Search (101 results, page 1 of 6)

0.037072238 = product of:
  0.12975283 = sum of:
    0.005178697 = product of:
      0.025893483 = sum of:
        0.025893483 = weight(_text_:retrieval in 566) [ClassicSimilarity], result of:
          0.025893483 = score(doc=566,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23632148 = fieldWeight in 566, 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=566)
      0.2 = coord(1/5)
    0.12457414 = weight(_text_:abfrage in 566) [ClassicSimilarity], result of:
      0.12457414 = score(doc=566,freq=2.0), product of:
        0.28580084 = queryWeight, product of:
          7.890225 = idf(docFreq=44, maxDocs=44218)
          0.03622214 = queryNorm
        0.43587744 = fieldWeight in 566, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.890225 = idf(docFreq=44, maxDocs=44218)
          0.0390625 = fieldNorm(doc=566)
  0.2857143 = coord(2/7)

Abstract

Eine der größten Herausforderungen für alle technischen Anwendungen ist die sogenannte Mensch-Maschine-Schnittstelle, also der Problemkreis, wie der bedienende Mensch mit der zu bedienenden Technik kommunizieren kann. Waren die Benutzungsschnittstellen bis Ende der Achtziger Jahre vor allem durch die Notwendigkeit des Benutzers geprägt, sich an die Erfordernisse der Maschine anzupassen, so wurde mit Durchsetzung grafischer Benutzungsoberflächen zunehmend versucht, die Bedienbarkeit so zu gestalten, dass ein Mensch auch ohne größere Einarbeitung in die Lage versetzt werden sollte, seine Befehle der Technik - letztlich also dem Computer - zu übermitteln. Trotz aller Fortschritte auf diesem Gebiet blieb immer die Anforderung, der Mensch solle auf die ihm natürlichste Art und Weise kommunizieren können, mit menschlicher Sprache. Diese Anforderung gilt gerade auch für das Retrieval von Informationen: Warum ist es nötig, die Nutzung von Booleschen Operatoren zu erlernen, nur um eine Suchanfrage stellen zu können? Ein anderes Thema ist die Frage nach der Visualisierung von Wissenszusammenhängen, die sich der Herausforderung stellt, in einem geradezu uferlos sich ausweitenden Informationsangebot weiterhin den Überblick behalten und relevante Informationen schnellstmöglich finden zu können.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.012472611 = product of:
  0.043654136 = sum of:
    0.028931338 = product of:
      0.072328344 = sum of:
        0.03107218 = weight(_text_:retrieval in 2419) [ClassicSimilarity], result of:
          0.03107218 = score(doc=2419,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.2835858 = fieldWeight in 2419, 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=2419)
        0.041256163 = weight(_text_:system in 2419) [ClassicSimilarity], result of:
          0.041256163 = score(doc=2419,freq=6.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.36163113 = fieldWeight in 2419, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=2419)
      0.4 = coord(2/5)
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
          0.0294456 = score(doc=2419,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 2419, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2419)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

The digital library system Daffodil is targeted at strategic support of users during the information search process. For searching, exploring and managing digital library objects it provides user-customisable information seeking patterns over a federation of heterogeneous digital libraries. In this paper evaluation results with respect to retrieval effectiveness, efficiency and user satisfaction are presented. The analysis focuses on strategic support for the scientific work-flow. Daffodil supports the whole work-flow, from data source selection over information seeking to the representation, organisation and reuse of information. By embedding high level search functionality into the scientific work-flow, the user experiences better strategic system support due to a more systematic work process. These ideas have been implemented in Daffodil followed by a qualitative evaluation. The evaluation has been conducted with 28 participants, ranging from information seeking novices to experts. The results are promising, as they support the chosen model.

