Search (6 results, page 1 of 1)

0.035233255 = product of:
  0.07046651 = sum of:
    0.07046651 = sum of:
      0.042176858 = weight(_text_:p in 2763) [ClassicSimilarity], result of:
        0.042176858 = score(doc=2763,freq=4.0), product of:
          0.18768665 = queryWeight, product of:
            3.5955126 = idf(docFreq=3298, maxDocs=44218)
            0.052200247 = queryNorm
          0.22471954 = fieldWeight in 2763, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.5955126 = idf(docFreq=3298, maxDocs=44218)
            0.03125 = fieldNorm(doc=2763)
      0.028289651 = weight(_text_:22 in 2763) [ClassicSimilarity], result of:
        0.028289651 = score(doc=2763,freq=2.0), product of:
          0.18279637 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052200247 = queryNorm
          0.15476047 = fieldWeight in 2763, 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=2763)
  0.5 = coord(1/2)

Abstract

The different points of views an knowledge representation and organization from various research communities reflect underlying philosophies and paradigms in these communities. This paper reviews differences and relations in knowledge representation and organization and generalizes four paradigms-integrative and disintegrative pragmatism and integrative and disintegrative epistemologism. Examples such as classification, XML schemas, and ontologies are compared based an how they specify concepts, build data models, and encode knowledge organization structures. 1. Introduction Knowledge representation (KR) is a term that several research communities use to refer to somewhat different aspects of the same research area. The artificial intelligence (AI) community considers KR as simply "something to do with writing down, in some language or communications medium, descriptions or pictures that correspond in some salient way to the world or a state of the world" (Duce & Ringland, 1988, p. 3). It emphasizes the ways in which knowledge can be encoded in a computer program (Bench-Capon, 1990). For the library and information science (LIS) community, KR is literally the synonym of knowledge organization, i.e., KR is referred to as the process of organizing knowledge into classifications, thesauri, or subject heading lists. KR has another meaning in LIS: it "encompasses every type and method of indexing, abstracting, cataloguing, classification, records management, bibliography and the creation of textual or bibliographic databases for information retrieval" (Anderson, 1996, p. 336). Adding the social dimension to knowledge organization, Hjoerland (1997) states that knowledge is a part of human activities and tied to the division of labor in society, which should be the primary organization of knowledge. Knowledge organization in LIS is secondary or derived, because knowledge is organized in learned institutions and publications. These different points of views an KR suggest that an essential difference in the understanding of KR between both AI and LIS lies in the source of representationwhether KR targets human activities or derivatives (knowledge produced) from human activities. This difference also decides their difference in purpose-in AI KR is mainly computer-application oriented or pragmatic and the result of representation is used to support decisions an human activities, while in LIS KR is conceptually oriented or abstract and the result of representation is used for access to derivatives from human activities.

Date

12. 9.2004 17:22:35

0.021217238 = product of:
  0.042434476 = sum of:
    0.042434476 = product of:
      0.08486895 = sum of:
        0.08486895 = weight(_text_:22 in 3895) [ClassicSimilarity], result of:
          0.08486895 = score(doc=3895,freq=2.0), product of:
            0.18279637 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052200247 = queryNorm
            0.46428138 = fieldWeight in 3895, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=3895)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

24. 8.2005 19:20:22

0.011183828 = product of:
  0.022367656 = sum of:
    0.022367656 = product of:
      0.044735312 = sum of:
        0.044735312 = weight(_text_:p in 2519) [ClassicSimilarity], result of:
          0.044735312 = score(doc=2519,freq=2.0), product of:
            0.18768665 = queryWeight, product of:
              3.5955126 = idf(docFreq=3298, maxDocs=44218)
              0.052200247 = queryNorm
            0.23835106 = fieldWeight in 2519, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5955126 = idf(docFreq=3298, maxDocs=44218)
              0.046875 = fieldNorm(doc=2519)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.0105944 = product of:
  0.0211888 = sum of:
    0.0211888 = product of:
      0.0847552 = sum of:
        0.0847552 = weight(_text_:authors in 4926) [ClassicSimilarity], result of:
          0.0847552 = score(doc=4926,freq=4.0), product of:
            0.23797122 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.052200247 = queryNorm
            0.35615736 = fieldWeight in 4926, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4926)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

The structural, functional, and production views on learning objects influence metadata structure and vocabulary. The authors drew on these views and conducted a literature review and in-depth analysis of 14 learning objects and over 500 components in these learning objects to model the knowledge framework for a learning object ontology. The learning object ontology reported in this article consists of 8 top-level classes, 28 classes at the second level, and 34 at the third level. Except class Learning object, all other classes have the three properties of preferred term, related term, and synonym. To validate the ontology, we conducted a query log analysis that focused an discovering what terms users have used at both conceptual and word levels. The findings show that the main classes in the ontology are either conceptually or linguistically similar to the top terms in the query log data. The authors built an "Exercise Editor" as an informal experiment to test its adoption ability in authoring tools. The main contribution of this project is in the framework for the learning object domain and the methodology used to develop and validate an ontology.

0.008840516 = product of:
  0.017681032 = sum of:
    0.017681032 = product of:
      0.035362065 = sum of:
        0.035362065 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
          0.035362065 = score(doc=2648,freq=2.0), product of:
            0.18279637 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052200247 = queryNorm
            0.19345059 = fieldWeight in 2648, 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=2648)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.007491372 = product of:
  0.014982744 = sum of:
    0.014982744 = product of:
      0.059930976 = sum of:
        0.059930976 = weight(_text_:authors in 5054) [ClassicSimilarity], result of:
          0.059930976 = score(doc=5054,freq=2.0), product of:
            0.23797122 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.052200247 = queryNorm
            0.25184128 = fieldWeight in 5054, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5054)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIP), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English-Chinese Web portal that incorporates various CLIP techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-byword translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise.