Search (153 results, page 1 of 8)

0.084764846 = product of:
  0.14127474 = sum of:
    0.00770594 = weight(_text_:a in 1594) [ClassicSimilarity], result of:
      0.00770594 = score(doc=1594,freq=4.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.14413087 = fieldWeight in 1594, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=1594)
    0.12725374 = weight(_text_:91 in 1594) [ClassicSimilarity], result of:
      0.12725374 = score(doc=1594,freq=2.0), product of:
        0.25837386 = queryWeight, product of:
          5.5722036 = idf(docFreq=456, maxDocs=44218)
          0.046368346 = queryNorm
        0.49251786 = fieldWeight in 1594, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.5722036 = idf(docFreq=456, maxDocs=44218)
          0.0625 = fieldNorm(doc=1594)
    0.006315058 = product of:
      0.012630116 = sum of:
        0.012630116 = weight(_text_:information in 1594) [ClassicSimilarity], result of:
          0.012630116 = score(doc=1594,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.1551638 = fieldWeight in 1594, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0625 = fieldNorm(doc=1594)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Source

Information - Wissenschaft und Praxis. 59(2008) H.2, S.91-99

Type

a

0.05006103 = product of:
  0.08343504 = sum of:
    0.009823184 = weight(_text_:a in 4404) [ClassicSimilarity], result of:
      0.009823184 = score(doc=4404,freq=26.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.18373153 = fieldWeight in 4404, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.03125 = fieldNorm(doc=4404)
    0.06362687 = weight(_text_:91 in 4404) [ClassicSimilarity], result of:
      0.06362687 = score(doc=4404,freq=2.0), product of:
        0.25837386 = queryWeight, product of:
          5.5722036 = idf(docFreq=456, maxDocs=44218)
          0.046368346 = queryNorm
        0.24625893 = fieldWeight in 4404, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.5722036 = idf(docFreq=456, maxDocs=44218)
          0.03125 = fieldNorm(doc=4404)
    0.009984984 = product of:
      0.019969968 = sum of:
        0.019969968 = weight(_text_:information in 4404) [ClassicSimilarity], result of:
          0.019969968 = score(doc=4404,freq=20.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.2453355 = fieldWeight in 4404, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=4404)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Significant progress has been made in technologies for publishing and distributing knowledge and information on the web. However, much of the published information is not organized, and it is hard to find answers to questions that require more than a keyword search. In general, one can say that the web is organizing itself. Information is often published in relatively ad hoc fashion. Typically, concern about the presentation of content has been limited to purely layout issues. This, combined with the fact that the representation language used on the World Wide Web (HTML) is mainly format-oriented, makes publishing on the WWW easy, giving it an enormous expressiveness. People add private, educational or organizational content to the web that is of an immensely diverse nature. Content on the web is growing closer to a real universal knowledge base, with one problem relatively undefined; the problem of the interpretation of its contents. Although widely acknowledged for its general and universal advantages, the increasing popularity of the web also shows us some major drawbacks. The developments of the information content on the web during the last year alone, clearly indicates the need for some changes. Perhaps one of the most significant problems with the web as a distributed information system is the difficulty of finding and comparing information.
Thus, there is a clear need for the web to become more semantic. The aim of introducing semantics into the web is to enhance the precision of search, but also enable the use of logical reasoning on web contents in order to answer queries. The CORPORUM OntoBuilder toolset is developed specifically for this task. It consists of a set of applications that can fulfil a variety of tasks, either as stand-alone tools, or augmenting each other. Important tasks that are dealt with by CORPORUM are related to document and information retrieval (find relevant documents, or support the user finding them), as well as information extraction (building a knowledge base from web documents to answer queries), information dissemination (summarizing strategies and information visualization), and automated document classification strategies. First versions of the toolset are encouraging in that they show large potential as a supportive technology for building up the Semantic Web. In this chapter, methods for transforming the current web into a semantic web are discussed, as well as a technical solution that can perform this task: the CORPORUM tool set. First, the toolset is introduced; followed by some pragmatic issues relating to the approach; then there will be a short overview of the theory in relation to CognIT's vision; and finally, a discussion on some of the applications that arose from the project.

