Search (3 results, page 1 of 1)

0.03754639 = product of:
  0.07509278 = sum of:
    0.07509278 = sum of:
      0.03267146 = weight(_text_:systems in 2230) [ClassicSimilarity], result of:
        0.03267146 = score(doc=2230,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.2037246 = fieldWeight in 2230, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.046875 = fieldNorm(doc=2230)
      0.042421322 = weight(_text_:22 in 2230) [ClassicSimilarity], result of:
        0.042421322 = score(doc=2230,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.23214069 = fieldWeight in 2230, 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=2230)
  0.5 = coord(1/2)

Abstract

We present a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and occurrence levels. Concepts and relationships among them are represented at the conceptual level. The expression level represents natural language expressions for concepts. Each expression has one or more matching models at the occurrence level. Each model specifies the matching of the expression in database indices built in varying ways. The data model supports a concept-based query expansion and formulation tool, the ExpansionTool, for environments providing heterogeneous IR systems. Expansion is controlled by adjustable matching reliability.

Source

Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al

0.008167865 = product of:
  0.01633573 = sum of:
    0.01633573 = product of:
      0.03267146 = sum of:
        0.03267146 = weight(_text_:systems in 2229) [ClassicSimilarity], result of:
          0.03267146 = score(doc=2229,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.2037246 = fieldWeight in 2229, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=2229)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Modern fact databses contain abundant data classified through several classifications. Typically, users msut consult these classifications in separate manuals or files, thus making their effective use difficult. Contemporary database systems do little support deductive use of classifications. In this study we show how deductive data management techniques can be applied to the utilization of data value classifications. Computation of transitive class relationships is of primary importance here. We define a representation of classifications which supports transitive computation and present an operation-oriented deductive query language tailored for classification-based deductive information retrieval. The operations of this language are on the same abstraction level as relational algebra operations and can be integrated with these to form a powerful and flexible query language for deductive information retrieval. We define the integration of these operations and demonstrate the usefulness of the language in terms of several sample queries

0.0048129605 = product of:
  0.009625921 = sum of:
    0.009625921 = product of:
      0.019251842 = sum of:
        0.019251842 = weight(_text_:systems in 1323) [ClassicSimilarity], result of:
          0.019251842 = score(doc=1323,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.12004587 = fieldWeight in 1323, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1323)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Turn analyzes the research of information seeking and retrieval (IS&R) and proposes a new direction of integrating research in these two areas: the fields should turn off their separate and narrow paths and construct a new avenue of research. An essential direction for this avenue is context as given in the subtitle Integration of Information Seeking and Retrieval in Context. Other essential themes in the book include: IS&R research models, frameworks and theories; search and works tasks and situations in context; interaction between humans and machines; information acquisition, relevance and information use; research design and methodology based on a structured set of explicit variables - all set into the holistic cognitive approach. The present monograph invites the reader into a construction project - there is much research to do for a contextual understanding of IS&R. The Turn represents a wide-ranging perspective of IS&R by providing a novel unique research framework, covering both individual and social aspects of information behavior, including the generation, searching, retrieval and use of information. Regarding traditional laboratory information retrieval research, the monograph proposes the extension of research toward actors, search and work tasks, IR interaction and utility of information. Regarding traditional information seeking research, it proposes the extension toward information access technology and work task contexts. The Turn is the first synthesis of research in the broad area of IS&R ranging from systems oriented laboratory IR research to social science oriented information seeking studies. TOC:Introduction.- The Cognitive Framework for Information.- The Development of Information Seeking Research.- Systems-Oriented Information Retrieval.- Cognitive and User-Oriented Information Retrieval.- The Integrated IS&R Research Framework.- Implications of the Cognitive Framework for IS&R.- Towards a Research Program.- Conclusion.- Definitions.- References.- Index.