time causation, quality, quantity) has been misconstrued, to the effect that there exists some deeper categories and principles behind these categories of information - with enormous implications for our understanding of reality in general. To understand this, the book is organised in to four main parts: Part I begins with the vital question concerning the role of information within the context of the larger theoretical debate in the literature. Part II provides a critical examination of the nature of data taxonomy from the main perspectives of culture, society, nature and the mind. Part III constructively invesitgates the world of information network from the main perspectives of culture, society, nature and the mind. Part IV proposes six main theses in the authors synthetic theory of information architecture, namely, (a) the first thesis on the simpleness-complicatedness principle, (b) the second thesis on the exactness-vagueness principle (c) the third thesis on the slowness-quickness principle (d) the fourth thesis on the order-chaos principle, (e) the fifth thesis on the symmetry-asymmetry principle, and (f) the sixth thesis on the post-human stage.

0.03223907 = product of:
  0.08059767 = sum of:
    0.017665926 = weight(_text_:of in 2385) [ClassicSimilarity], result of:
      0.017665926 = score(doc=2385,freq=10.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2704316 = fieldWeight in 2385, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2385)
    0.062931746 = product of:
      0.12586349 = sum of:
        0.12586349 = weight(_text_:mind in 2385) [ClassicSimilarity], result of:
          0.12586349 = score(doc=2385,freq=2.0), product of:
            0.2607373 = queryWeight, product of:
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.04177434 = queryNorm
            0.48272148 = fieldWeight in 2385, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2385)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

LyberWorld is a prototype IR user interface. It implements visualizations of an abstract information space: fulltext. The video demonstrates a visual user interface for the probabilistic fulltext retrieval system INQUERY. Visualizations are used to communicate information search and browsing activities in a natural way by applying metaphors of spatial navigation in abstract information spaces. Visualization tools for exploring information spaces and judging relevance of information items are introduced and an example session demonstrates the prototype. The presence of a spatial model in the user's mind is regarded as an essential contribution towards natural interaction and reduction of cognitive costs during retrieval dialogues.

0.029486848 = product of:
  0.07371712 = sum of:
    0.05623238 = weight(_text_:philosophy in 535) [ClassicSimilarity], result of:
      0.05623238 = score(doc=535,freq=2.0), product of:
        0.23055021 = queryWeight, product of:
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.04177434 = queryNorm
        0.24390514 = fieldWeight in 535, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.03125 = fieldNorm(doc=535)
    0.017484732 = weight(_text_:of in 535) [ClassicSimilarity], result of:
      0.017484732 = score(doc=535,freq=30.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.26765788 = fieldWeight in 535, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=535)
  0.4 = coord(2/5)

Abstract

Literature-based discovery (LBD) research aims at finding effective computational methods for predicting previously unknown connections between clusters of research papers from disparate research areas. Existing methods encompass two general approaches. The first approach searches for these unknown connections by examining the textual contents of research papers. In addition to the existing textual features, the second approach incorporates structural features of scientific literatures, such as citation structures. These approaches, however, have not considered research papers' latent bibliographic metadata structures as important features that can be used for predicting previously unknown relationships between them. This thesis investigates a new graph-based LBD method that exploits the latent bibliographic metadata connections between pairs of research papers. The heterogeneous bibliographic information network is proposed as an efficient graph-based data structure for modeling the complex relationships between these metadata. In contrast to previous approaches, this method seamlessly combines textual and citation information in the form of pathbased metadata features for predicting future co-citation links between research papers from disparate research fields. The results reported in this thesis provide evidence that the method is effective for reconstructing the historical literature-based discovery hypotheses. This thesis also investigates the effects of semantic modeling and topic modeling on the performance of the proposed method. For semantic modeling, a general-purpose word sense disambiguation technique is proposed to reduce the lexical ambiguity in the title and abstract of research papers. The experimental results suggest that the reduced lexical ambiguity did not necessarily lead to a better performance of the method. This thesis discusses some of the possible contributing factors to these results. Finally, topic modeling is used for learning the latent topical relations between research papers. The learned topic model is incorporated into the heterogeneous bibliographic information network graph and allows new predictive features to be learned. The results in this thesis suggest that topic modeling improves the performance of the proposed method by increasing the overall accuracy for predicting the future co-citation links between disparate research papers.