Pages

S.91-115

Type

a

0.027656192 = product of:
  0.06914048 = sum of:
    0.009535614 = weight(_text_:a in 5095) [ClassicSimilarity], result of:
      0.009535614 = score(doc=5095,freq=8.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 5095, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5095)
    0.05960487 = sum of:
      0.015628971 = weight(_text_:information in 5095) [ClassicSimilarity], result of:
        0.015628971 = score(doc=5095,freq=4.0), product of:
          0.08139861 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046368346 = queryNorm
          0.1920054 = fieldWeight in 5095, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.0546875 = fieldNorm(doc=5095)
      0.043975897 = weight(_text_:22 in 5095) [ClassicSimilarity], result of:
        0.043975897 = score(doc=5095,freq=2.0), product of:
          0.16237405 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046368346 = queryNorm
          0.2708308 = fieldWeight in 5095, 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=5095)
  0.4 = coord(2/5)

Abstract

This paper presents an abstract formalization of the notion of "facets". Facets are relational structures of units, relations and other facets selected for a certain purpose. Facets can be used to structure large knowledge representation systems into a hierarchical arrangement of consistent and independent subsystems (facets) that facilitate flexibility and combinations of different viewpoints or aspects. This paper describes the basic notions, facet characteristics and construction mechanisms. It then explicates the theory in an example of a faceted information retrieval system (FaIR)

Date

22. 1.2016 17:47:06

Type

a

0.026819343 = product of:
  0.067048356 = sum of:
    0.008173384 = weight(_text_:a in 3261) [ClassicSimilarity], result of:
      0.008173384 = score(doc=3261,freq=8.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.15287387 = fieldWeight in 3261, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=3261)
    0.058874972 = sum of:
      0.02118135 = weight(_text_:information in 3261) [ClassicSimilarity], result of:
        0.02118135 = score(doc=3261,freq=10.0), product of:
          0.08139861 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046368346 = queryNorm
          0.2602176 = fieldWeight in 3261, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046875 = fieldNorm(doc=3261)
      0.037693623 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
        0.037693623 = score(doc=3261,freq=2.0), product of:
          0.16237405 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046368346 = queryNorm
          0.23214069 = fieldWeight in 3261, 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=3261)
  0.4 = coord(2/5)

Abstract

Ontologies improve IR systems regarding its retrieval and presentation of information, which make the task of finding information more effective, efficient, and interactive. In this paper we argue that ontologies also greatly improve the engineering of such systems. We created a framework that uses ontology to drive the process of engineering an IR system. We developed a prototype that shows how a domain specialist without knowledge in the IR field can build an IR system with interactive components. The resulting system provides support for users not only to find their information needs but also to extend their state of knowledge. This way, our approach to ontology-enabled information retrieval addresses both the engineering aspect described here and also the usability aspect described elsewhere.

Date

28.11.2016 12:43:22

Type

a

0.024290197 = product of:
  0.06072549 = sum of:
    0.004086692 = weight(_text_:a in 4820) [ClassicSimilarity], result of:
      0.004086692 = score(doc=4820,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.07643694 = fieldWeight in 4820, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=4820)
    0.0566388 = sum of:
      0.018945174 = weight(_text_:information in 4820) [ClassicSimilarity], result of:
        0.018945174 = score(doc=4820,freq=8.0), product of:
          0.08139861 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046368346 = queryNorm
          0.23274569 = fieldWeight in 4820, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046875 = fieldNorm(doc=4820)
      0.037693623 = weight(_text_:22 in 4820) [ClassicSimilarity], result of:
        0.037693623 = score(doc=4820,freq=2.0), product of:
          0.16237405 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046368346 = queryNorm
          0.23214069 = fieldWeight in 4820, 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=4820)
  0.4 = coord(2/5)

Abstract

One of the major problems facing systems for Computer Aided Design (CAD), Architecture Engineering and Construction (AEC) and Geographic Information Systems (GIS) applications today is the lack of interoperability among the various systems. When integrating software applications, substantial di culties can arise in translating information from one application to the other. In this paper, we focus on semantic di culties that arise in software integration. Applications may use di erent terminologies to describe the same domain. Even when appli-cations use the same terminology, they often associate di erent semantics with the terms. This obstructs information exchange among applications. To cir-cumvent this obstacle, we need some way of explicitly specifying the semantics for each terminology in an unambiguous fashion. Ontologies can provide such specification. It will be the task of this paper to explain what ontologies are and how they can be used to facilitate interoperability between software systems used in computer aided design, architecture engineering and construction, and geographic information processing.