Date

16.11.2008 16:22:48

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.012007359 = product of:
  0.042025756 = sum of:
    0.007323784 = product of:
      0.03661892 = sum of:
        0.03661892 = weight(_text_:retrieval in 1352) [ClassicSimilarity], result of:
          0.03661892 = score(doc=1352,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.33420905 = fieldWeight in 1352, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
      0.2 = coord(1/5)
    0.034701973 = product of:
      0.06940395 = sum of:
        0.06940395 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
          0.06940395 = score(doc=1352,freq=4.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.54716086 = fieldWeight in 1352, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Date

22. 2.2003 17:25:39
22. 2.2003 18:17:40

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.009011874 = product of:
  0.031541556 = sum of:
    0.007250175 = product of:
      0.036250874 = sum of:
        0.036250874 = weight(_text_:retrieval in 5295) [ClassicSimilarity], result of:
          0.036250874 = score(doc=5295,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.33085006 = fieldWeight in 5295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5295)
      0.2 = coord(1/5)
    0.024291381 = product of:
      0.048582762 = sum of:
        0.048582762 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
          0.048582762 = score(doc=5295,freq=4.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.2857143 = coord(2/7)

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

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.007866439 = product of:
  0.027532537 = sum of:
    0.015263537 = product of:
      0.03815884 = sum of:
        0.01830946 = weight(_text_:retrieval in 56) [ClassicSimilarity], result of:
          0.01830946 = score(doc=56,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.16710453 = fieldWeight in 56, 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=56)
        0.01984938 = weight(_text_:system in 56) [ClassicSimilarity], result of:
          0.01984938 = score(doc=56,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.17398985 = fieldWeight in 56, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=56)
      0.4 = coord(2/5)
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 56) [ClassicSimilarity], result of:
          0.024538001 = score(doc=56,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.19345059 = fieldWeight in 56, 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=56)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.

Date

22. 7.2006 16:32:43

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.006979079 = product of:
  0.024426775 = sum of:
    0.007250175 = product of:
      0.036250874 = sum of:
        0.036250874 = weight(_text_:retrieval in 4324) [ClassicSimilarity], result of:
          0.036250874 = score(doc=4324,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.33085006 = fieldWeight in 4324, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4324)
      0.2 = coord(1/5)
    0.0171766 = product of:
      0.0343532 = sum of:
        0.0343532 = weight(_text_:22 in 4324) [ClassicSimilarity], result of:
          0.0343532 = score(doc=4324,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.2708308 = fieldWeight in 4324, 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=4324)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Date

11. 2.2011 18:22:25

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0063723573 = product of:
  0.02230325 = sum of:
    0.0051266486 = product of:
      0.025633242 = sum of:
        0.025633242 = weight(_text_:retrieval in 1026) [ClassicSimilarity], result of:
          0.025633242 = score(doc=1026,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23394634 = fieldWeight in 1026, 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=1026)
      0.2 = coord(1/5)
    0.0171766 = product of:
      0.0343532 = sum of:
        0.0343532 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
          0.0343532 = score(doc=1026,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.2857143 = coord(2/7)

Source

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

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0063723573 = product of:
  0.02230325 = sum of:
    0.0051266486 = product of:
      0.025633242 = sum of:
        0.025633242 = weight(_text_:retrieval in 1852) [ClassicSimilarity], result of:
          0.025633242 = score(doc=1852,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23394634 = fieldWeight in 1852, 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=1852)
      0.2 = coord(1/5)
    0.0171766 = product of:
      0.0343532 = sum of:
        0.0343532 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
          0.0343532 = score(doc=1852,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.2708308 = fieldWeight in 1852, 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=1852)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Date

11. 2.2011 18:22:58

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.005317596 = product of:
  0.018611584 = sum of:
    0.0063425824 = product of:
      0.031712912 = sum of:
        0.031712912 = weight(_text_:retrieval in 1428) [ClassicSimilarity], result of:
          0.031712912 = score(doc=1428,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.28943354 = fieldWeight in 1428, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1428)
      0.2 = coord(1/5)
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 1428) [ClassicSimilarity], result of:
          0.024538001 = score(doc=1428,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.2857143 = coord(2/7)

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

Footnote

Beitrag eines Themenheftes: Mathematical, logical, and formal methods in information retrieval