Footnote

A thesis submitted in ful llment of the requirements for the degree of Doctor of Philosophy Monash University, Faculty of Information Technology.

0.028211588 = product of:
  0.07052897 = sum of:
    0.016587472 = weight(_text_:of in 2384) [ClassicSimilarity], result of:
      0.016587472 = score(doc=2384,freq=12.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.25392252 = fieldWeight in 2384, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2384)
    0.053941496 = product of:
      0.10788299 = sum of:
        0.10788299 = weight(_text_:mind in 2384) [ClassicSimilarity], result of:
          0.10788299 = score(doc=2384,freq=2.0), product of:
            0.2607373 = queryWeight, product of:
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.04177434 = queryNorm
            0.41376126 = fieldWeight in 2384, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.241566 = idf(docFreq=233, maxDocs=44218)
              0.046875 = fieldNorm(doc=2384)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

LyberWorld is a prototype IR user interface. It implements visualizations of an abstract information space-fulltext. The paper derives a model for such visualizations and an exemplar user interface design is implemented for the probabilistic fulltext retrieval system INQUERY. Visualizations are used to communicate information search and browsing activities in a natural way by applying metaphors of spatial navigation in abstract information spaces. Visualization tools for exploring information spaces and judging relevance of information items are introduced and an example session demonstrates the prototype. The presence of a spatial model in the user's mind and interaction with a system's corresponding display methods is regarded as an essential contribution towards natural interaction and reduction of cognitive costs during e.g. query construction, orientation within the database content, relevance judgement and orientation within the retrieval context.

Source

Proceeding SIGIR '94: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval

0.024185965 = product of:
  0.04030994 = sum of:
    0.008315044 = product of:
      0.041575223 = sum of:
        0.041575223 = weight(_text_:problem in 1343) [ClassicSimilarity], result of:
          0.041575223 = score(doc=1343,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.23447686 = fieldWeight in 1343, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1343)
      0.2 = coord(1/5)
    0.017845279 = weight(_text_:of in 1343) [ClassicSimilarity], result of:
      0.017845279 = score(doc=1343,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 1343, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1343)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 1343) [ClassicSimilarity], result of:
          0.028299233 = score(doc=1343,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 1343, 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=1343)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.

Date

22. 8.2014 17:07:50

Source

Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1870-1883

0.022275176 = product of:
  0.037125293 = sum of:
    0.0066520358 = product of:
      0.033260178 = sum of:
        0.033260178 = weight(_text_:problem in 1163) [ClassicSimilarity], result of:
          0.033260178 = score(doc=1163,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.1875815 = fieldWeight in 1163, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03125 = fieldNorm(doc=1163)
      0.2 = coord(1/5)
    0.019153563 = weight(_text_:of in 1163) [ClassicSimilarity], result of:
      0.019153563 = score(doc=1163,freq=36.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2932045 = fieldWeight in 1163, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=1163)
    0.011319693 = product of:
      0.022639386 = sum of:
        0.022639386 = weight(_text_:22 in 1163) [ClassicSimilarity], result of:
          0.022639386 = score(doc=1163,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.15476047 = fieldWeight in 1163, 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=1163)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

This paper addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. This paper presents the results of an experiment designed to test one approach to generation of such alerts. The technique of latent semantic indexing (LSI) is used to identify relationships among entities of interest. Entity extraction software is used to pre-process the text of the collection so that the LSI space contains representation vectors for named entities in addition to those for individual terms. In the LSI space, the cosine of the angle between the representation vectors for two entities captures important information regarding the degree of association of those two entities. For appropriate choices of entities, determining the entity pairs with the highest mutual cosine values yields valuable information regarding the contents of the text collection. The test database used for the experiment consists of 150,000 news articles. The proposed approach for alert generation is tested using a counterterrorism analysis example. The approach is shown to have significant potential for aiding users in rapidly focusing on information of potential importance in large text collections. The approach also has value in identifying possible use of aliases.