Date

3.12.2016 18:39:22

Type

a

0.023918023 = product of:
  0.05979506 = sum of:
    0.004767807 = weight(_text_:a in 1852) [ClassicSimilarity], result of:
      0.004767807 = score(doc=1852,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.089176424 = fieldWeight in 1852, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1852)
    0.05502725 = sum of:
      0.011051352 = weight(_text_:information in 1852) [ClassicSimilarity], result of:
        0.011051352 = score(doc=1852,freq=2.0), product of:
          0.08139861 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046368346 = queryNorm
          0.13576832 = fieldWeight in 1852, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1852)
      0.043975897 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
        0.043975897 = score(doc=1852,freq=2.0), product of:
          0.16237405 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046368346 = 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)
  0.4 = coord(2/5)

Date

11. 2.2011 18:22:58

Source

Information - Wissenschaft und Praxis. 56(2005) H.5/6, S.281-290

Type

a

0.023705307 = product of:
  0.059263267 = sum of:
    0.008173384 = weight(_text_:a in 2623) [ClassicSimilarity], result of:
      0.008173384 = score(doc=2623,freq=8.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.15287387 = fieldWeight in 2623, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2623)
    0.051089883 = sum of:
      0.013396261 = weight(_text_:information in 2623) [ClassicSimilarity], result of:
        0.013396261 = score(doc=2623,freq=4.0), product of:
          0.08139861 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046368346 = queryNorm
          0.16457605 = fieldWeight in 2623, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046875 = fieldNorm(doc=2623)
      0.037693623 = weight(_text_:22 in 2623) [ClassicSimilarity], result of:
        0.037693623 = score(doc=2623,freq=2.0), product of:
          0.16237405 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046368346 = queryNorm
          0.23214069 = fieldWeight in 2623, 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=2623)
  0.4 = coord(2/5)

Abstract

Contemporary retrieval systems, which search across collections, usually ignore collection-level metadata. Alternative approaches, exploiting collection-level information, will require an understanding of the various kinds of relationships that can obtain between collection-level and item-level metadata. This paper outlines the problem and describes a project that is developing a logic-based framework for classifying collection/item metadata relationships. This framework will support (i) metadata specification developers defining metadata elements, (ii) metadata creators describing objects, and (iii) system designers implementing systems that take advantage of collection-level metadata. We present three examples of collection/item metadata relationship categories, attribute/value-propagation, value-propagation, and value-constraint and show that even in these simple cases a precise formulation requires modal notions in addition to first-order logic. These formulations are related to recent work in information retrieval and ontology evaluation.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Type

a

0.02207063 = product of:
  0.055176575 = sum of:
    0.004086692 = weight(_text_:a in 3387) [ClassicSimilarity], result of:
      0.004086692 = score(doc=3387,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.07643694 = fieldWeight in 3387, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=3387)
    0.051089883 = sum of:
      0.013396261 = weight(_text_:information in 3387) [ClassicSimilarity], result of:
        0.013396261 = score(doc=3387,freq=4.0), product of:
          0.08139861 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046368346 = queryNorm
          0.16457605 = fieldWeight in 3387, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046875 = fieldNorm(doc=3387)
      0.037693623 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
        0.037693623 = score(doc=3387,freq=2.0), product of:
          0.16237405 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046368346 = queryNorm
          0.23214069 = fieldWeight in 3387, 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=3387)
  0.4 = coord(2/5)

Abstract

Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.

Date

1. 8.2010 12:35:22

Type

a

0.012231203 = product of:
  0.030578006 = sum of:
    0.005448922 = weight(_text_:a in 3376) [ClassicSimilarity], result of:
      0.005448922 = score(doc=3376,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.10191591 = fieldWeight in 3376, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=3376)
    0.025129084 = product of:
      0.050258167 = sum of:
        0.050258167 = weight(_text_:22 in 3376) [ClassicSimilarity], result of:
          0.050258167 = score(doc=3376,freq=2.0), product of:
            0.16237405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046368346 = queryNorm
            0.30952093 = fieldWeight in 3376, 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=3376)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Date

