Search (8 results, page 1 of 1)

0.011128641 = product of:
  0.06677184 = sum of:
    0.06677184 = weight(_text_:propose in 5082) [ClassicSimilarity], result of:
      0.06677184 = score(doc=5082,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.3403687 = fieldWeight in 5082, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.046875 = fieldNorm(doc=5082)
  0.16666667 = coord(1/6)

Abstract

The aim of this paper is to indicate how TOSCANA may be extended to allow graphical representations not only of concept lattices but also of concept graphs in the sense of Contextual Logic. The contextual- logic extension of TOSCANA requires the logical scaling of conceptual and relational scales for which we propose the Peircean Algebraic Logic as reconstructed by R. W. Burch. As graphical representations we recommend, besides labelled line diagrams of concept lattices and Sowa's diagrams of conceptual graphs, particular information maps for utilizing background knowledge as much as possible. Our considerations are illustrated by a small information system about the domestic flights in Austria

0.010492183 = product of:
  0.0629531 = sum of:
    0.0629531 = weight(_text_:propose in 3291) [ClassicSimilarity], result of:
      0.0629531 = score(doc=3291,freq=4.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.3209027 = fieldWeight in 3291, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.03125 = fieldNorm(doc=3291)
  0.16666667 = coord(1/6)

Abstract

The web is a network of linked sites whereby each site either forms a physical portal or a standalone page. In the former case, the portal presents an access point to its embedded web pages that coherently present a specific topic. In the latter case, there are millions of standalone web pages, that are scattered throughout the web, having the same topic and could be conceptually linked together to form virtual portals. Search engines have been developed to help users in reaching the adequate pages in an efficient and effective manner. All the known current search engine techniques rely on the web page as the basic atomic search unit. They ignore the conceptual links, that reveal the implicit web related meanings, among the retrieved pages. However, building a semantic model for the whole portal may contain more semantic information than a model of scattered individual pages. In addition, user queries can be poor and contain imprecise terms that do not reflect the real user intention. Consequently, retrieving the standalone individual pages that are directly related to the query may not satisfy the user's need. In this paper, we propose PREFCA, a Portal Retrieval Engine based on Formal Concept Analysis that relies on the portal as the main search unit. PREFCA consists of three phases: First, the information extraction phase that is concerned with extracting portal's semantic data. Second, the formal concept analysis phase that utilizes formal concept analysis to discover the conceptual links among portal and attributes. Finally, the information retrieval phase where we propose a portal ranking method to retrieve ranked pairs of portals and embedded pages. Additionally, we apply the network analysis rules to output some portal characteristics. We evaluated PREFCA using two data sets, namely the Forum for Information Retrieval Evaluation 2010 and ClueWeb09 (category B) test data, for physical and virtual portals respectively. PREFCA proves higher F-measure accuracy, better Mean Average Precision ranking and comparable network analysis and efficiency results than other search engine approaches, namely Term Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and BM25 techniques. As well, it gains high Mean Average Precision in comparison with learning to rank techniques. Moreover, PREFCA also gains better reach time than Carrot as a well-known topic-based search engine.

0.009273868 = product of:
  0.055643205 = sum of:
    0.055643205 = weight(_text_:propose in 4843) [ClassicSimilarity], result of:
      0.055643205 = score(doc=4843,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.2836406 = fieldWeight in 4843, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4843)
  0.16666667 = coord(1/6)

Abstract

Formal Concept Analysis (FCA) is a well founded mathematical framework used for conceptual classication and knowledge management. Given a binary table describing a relation between objects and attributes, FCA consists in building a set of concepts organized by a subsumption relation within a concept lattice. Accordingly, FCA requires to transform complex data, e.g. numbers, intervals, graphs, into binary data leading to loss of information and poor interpretability of object classes. In this paper, we propose a pre-processing method producing binary data from complex data taking advantage of similarity between objects. As a result, the concept lattice is composed of classes being maximal sets of pairwise similar objects. This method is based on FCA and on a formalization of similarity as a tolerance relation (reexive and symmetric). It applies to complex object descriptions and especially here to interval data. Moreover, it can be applied to any kind of structured data for which a similarity can be dened (sequences, graphs, etc.). Finally, an application highlights that the resulting concept lattice plays an important role in information fusion problem, as illustrated with a real-world example in agronomy.

0.0046431995 = product of:
  0.027859196 = sum of:
    0.027859196 = product of:
      0.08357759 = sum of:
        0.08357759 = weight(_text_:29 in 4305) [ClassicSimilarity], result of:
          0.08357759 = score(doc=4305,freq=2.0), product of:
            0.13440257 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.038207654 = queryNorm
            0.6218451 = fieldWeight in 4305, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.125 = fieldNorm(doc=4305)
      0.33333334 = coord(1/3)
  0.16666667 = coord(1/6)

Date

13. 7.2008 19:29:59

0.0028758943 = product of:
  0.017255366 = sum of:
    0.017255366 = product of:
      0.051766098 = sum of:
        0.051766098 = weight(_text_:22 in 3142) [ClassicSimilarity], result of:
          0.051766098 = score(doc=3142,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = queryNorm
            0.38690117 = fieldWeight in 3142, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3142)
      0.33333334 = coord(1/3)
  0.16666667 = coord(1/6)

Date

26. 2.2008 15:58:22

0.0023007155 = product of:
  0.013804292 = sum of:
    0.013804292 = product of:
      0.041412875 = sum of:
        0.041412875 = weight(_text_:22 in 1901) [ClassicSimilarity], result of:
          0.041412875 = score(doc=1901,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = queryNorm
            0.30952093 = fieldWeight in 1901, 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=1901)
      0.33333334 = coord(1/3)
  0.16666667 = coord(1/6)

Source

Knowledge organization. 22(1995) no.2, S.78-81

0.0020131262 = product of:
  0.0120787565 = sum of:
    0.0120787565 = product of:
      0.036236268 = sum of:
        0.036236268 = weight(_text_:22 in 5095) [ClassicSimilarity], result of:
          0.036236268 = score(doc=5095,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = 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)
      0.33333334 = coord(1/3)
  0.16666667 = coord(1/6)

Date

22. 1.2016 17:47:06

0.0020131262 = product of:
  0.0120787565 = sum of:
    0.0120787565 = product of:
      0.036236268 = sum of:
        0.036236268 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.036236268 = score(doc=2654,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = queryNorm
            0.2708308 = fieldWeight in 2654, 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=2654)
      0.33333334 = coord(1/3)
  0.16666667 = coord(1/6)

Date

22. 1.2016 17:30:31