Source

Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]

0.022167925 = product of:
  0.05541981 = sum of:
    0.015800884 = weight(_text_:of in 2134) [ClassicSimilarity], result of:
      0.015800884 = score(doc=2134,freq=2.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.24188137 = fieldWeight in 2134, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.109375 = fieldNorm(doc=2134)
    0.039618924 = product of:
      0.07923785 = sum of:
        0.07923785 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
          0.07923785 = score(doc=2134,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.5416616 = fieldWeight in 2134, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=2134)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Date

30. 3.2001 13:32:22

0.017131606 = product of:
  0.042829014 = sum of:
    0.02018963 = weight(_text_:of in 5693) [ClassicSimilarity], result of:
      0.02018963 = score(doc=5693,freq=10.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.3090647 = fieldWeight in 5693, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=5693)
    0.022639386 = product of:
      0.045278773 = sum of:
        0.045278773 = weight(_text_:22 in 5693) [ClassicSimilarity], result of:
          0.045278773 = score(doc=5693,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.30952093 = fieldWeight in 5693, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=5693)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

The process of term selection for query expansion by end-users is discussed within the context of a study of interactive query expansion in a relevance feedback environment. This user study focuses on how users' perceive and understand term relationships, such as hierarchical and associative relationships, in their searches

Date

30. 3.2001 13:35:22

Source

Knowledge organization and quality management: Proc. of the 3rd International ISKO Conference, 20-24 June 1994, Copenhagen, Denmark. Ed.: H. Albrechtsen et al

0.016862115 = product of:
  0.04215529 = sum of:
    0.022345824 = weight(_text_:of in 6958) [ClassicSimilarity], result of:
      0.022345824 = score(doc=6958,freq=16.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.34207192 = fieldWeight in 6958, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6958)
    0.019809462 = product of:
      0.039618924 = sum of:
        0.039618924 = weight(_text_:22 in 6958) [ClassicSimilarity], result of:
          0.039618924 = score(doc=6958,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.2708308 = fieldWeight in 6958, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6958)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Reports on the design of a GUI for the Okapi 'best match' retrieval system developed at the Centre for Interactive Systems Research, City University, UK, for online library catalogues. The X-Windows interface includes an interactive query expansion (IQE) facilty which involves the user in the selection of query terms to reformulate a search. Presents the design rationale, based on a game board metaphor, and describes the features of each of the stages of the search interaction. Reports on the early operational field trial and discusses relevant evaluation issues and objectives

Source

Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon

0.01667951 = product of:
  0.041698776 = sum of:
    0.013683967 = weight(_text_:of in 5295) [ClassicSimilarity], result of:
      0.013683967 = score(doc=5295,freq=6.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.20947541 = fieldWeight in 5295, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5295)
    0.02801481 = product of:
      0.05602962 = sum of:
        0.05602962 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
          0.05602962 = score(doc=5295,freq=4.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.38301262 = fieldWeight in 5295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5295)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

A new search paradigm, in which the primary user activity is the guided exploration of a complex information space rather than the retrieval of items based on precise specifications, is proposed. The author claims that this paradigm is the norm in most practical applications, and that solutions based on traditional search methods are not effective in this context. He then presents a solution based on dynamic taxonomies, a knowledge management model that effectively guides users to reach their goal while giving them total freedom in exploring the information base. Applications, benefits, and current research are discussed.

Date

22. 7.2006 17:56:22

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.792-796

0.015834233 = product of:
  0.03958558 = sum of:
    0.011286346 = weight(_text_:of in 2754) [ClassicSimilarity], result of:
      0.011286346 = score(doc=2754,freq=2.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.17277241 = fieldWeight in 2754, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.078125 = fieldNorm(doc=2754)
    0.028299233 = product of:
      0.056598466 = sum of:
        0.056598466 = weight(_text_:22 in 2754) [ClassicSimilarity], result of:
          0.056598466 = score(doc=2754,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.38690117 = fieldWeight in 2754, 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=2754)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Date

1. 2.2016 18:25:22

0.015834233 = product of:
  0.03958558 = sum of:
    0.011286346 = weight(_text_:of in 3280) [ClassicSimilarity], result of:
      0.011286346 = score(doc=3280,freq=2.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.17277241 = fieldWeight in 3280, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.078125 = fieldNorm(doc=3280)
    0.028299233 = product of:
      0.056598466 = sum of:
        0.056598466 = weight(_text_:22 in 3280) [ClassicSimilarity], result of:
          0.056598466 = score(doc=3280,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.38690117 = fieldWeight in 3280, 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=3280)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.015664605 = product of:
  0.03916151 = sum of:
    0.01935205 = weight(_text_:of in 1466) [ClassicSimilarity], result of:
      0.01935205 = score(doc=1466,freq=12.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.29624295 = fieldWeight in 1466, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1466)
    0.019809462 = product of:
      0.039618924 = sum of:
        0.039618924 = weight(_text_:22 in 1466) [ClassicSimilarity], result of:
          0.039618924 = score(doc=1466,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.2708308 = fieldWeight in 1466, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1466)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