31. 7.2010 16:58:22

Type

a

0.010808079 = product of:
  0.027020195 = sum of:
    0.008173384 = weight(_text_:a in 2418) [ClassicSimilarity], result of:
      0.008173384 = score(doc=2418,freq=8.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.15287387 = fieldWeight in 2418, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2418)
    0.018846812 = product of:
      0.037693623 = sum of:
        0.037693623 = weight(_text_:22 in 2418) [ClassicSimilarity], result of:
          0.037693623 = score(doc=2418,freq=2.0), product of:
            0.16237405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046368346 = queryNorm
            0.23214069 = fieldWeight in 2418, 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=2418)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Integrated digital access to multiple collections is a prominent issue for many Cultural Heritage institutions. The metadata describing diverse collections must be interoperable, which requires aligning the controlled vocabularies that are used to annotate objects from these collections. In this paper, we present an experiment where we match the vocabularies of two collections by applying the Knowledge Representation techniques established in recent Semantic Web research. We discuss the steps that are required for such matching, namely formalising the initial resources using Semantic Web languages, and running ontology mapping tools on the resulting representations. In addition, we present a prototype that enables the user to browse the two collections using the obtained alignment while still providing her with the original vocabulary structures.

Source

Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006

Type

a

0.010527806 = product of:
  0.026319515 = sum of:
    0.012923255 = weight(_text_:a in 2369) [ClassicSimilarity], result of:
      0.012923255 = score(doc=2369,freq=20.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.24171482 = fieldWeight in 2369, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2369)
    0.013396261 = product of:
      0.026792523 = sum of:
        0.026792523 = weight(_text_:information in 2369) [ClassicSimilarity], result of:
          0.026792523 = score(doc=2369,freq=16.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.3291521 = fieldWeight in 2369, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=2369)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

As information becomes richer and more complex, alternative information-organization methods are needed to more effectively and efficiently retrieve information from various systems, including the Web. The objective of this study is to explore how a Topic Maps-based ontology approach affects users' searching performance. Forty participants participated in a task-based evaluation where two dependent variables, recall and search time, were measured. The results of this study indicate that a Topic Maps-based ontology information retrieval (TOIR) system has a significant and positive effect on both recall and search time, compared to a thesaurus-based information retrieval (TIR) system. These results suggest that the inclusion of a Topic Maps-based ontology is a beneficial approach to take when designing information retrieval systems.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.12, S.1898-1911

Type

a

0.010376932 = product of:
  0.02594233 = sum of:
    0.008173384 = weight(_text_:a in 2654) [ClassicSimilarity], result of:
      0.008173384 = score(doc=2654,freq=18.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.15287387 = fieldWeight in 2654, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.03125 = fieldNorm(doc=2654)
    0.017768946 = product of:
      0.03553789 = sum of:
        0.03553789 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.03553789 = score(doc=2654,freq=4.0), product of:
            0.16237405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046368346 = queryNorm
            0.21886435 = fieldWeight in 2654, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=2654)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Type

a

0.009683453 = product of:
  0.024208631 = sum of:
    0.010812371 = weight(_text_:a in 277) [ClassicSimilarity], result of:
      0.010812371 = score(doc=277,freq=14.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.20223314 = fieldWeight in 277, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=277)
    0.013396261 = product of:
      0.026792523 = sum of:
        0.026792523 = weight(_text_:information in 277) [ClassicSimilarity], result of:
          0.026792523 = score(doc=277,freq=16.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.3291521 = fieldWeight in 277, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=277)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

In philosophy, Ontology is the basic description of things in the world. In information science, an ontology refers to an engineering artifact, constituted by a specific vocabulary used to describe a certain reality. Ontologies have been proposed for validating both conceptual models and conceptual schemas; however, these roles are quite dissimilar. In this article, we show that ontologies can be better understood if we classify the different uses of the term as it appears in the literature. First, we explain Ontology (upper case O) as used in Philosophy. Then, we propose a differentiation between ontologies of information systems and ontologies for information systems. All three concepts have an important role in information science. We clarify the different meanings and uses of Ontology and ontologies through a comparison of research by Wand and Weber and by Guarino in ontology-driven information systems. The contributions of this article are twofold: (a) It provides a better understanding of what ontologies are, and (b) it explains the double role of ontologies in information science research.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.6, S.786-793