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.004589508 = product of:
  0.032126553 = sum of:
    0.032126553 = product of:
      0.08031638 = sum of:
        0.02197135 = weight(_text_:retrieval in 202) [ClassicSimilarity], result of:
          0.02197135 = score(doc=202,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.20052543 = fieldWeight in 202, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=202)
        0.058345027 = weight(_text_:system in 202) [ClassicSimilarity], result of:
          0.058345027 = score(doc=202,freq=12.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.51142365 = fieldWeight in 202, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=202)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

The authors present a model of information searching in thesaurus-enhanced search systems, intended as a reference model for system developers. The model focuses on user-system interaction and charts the specific stages of searching an indexed collection with a thesaurus. It was developed based on literature, findings from empirical studies, and analysis of existing systems. The model describes in detail the entities, processes, and decisions when interacting with a search system augmented with a thesaurus. A basic search scenario illustrates this process through the model. Graphical and textual depictions of the model are complemented by a concise matrix representation for evaluation purposes. Potential problems at different stages of the search process are discussed, together with possibilities for system developers. The aim is to set out a framework of processes, decisions, and risks involved in thesaurus-based search, within which system developers can consider potential avenues for support.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.004168497 = product of:
  0.029179478 = sum of:
    0.029179478 = product of:
      0.072948694 = sum of:
        0.049129434 = weight(_text_:retrieval in 1201) [ClassicSimilarity], result of:
          0.049129434 = score(doc=1201,freq=10.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.44838852 = fieldWeight in 1201, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=1201)
        0.023819257 = weight(_text_:system in 1201) [ClassicSimilarity], result of:
          0.023819257 = score(doc=1201,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 1201, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=1201)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

Thesaural relations have long been used in information retrieval to enrich queries; they have sometimes been used to cluster documents as well. Sometimes the first query to an information retrieval system yields no results at all, or, what can be even more disconcerting, many thousands of hits. One solution is to rephrase the query, improving the choice of query terms by using related terms of different types. A collection of related terms is often called a thesaurus. This chapter describes the lexical-semantic relations that have been used in building thesauri and summarizes some of the effects of using these relational thesauri in information retrieval experiments

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.004080667 = product of:
  0.028564667 = sum of:
    0.028564667 = product of:
      0.07141167 = sum of:
        0.031712912 = weight(_text_:retrieval in 85) [ClassicSimilarity], result of:
          0.031712912 = score(doc=85,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.28943354 = fieldWeight in 85, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=85)
        0.03969876 = weight(_text_:system in 85) [ClassicSimilarity], result of:
          0.03969876 = score(doc=85,freq=8.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.3479797 = fieldWeight in 85, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=85)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

The amount of knowledge accumulated in published scientific papers has increased due to the continuing progress being made in scientific research. Since numerous papers have only reported fragments of scientific facts, there are possibilities for discovering new knowledge by connecting these facts. We therefore developed a system called BioTermNet to draft a conceptual network with hybrid methods of information extraction and information retrieval. Two concepts are regarded as related in this system if (a) their relationship is clearly described in MEDLINE abstracts or (b) they have distinctively co-occurred in abstracts. PRIME data, including protein interactions and functions extracted by NLP techniques, are used in the former, and the Singhalmeasure for information retrieval is used in the latter. Relationships that are not clearly or directly described in an abstract can be extracted by connecting multiple concepts. To evaluate how well this system performs, Swanson's association between Raynaud's disease and fish oil and that between migraine and magnesium were tested with abstracts that had been published before the discovery of these associations. The result was that when start and end concepts were given, plausible and understandable intermediate concepts connecting them could be detected. When only the start concept was given, not only the focused concept (magnesium and fish oil) but also other probable concepts could be detected as related concept candidates. Finally, this system was applied to find diseases related to the BRCA1 gene. Some other new potentially related diseases were detected along with diseases whose relations to BRCA1 were already known.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0038721121 = product of:
  0.027104784 = sum of:
    0.027104784 = product of:
      0.06776196 = sum of:
        0.0439427 = weight(_text_:retrieval in 4708) [ClassicSimilarity], result of:
          0.0439427 = score(doc=4708,freq=8.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.40105087 = fieldWeight in 4708, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=4708)
        0.023819257 = weight(_text_:system in 4708) [ClassicSimilarity], result of:
          0.023819257 = score(doc=4708,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 4708, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=4708)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontologybased KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0038245893 = product of:
  0.026772125 = sum of:
    0.026772125 = product of:
      0.06693031 = sum of:
        0.01830946 = weight(_text_:retrieval in 4575) [ClassicSimilarity], result of:
          0.01830946 = score(doc=4575,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.16710453 = fieldWeight in 4575, 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=4575)
        0.048620854 = weight(_text_:system in 4575) [ClassicSimilarity], result of:
          0.048620854 = score(doc=4575,freq=12.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.42618635 = fieldWeight in 4575, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4575)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

