Search (2 results, page 1 of 1)

  • × author_ss:"Keskustalo, H."
  • × author_ss:"Järvelin, K."
  1. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.04
    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
  2. Ferro, N.; Silvello, G.; Keskustalo, H.; Pirkola, A.; Järvelin, K.: ¬The twist measure for IR evaluation : taking user's effort into account (2016) 0.01
    0.0068065543 = product of:
      0.013613109 = sum of:
        0.013613109 = product of:
          0.027226217 = sum of:
            0.027226217 = weight(_text_:systems in 2771) [ClassicSimilarity], result of:
              0.027226217 = score(doc=2771,freq=2.0), product of:
                0.16037072 = queryWeight, product of:
                  3.0731742 = idf(docFreq=5561, maxDocs=44218)
                  0.052184064 = queryNorm
                0.1697705 = fieldWeight in 2771, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.0731742 = idf(docFreq=5561, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2771)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    We present a novel measure for ranking evaluation, called Twist (t). It is a measure for informational intents, which handles both binary and graded relevance. t stems from the observation that searching is currently a that searching is currently taken for granted and it is natural for users to assume that search engines are available and work well. As a consequence, users may assume the utility they have in finding relevant documents, which is the focus of traditional measures, as granted. On the contrary, they may feel uneasy when the system returns nonrelevant documents because they are then forced to do additional work to get the desired information, and this causes avoidable effort. The latter is the focus of t, which evaluates the effectiveness of a system from the point of view of the effort required to the users to retrieve the desired information. We provide a formal definition of t, a demonstration of its properties, and introduce the notion of effort/gain plots, which complement traditional utility-based measures. By means of an extensive experimental evaluation, t is shown to grasp different aspects of system performances, to not require extensive and costly assessments, and to be a robust tool for detecting differences between systems.