Type

a

0.009328311 = product of:
  0.023320777 = sum of:
    0.0076151006 = weight(_text_:a in 4607) [ClassicSimilarity], result of:
      0.0076151006 = score(doc=4607,freq=10.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.14243183 = fieldWeight in 4607, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4607)
    0.015705677 = product of:
      0.031411353 = sum of:
        0.031411353 = weight(_text_:22 in 4607) [ClassicSimilarity], result of:
          0.031411353 = score(doc=4607,freq=2.0), product of:
            0.16237405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046368346 = queryNorm
            0.19345059 = fieldWeight in 4607, 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=4607)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Smart applications behave intelligently because they understand at least partially the context where they operate. To do this, they need not only a formal domain model but also formal descriptions of the data they process and their own operational behaviour. Interoperability of smart applications is based on formalised definitions of all their data and processes. This paper studies the semantic interoperability of data in the case of eLearning and describes an experiment and its assessment. New content is imported into a knowledge-based learning environment without real updates of the original domain model, which is encoded as a knowledge base of conceptual graphs. A component called mediator enables the import by assigning dummy metadata annotations for the imported items. However, some functionality of the original system is lost, when processing the imported content, due to the lack of proper metadata annotation which cannot be associated fully automatically. So the paper presents an interoperability scenario when appropriate content items are viewed from the perspective of the original world and can be (partially) reused there.

Source

Conceptual structures: knowledge architectures for smart applications: 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, July 22 - 27, 2007 ; proceedings. Eds.: U. Priss u.a

Type

a

0.008245549 = product of:
  0.020613872 = sum of:
    0.008258085 = weight(_text_:a in 3149) [ClassicSimilarity], result of:
      0.008258085 = score(doc=3149,freq=6.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.1544581 = fieldWeight in 3149, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3149)
    0.012355788 = product of:
      0.024711575 = sum of:
        0.024711575 = weight(_text_:information in 3149) [ClassicSimilarity], result of:
          0.024711575 = score(doc=3149,freq=10.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.3035872 = fieldWeight in 3149, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3149)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Topic Maps is an international standard technology for describing knowledge structures and using them to improve the findability of information. It is based on a formal model that subsumes those of traditional finding aids such as indexes, glossaries, and thesauri, and extends them to cater for the additional complexities of digital information. Topic Maps is increasingly used in enterprise information integration, knowledge management, e-learning, and digital libraries, and as the foundation for Web-based information delivery solutions. This entry provides a comprehensive treatment of the core concepts, as well as describing the background and current status of the standard and its relationship to traditional knowledge organization techniques.

Source

Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates

Type

a

0.00816826 = product of:
  0.020420648 = sum of:
    0.009036016 = weight(_text_:a in 4406) [ClassicSimilarity], result of:
      0.009036016 = score(doc=4406,freq=22.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.16900843 = fieldWeight in 4406, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.03125 = fieldNorm(doc=4406)
    0.011384632 = product of:
      0.022769265 = sum of:
        0.022769265 = weight(_text_:information in 4406) [ClassicSimilarity], result of:
          0.022769265 = score(doc=4406,freq=26.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.2797255 = fieldWeight in 4406, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=4406)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Important information is often scattered across Web and/or intranet resources. Traditional search engines return ranked retrieval lists that offer little or no information on the semantic relationships among documents. Knowledge workers spend a substantial amount of their time browsing and reading to find out how documents are related to one another and where each falls into the overall structure of the problem domain. Yet only when knowledge workers begin to locate the similarities and differences among pieces of information do they move into an essential part of their work: building relationships to create new knowledge. Information retrieval traditionally focuses on the relationship between a given query (or user profile) and the information store. On the other hand, exploitation of interrelationships between selected pieces of information (which can be facilitated by the use of ontologies) can put otherwise isolated information into a meaningful context. The implicit structures so revealed help users use and manage information more efficiently. Knowledge management tools are needed that integrate the resources dispersed across Web resources into a coherent corpus of interrelated information. Previous research in information integration has largely focused on integrating heterogeneous databases and knowledge bases, which represent information in a highly structured way, often by means of formal languages. In contrast, the Web consists to a large extent of unstructured or semi-structured natural language texts. As we have seen, ontologies offer an alternative way to cope with heterogeneous representations of Web resources. The domain model implicit in an ontology can be taken as a unifying structure for giving information a common representation and semantics. Once such a unifying structure exists, it can be exploited to improve browsing and retrieval performance in information access tools. QuizRDF is an example of such a tool.