The Web provides a massive knowledge source, as do intranets and other electronic document collections. However, much of that knowledge is encoded implicitly and cannot be applied directly without processing into some more appropriate structures. Searching, browsing, question answering, for example, could all benefit from domain-specific knowledge contained in the documents, and in applications such as simple search we do not actually need very "deep" knowledge structures such as ontologies, but we can get a long way with a model of the domain that consists of term hierarchies. We combine domain knowledge automatically acquired by exploiting the documents' markup structure with knowledge extracted an the fly to assist a user with ad hoc search requests. Such a search system can suggest query modification options derived from the actual data and thus guide a user through the space of documents. This article gives a detailed account of a task-based evaluation that compares a search system that uses the outlined domain knowledge with a standard search system. We found that users do use the query modification suggestions proposed by the system. The main conclusion we can draw from this evaluation, however, is that users prefer a system that can suggest query modifications over a standard search engine, which simply presents a ranked list of documents. Most interestingly, we observe this user preference despite the fact that the baseline system even performs slightly better under certain criteria.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.003641347 = product of:
  0.012744714 = sum of:
    0.0029295133 = product of:
      0.014647567 = sum of:
        0.014647567 = weight(_text_:retrieval in 1163) [ClassicSimilarity], result of:
          0.014647567 = score(doc=1163,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.13368362 = fieldWeight in 1163, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=1163)
      0.2 = coord(1/5)
    0.0098152 = product of:
      0.0196304 = sum of:
        0.0196304 = weight(_text_:22 in 1163) [ClassicSimilarity], result of:
          0.0196304 = score(doc=1163,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.2857143 = coord(2/7)

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]

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0034503236 = product of:
  0.024152264 = sum of:
    0.024152264 = product of:
      0.06038066 = sum of:
        0.027464187 = weight(_text_:retrieval in 42) [ClassicSimilarity], result of:
          0.027464187 = score(doc=42,freq=18.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.25065678 = fieldWeight in 42, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.01953125 = fieldNorm(doc=42)
        0.032916475 = weight(_text_:system in 42) [ClassicSimilarity], result of:
          0.032916475 = score(doc=42,freq=22.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.28852955 = fieldWeight in 42, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.01953125 = fieldNorm(doc=42)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Footnote