The paper discusses the issue of increasing users' access to library records by their publication in Google. Data from the records, converted into html format, have been indexed by Google. The process covered basic formal description fields of the records, description of the content, supported with a thesaurus, as well as an abstract, if present in the record. In addition to monitoring the end users' statistics, the pilot testing covered visibility of library records in Google search results.

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

0.015664605 = product of:
  0.03916151 = sum of:
    0.01935205 = weight(_text_:of in 2151) [ClassicSimilarity], result of:
      0.01935205 = score(doc=2151,freq=12.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.29624295 = fieldWeight in 2151, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2151)
    0.019809462 = product of:
      0.039618924 = sum of:
        0.039618924 = weight(_text_:22 in 2151) [ClassicSimilarity], result of:
          0.039618924 = score(doc=2151,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.2708308 = fieldWeight in 2151, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2151)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

We present a model of semantic memory that utilizes a high dimensional semantic space constructed from a co-occurrence matrix. This matrix was formed by analyzing a lot) million word corpus. Word vectors were then obtained by extracting rows and columns of this matrix, These vectors were subjected to multidimensional scaling. Words were found to cluster semantically. suggesting that interword distance may be interpretable as a measure of semantic similarity, In attempting to replicate with our simulation the semantic and ...

Source

Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society: July 22 - 25, 1995, University of Pittsburgh / ed. by Johanna D. Moore and Jill Fain Lehmann

0.014244139 = product of:
  0.035610348 = sum of:
    0.015800884 = weight(_text_:of in 1026) [ClassicSimilarity], result of:
      0.015800884 = score(doc=1026,freq=8.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.24188137 = fieldWeight in 1026, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1026)
    0.019809462 = product of:
      0.039618924 = sum of:
        0.039618924 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
          0.039618924 = score(doc=1026,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.2708308 = fieldWeight in 1026, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1026)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

We have created software applications that allow users to both author and use Semantic Web metadata. To create and use a layer of semantic content on top of the existing Web, we have (1) implemented a user interface that expedites the task of attributing metadata to resources on the Web, and (2) augmented a Web browser to leverage this semantic metadata to provide relevant information and tasks to the user. This project provides a framework for annotating and reorganizing existing files, pages, and sites on the Web that is similar to Vannevar Bushrsquos original concepts of trail blazing and associative indexing.

Source

Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003

0.013670124 = product of:
  0.03417531 = sum of:
    0.008315044 = product of:
      0.041575223 = sum of:
        0.041575223 = weight(_text_:problem in 1520) [ClassicSimilarity], result of:
          0.041575223 = score(doc=1520,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.23447686 = fieldWeight in 1520, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1520)
      0.2 = coord(1/5)
    0.025860265 = weight(_text_:of in 1520) [ClassicSimilarity], result of:
      0.025860265 = score(doc=1520,freq=42.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.39587128 = fieldWeight in 1520, product of:
          6.4807405 = tf(freq=42.0), with freq of:
            42.0 = termFreq=42.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1520)
  0.4 = coord(2/5)

Abstract

This paper discusses issues concerning the augmentation of thesaurus relationships, in light of new application possibilities for retrieval. We first discuss a case study that explored the retrieval potential of an augmented set of thesaurus relationships by specialising standard relationships into richer subtypes, in particular hierarchical geographical containment and the associative relationship. We then locate this work in a broader context by reviewing various attempts to build taxonomies of thesaurus relationships, and conclude by discussing the feasibility of hierarchically augmenting the core set of thesaurus relationships, particularly the associative relationship. We discuss the possibility of enriching the specification and semantics of Related Term (RT relationships), while maintaining compatibility with traditional thesauri via a limited hierarchical extension of the associative (and hierarchical) relationships. This would be facilitated by distinguishing the type of term from the (sub)type of relationship and explicitly specifying semantic categories for terms following a faceted approach. We first illustrate how hierarchical spatial relationships can be used to provide more flexible retrieval for queries incorporating place names in applications employing online gazetteers and geographical thesauri. We then employ a set of experimental scenarios to investigate key issues affecting use of the associative (RT) thesaurus relationships in semantic distance measures. Previous work has noted the potential of RTs in thesaurus search aids but also the problem of uncontrolled expansion of query term sets. Results presented in this paper suggest the potential for taking account of the hierarchical context of an RT link and specialisations of the RT relationship