Type

a

0.007891519 = product of:
  0.019728797 = sum of:
    0.009138121 = weight(_text_:a in 3165) [ClassicSimilarity], result of:
      0.009138121 = score(doc=3165,freq=10.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.1709182 = fieldWeight in 3165, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=3165)
    0.010590675 = product of:
      0.02118135 = sum of:
        0.02118135 = weight(_text_:information in 3165) [ClassicSimilarity], result of:
          0.02118135 = score(doc=3165,freq=10.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.2602176 = fieldWeight in 3165, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3165)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

As a greater volume of information becomes increasingly available across all disciplines, many approaches, such as document clustering and information visualization, have been proposed to help users manage information easily. However, most of these methods do not directly extract key concepts and their semantic relationships from document corpora, which could help better illuminate the conceptual structures within given information. To address this issue, we propose an approach called Clonto to process a document corpus, identify the key concepts, and automatically generate ontologies based on these concepts for the purpose of conceptualization. For a given document corpus, Clonto applies latent semantic analysis to identify key concepts, allocates documents based on these concepts, and utilizes WordNet to automatically generate a corpus-related ontology. The documents are linked to the ontology through the key concepts. Based on two test collections, the experimental results show that Clonto is able to identify key concepts, and outperforms four other clustering algorithms. Moreover, the ontologies generated by Clonto show significant informative conceptual structures.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2287-2299

Type

a

0.007848554 = product of:
  0.019621385 = sum of:
    0.012923255 = weight(_text_:a in 2357) [ClassicSimilarity], result of:
      0.012923255 = score(doc=2357,freq=20.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.24171482 = fieldWeight in 2357, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2357)
    0.0066981306 = product of:
      0.013396261 = sum of:
        0.013396261 = weight(_text_:information in 2357) [ClassicSimilarity], result of:
          0.013396261 = score(doc=2357,freq=4.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.16457605 = fieldWeight in 2357, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=2357)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Ontology plays an essential role in recognizing the meaning of the information in Web documents. It has been shown that extracting concepts is easier than building relationships among them. For a defined set of concepts, many existing algorithms produce all possible relationships for that set. This makes the process of refining the relationships almost impossible. A new algorithm is needed to reduce the number of relationships among a defined set of concepts produced by existing algorithms. This article contributes such an algorithm, which enables a domain-knowledge expert to refine the relationships linking a set of concepts. In the research reported here, text-mining tools have been used to extract concepts in the domain of e-commerce laws. A new algorithm has been proposed to reduce the number of extracted relationships. It groups the concepts according to the number of relationships with other concepts and provides formalization. An experiment and software have been built, proving that reducing the number of relationships will reduce the efforts needed from a human expert.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.11, S.1801-1809

Type

a

0.007831501 = product of:
  0.019578751 = sum of:
    0.0127425 = weight(_text_:a in 2393) [ClassicSimilarity], result of:
      0.0127425 = score(doc=2393,freq=28.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.23833402 = fieldWeight in 2393, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2393)
    0.006836252 = product of:
      0.013672504 = sum of:
        0.013672504 = weight(_text_:information in 2393) [ClassicSimilarity], result of:
          0.013672504 = score(doc=2393,freq=6.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.16796975 = fieldWeight in 2393, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.13, S.2171-2185

Type

a

0.0077237748 = product of:
  0.019309437 = sum of:
    0.008258085 = weight(_text_:a in 4696) [ClassicSimilarity], result of:
      0.008258085 = score(doc=4696,freq=6.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.1544581 = fieldWeight in 4696, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4696)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 4696) [ClassicSimilarity], result of:
          0.022102704 = score(doc=4696,freq=8.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 4696, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4696)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

WordNet, a lexical database for English, is organized around semantic and lexical relationships between synsets, concepts represented by sets of synonymous word senses. Offering reasonably comprehensive coverage of the nouns, verbs, adjectives, and adverbs of general English, WordNet is a widely used resource for dealing with the ambiguity that arises from homonymy, polysemy, and synonymy. WordNet is used in many information-related tasks and applications (e.g., word sense disambiguation, semantic similarity, lexical chaining, alignment of parallel corpora, text segmentation, sentiment and subjectivity analysis, text classification, information retrieval, text summarization, question answering, information extraction, and machine translation).

Source

Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates

Type

a