Am interessantesten und wichtigsten erschien mir der Grundsatzartikel von Peter Ingwersen und Kalervo Järvelin (Kopenhagen/Tampere), The sense of information: Understanding the cognitive conditional information concept in relation to information acquisition (S. 7-19). Hier versuchen die Autoren, den ursprünglich von Ingwersen1 vorgeschlagenen und damals ausschliesslich im Zusammenhang mit dem interaktiven Information Retrieval verwendeten Begriff "conditional cognitive information" anhand eines erweiterten Modells nicht nur auf das Gesamtgebiet von "information seeking and retrieval" (IS&R) auszuweiten, sondern auch auf den menschlichen Informationserwerb aus der Sinneswahrnehmung, wie z.B. im Alltag oder im Rahmen der wissenschaftlichen Erkenntnistätigkeit. Dabei werden auch alternative Informationsbegriffe sowie die Beziehung von Information und Bedeutung diskutiert. Einen ebenfalls auf Ingwersen zurückgehenden Ansatz thematisiert der Beitrag von Birger Larsen (Kopenhagen), indem er sich mit dessen vor über 10 Jahren veröffentlichten2 Principle of Polyrepresentation befasst. Dieses beruht auf der Hypothese, wonach die Überlappung zwischen unterschiedlichen kognitiven Repräsentationen - nämlich jenen der Situation des Informationssuchenden und der Dokumente - zur Reduktion der einer Retrievalsituation anhaftenden Unsicherheit und damit zur Verbesserung der Performance des IR-Systems genutzt werden könne. Das Prinzip stellt die Dokumente, ihre Autoren und Indexierer, aber auch die sie zugänglich machende IT-Lösung in einen umfassenden und kohärenten theoretischen Bezugsrahmen, der die benutzerorientierte Forschungsrichtung "Information-Seeking" mit der systemorientierten IR-Forschung zu integrieren trachtet. Auf der Basis theoretischer Überlegungen sowie der (wenigen) dazu vorliegenden empirischen Studien hält Larsen das Model, das von Ingwersen sowohl für "exact match-IR" als auch für "best match-IR" intendiert war, allerdings schon in seinen Grundzügen für "Boolean" (d.h. "exact match"-orientiert) und schlägt ein "polyrepresentation continuum" als Verbesserungsmöglichkeit vor.
Mehrere Beiträge befassen sich mit dem Problem der Relevanz. Erica Cosijn und Theo Bothma (Pretoria) argumentieren, dass für das Benutzerverhalten neben der thematischen Relevanz auch verschiedene andere Relevanzdimensionen eine Rolle spielen und schlagen auf der Basis eines (abermals auf Ingwersen zurückgehenden) erweiterten Relevanzmodells vor, dass IR-Systeme die Möglichkeit zur Abgabe auch kognitiver, situativer und sozio-kognitiver Relevanzurteile bieten sollten. Elaine Toms et al. (Kanada) berichten von einer Studie, in der versucht wurde, die schon vor 30 Jahren von Tefko Saracevic3 erstellten fünf Relevanzdimensionen (kognitiv, motivational, situativ, thematisch und algorithmisch) zu operationalisieren und anhand von Recherchen mit einer Web-Suchmaschine zu untersuchen. Die Ergebnisse zeigten, dass sich diese fünf Dimensionen in drei Typen vereinen lassen, die Benutzer, System und Aufgabe repräsentieren. Von einer völlig anderen Seite nähern sich Olof Sundin und Jenny Johannison (Boras, Schweden) der Relevanzthematik, indem sie einen kommunikationsorientierten, neo-pragmatistischen Ansatz (nach Richard Rorty) wählen, um Informationssuche und Relevanz zu analysieren, und dabei auch auf das Werk von Michel Foucault zurückgreifen. Weitere interessante Artikel befassen sich mit Bradford's Law of Scattering (Hjørland & Nicolaisen), Information Sharing and Timing (Widén-Wulff & Davenport), Annotations as Context for Searching Documents (Agosti & Ferro), sowie dem Nutzen von neuen Informationsquellen wie Web Links, Newsgroups und Blogs für die sozial- und informationswissenschaftliche Forschung (Thelwall & Wouters). In Summe liegt hier ein interessantes und anspruchsvolles Buch vor - inhaltlich natürlich nicht gerade einheitlich und geschlossen, doch dies darf man bei einem Konferenzband ohnedies nicht erwarten. Manche der abgedruckten Beiträge sind sicher nicht einfach zu lesen, lohnen aber die Mühe. Auch für Praktiker aus Bibliothek und Information ist einiges dabei, sofern sie sich für die wissenschaftliche Basis ihrer Tätigkeit interessieren. Fachlich einschlägige Spezial- und grössere Allgemeinbibliotheken sollten das Werk daher unbedingt führen.