Source

Journal of digital information. 1(2001) no.8

0.013479257 = product of:
  0.03369814 = sum of:
    0.019548526 = weight(_text_:of in 5697) [ClassicSimilarity], result of:
      0.019548526 = score(doc=5697,freq=24.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2992506 = fieldWeight in 5697, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5697)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 5697) [ClassicSimilarity], result of:
          0.028299233 = score(doc=5697,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 5697, 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=5697)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

The performance of 8 ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. Focuses on the identification of algorithms that most effectively take cognizance of user preferences. user choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the 8 ranking algorithms. This methodology introduces a user oriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, system oriented approaches. Similarities in the performance of the 8 algorithms and the ways these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the r-lohi, wpq, enim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. full text) environments is needed before these results can be generalized beyond the context of the present study

Date

22. 2.1996 13:14:10

0.013374514 = product of:
  0.033436283 = sum of:
    0.009978054 = product of:
      0.04989027 = sum of:
        0.04989027 = weight(_text_:problem in 2178) [ClassicSimilarity], result of:
          0.04989027 = score(doc=2178,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.28137225 = fieldWeight in 2178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=2178)
      0.2 = coord(1/5)
    0.02345823 = weight(_text_:of in 2178) [ClassicSimilarity], result of:
      0.02345823 = score(doc=2178,freq=24.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.3591007 = fieldWeight in 2178, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2178)
  0.4 = coord(2/5)

Abstract

A set of semantic relations is created every time a domain modeler wants to solve some complex problem computationally. These relations are usually organized into ontologies. But three is little standardization of ontologies today, and almost no discussion an ways of comparing relations, of determining a general approach to creating relations, or of modeling in general. This chapter outlines an approach to establishing a general methodology for comparing and justifying sets of relations (and ontologies in general). It first provides several dozen characteristics of ontologies, organized into three taxonomies of increasingly detailed features, by which many essential characteristics of ontologies can be described. These features enable one to compare ontologies at a general level, without studying every concept they contain. But sometimes it is necessary to make detailed comparisons of content. The chapter then illustrates one method for determining salient points for comparison, using algorithms that semi-automatically identify similarities and differences between ontologies.

Source

The semantics of relationships: an interdisciplinary perspective. Eds: Green, R., C.A. Bean u. S.H. Myaeng

0.012797958 = product of:
  0.031994894 = sum of:
    0.017845279 = weight(_text_:of in 1428) [ClassicSimilarity], result of:
      0.017845279 = score(doc=1428,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 1428, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1428)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 1428) [ClassicSimilarity], result of:
          0.028299233 = score(doc=1428,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 1428, 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=1428)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. This article furnishes an account of information inference from a psychologistic stance. By drawing an theories from nonclassical logic and applied cognition, an information inference mechanism is proposed that makes inferences via computations of information flow through an approximation of a conceptual space. Within a conceptual space information is represented geometrically. In this article, geometric representations of words are realized as vectors in a high dimensional semantic space, which is automatically constructed from a text corpus. Two approaches were presented for priming vector representations according to context. The first approach uses a concept combination heuristic to adjust the vector representation of a concept in the light of the representation of another concept. The second approach computes a prototypical concept an the basis of exemplar trace texts and moves it in the dimensional space according to the context. Information inference is evaluated by measuring the effectiveness of query models derived by information flow computations. Results show that information flow contributes significantly to query model effectiveness, particularly with respect to precision. Moreover, retrieval effectiveness compares favorably with two probabilistic query models, and another based an semantic association. More generally, this article can be seen as a contribution towards realizing operational systems that mimic text-based human reasoning.

Date

22. 3.2003 19:35:46

Source

Journal of the American Society for Information Science and technology. 54(2003) no.4, S.321-334