RSWK

Informationssystem / Navigieren / Kontextbezogenes System / Kongress / Glasgow <2005>
Information Retrieval / Kontextbezogenes System / Kongress / Glasgow <2005>
Information-Retrieval-System / Kontextbezogenes System / Kongress / Glasgow <2005>
Elektronische Bibliothek / Information Retrieval / Relevanz-Feedback / Kontextbezogenes System / Kongress / Glasgow <2005>

Subject

Informationssystem / Navigieren / Kontextbezogenes System / Kongress / Glasgow <2005>
Information Retrieval / Kontextbezogenes System / Kongress / Glasgow <2005>
Information-Retrieval-System / Kontextbezogenes System / Kongress / Glasgow <2005>
Elektronische Bibliothek / Information Retrieval / Relevanz-Feedback / Kontextbezogenes System / Kongress / Glasgow <2005>

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.003444207 = product of:
  0.024109447 = sum of:
    0.024109447 = product of:
      0.060273618 = sum of:
        0.025893483 = weight(_text_:retrieval in 624) [ClassicSimilarity], result of:
          0.025893483 = score(doc=624,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23632148 = fieldWeight in 624, 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=624)
        0.034380134 = weight(_text_:system in 624) [ClassicSimilarity], result of:
          0.034380134 = score(doc=624,freq=6.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.30135927 = fieldWeight in 624, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=624)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

With RetrievalWare 8.0(TM) the American company Convera offers an elaborated software in the range of Information Retrieval, Information Indexing and Knowledge Management. Convera promises the possibility of handling different file formats in many different languages. Regarding comparable products one innovation is to be stressed particularly: the possibility of the preparation as well as integration of an ontology. One tool of the software package is useful in order to produce ontologies manually, to process existing ontologies and to import the very. The processing of search results is also to be mentioned. By means of categorization strategies search results can be classified dynamically and presented in personalized representations. This study presents an evaluation of the functions and components of the system. Technological aspects and modes of operation under the surface of Convera RetrievalWare will be analysed, with a focus on the creation of libraries and thesauri, and the problems posed by the integration of an existing thesaurus. Broader aspects such as usability and system ergonomics are integrated in the examination as well.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.00322676 = product of:
  0.02258732 = sum of:
    0.02258732 = product of:
      0.0564683 = sum of:
        0.03661892 = weight(_text_:retrieval in 5962) [ClassicSimilarity], result of:
          0.03661892 = score(doc=5962,freq=8.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.33420905 = fieldWeight in 5962, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5962)
        0.01984938 = weight(_text_:system in 5962) [ClassicSimilarity], result of:
          0.01984938 = score(doc=5962,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.17398985 = fieldWeight in 5962, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5962)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

Seit 3. Juli 2006 stellt das ZPID eine verbesserte Suchoberfläche für die Recherche in der bibliographischen Psychologie-Datenbank PSYNDEX zur Verfügung. Hauptmerkmal der neuen Version 1.1 des 'ZPID-Retrieval für PSYNDEX' ist die Einbindung von 'PSYNDEX Terms', dem kontrollierten Wortschatz der psychologischen Fachsprache. PSYNDEX Terms basiert auf dem 'Thesaurus of Psychological Index Terms' der American Psychological Association (APA) und enthält im Moment über 5.400 Deskriptoren. Zu jedem Deskriptor werden ggf. Oberbegriffe, Unterbegriffe und verwandte Begriffe angezeigt. Wer die Suchoberfläche nutzt, kann entweder im Thesaurus blättern oder gezielt nach Thesaurusbegriffen suchen. Kommt der eigene frei gewählte Suchbegriff nicht im Thesaurus vor, macht das System selbsttätig Vorschläge für passende Thesaurusbegriffe. DerThesaurus ist komplett zweisprachig (deutsch/englisch) implementiert, sodass er auch als Übersetzungshilfe dient. Weitere Verbesserungen der Suchoberfläche betreffen die Darstellbarkeit in unterschiedlichen Web-Browsern mit dem Ziel der Barrierefreiheit, die Erweiterung der OnlineHilfe mit Beispielen für erfolgreiche Suchstrategien, die Möglichkeit, zu speziellen Themen vertiefte Informationen abzurufen (den Anfang machen psychologische Behandlungsprogramme) und die Bereitstellung eines Export-Filters für EndNote. Zielgruppe des ZPID-Retrieval sind Einzelpersonen, die keinen institutionellen PSYNDEX-Zugang, z.B. am Campus einer Universität, nutzen können. Sie können das kostenpflichtige Retrieval direkt online erwerben und werden binnen weniger Minuten freigeschaltet. Kunden mit existierendem Vertrag kommen automatisch in den Genuss der verbesserten Suchoberfläche.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0030837 = product of:
  0.0215859 = sum of:
    0.0215859 = product of:
      0.05396475 = sum of:
        0.025893483 = weight(_text_:retrieval in 2579) [ClassicSimilarity], result of:
          0.025893483 = score(doc=2579,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23632148 = fieldWeight in 2579, 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=2579)
        0.028071264 = weight(_text_:system in 2579) [ClassicSimilarity], result of:
          0.028071264 = score(doc=2579,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.24605882 = fieldWeight in 2579, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2579)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

An experiment was conducted to see how relevance feedback could be used to build and adjust profiles to improve the performance of filtering systems. Data was collected during the system interaction of 18 graduate students with SIFTER (Smart Information Filtering Technology for Electronic Resources), a filtering system that ranks incoming information based on users' profiles. The data set came from a collection of 6000 records concerning consumer health. In the first phase of the study, three different modes of profile acquisition were compared. The explicit mode allowed users to directly specify the profile; the implicit mode utilized relevance feedback to create and refine the profile; and the combined mode allowed users to initialize the profile and to continuously refine it using relevance feedback. Filtering performance, measured in terms of Normalized Precision, showed that the three approaches were significantly different ( [small alpha, Greek] =0.05 and p =0.012). The explicit mode of profile acquisition consistently produced superior results. Exclusive reliance on relevance feedback in the implicit mode resulted in inferior performance. The low performance obtained by the implicit acquisition mode motivated the second phase of the study, which aimed to clarify the role of context in relevance feedback judgments. An inductive content analysis of thinking aloud protocols showed dimensions that were highly situational, establishing the importance context plays in feedback relevance assessments. Results suggest the need for better representation of documents, profiles, and relevance feedback mechanisms that incorporate dimensions identified in this research.

Footnote

Beitrag in einem Themenheft: "Issues of context in information retrieval (IR)"

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0030213965 = product of:
  0.021149775 = sum of:
    0.021149775 = product of:
      0.05287444 = sum of:
        0.02537033 = weight(_text_:retrieval in 1254) [ClassicSimilarity], result of:
          0.02537033 = score(doc=1254,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23154683 = fieldWeight in 1254, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=1254)
        0.027504109 = weight(_text_:system in 1254) [ClassicSimilarity], result of:
          0.027504109 = score(doc=1254,freq=6.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.24108742 = fieldWeight in 1254, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=1254)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

Knowledge management in digital libraries is a universal problem. Keyword-based searching is applied everywhere no matter whether the resources are indexed databases or full-text Web pages. In keyword matching, the valuable content description and indexing of the metadata, such as the subject descriptors and the classification notations, are merely treated as common keywords to be matched with the user query. Without the support of vocabulary control tools, such as classification systems and thesauri, the intelligent labor of content analysis, description and indexing in metadata production are seriously wasted. New retrieval paradigms are needed to exploit the potential of the metadata resources. Could classification and thesauri, which contain the condensed intelligence of generations of librarians, be used in a digital library to organize the networked information, especially metadata, to facilitate their usability and change the digital library into a knowledge management environment? To examine that question, we designed and implemented a new paradigm that incorporates a classification system, a thesaurus and metadata. The classification and the thesaurus are merged into a concept network, and the metadata are distributed into the nodes of the concept network according to their subjects. The abstract concept node instantiated with the related metadata records becomes a knowledge node. A coherent and consistent knowledge network is thus formed. It is not only a framework for resource organization but also a structure for knowledge navigation, retrieval and learning. We have built an experimental system based on the Chinese Classification and Thesaurus, which is the most comprehensive and authoritative in China, and we have incorporated more than 5000 bibliographic records in the computing domain from the Peking University Library. The result is encouraging. In this article, we review the tools, the architecture and the implementation of our experimental system, which is called Vision.

Theme

Semantisches Umfeld in Indexierung u. Retrieval