Search (2 results, page 1 of 1)

0.021418769 = product of:
  0.0642563 = sum of:
    0.0642563 = sum of:
      0.029867431 = weight(_text_:web in 2443) [ClassicSimilarity], result of:
        0.029867431 = score(doc=2443,freq=2.0), product of:
          0.1656677 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.050763648 = queryNorm
          0.18028519 = fieldWeight in 2443, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2443)
      0.03438887 = weight(_text_:22 in 2443) [ClassicSimilarity], result of:
        0.03438887 = score(doc=2443,freq=2.0), product of:
          0.17776565 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050763648 = queryNorm
          0.19345059 = fieldWeight in 2443, 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=2443)
  0.33333334 = coord(1/3)

Abstract

Systems for searching the Web based on thematic contexts can be built on top of a conventional search engine and benefit from the huge amount of content as well as from the functionality available through the search engine interface. The quality of the material collected by such systems is highly dependant on the vocabulary used to generate the search queries. In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of this optimization problem are: (1) the high-dimensionality of the search space, where candidate solutions are queries and each term corresponds to a different dimension, (2) the existence of acceptable suboptimal solutions, (3) the possibility of finding multiple solutions, and in many cases (4) the quest for novelty. This article describes optimization techniques based on Genetic Algorithms to evolve "good query terms" in the context of a given topic. The proposed techniques place emphasis on searching for novel material that is related to the search context. We discuss the use of a mutation pool to allow the generation of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction of queries containing novel but related terms.

Date

22.11.2008 12:49:22

0.004977905 = product of:
  0.014933716 = sum of:
    0.014933716 = product of:
      0.029867431 = sum of:
        0.029867431 = weight(_text_:web in 1105) [ClassicSimilarity], result of:
          0.029867431 = score(doc=1105,freq=2.0), product of:
            0.1656677 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.050763648 = queryNorm
            0.18028519 = fieldWeight in 1105, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1105)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Topic ontologies or web directories consist of large collections of links to websites, arranged by topic in different categories. The structure of these ontologies is typically not flat because there are hierarchical and nonhierarchical relationships among topics. As a consequence, websites classified under a certain topic may be relevant to other topics. Although some of these relevance relations are explicit, most of them must be discovered by an analysis of the structure of the ontologies. This article proposes a family of models of relevance propagation in topic ontologies. An efficient computational framework is described and used to compute nine different models for a portion of the Open Directory Project graph consisting of more than half a million nodes and approximately 1.5 million edges of different types. After performing a quantitative analysis, a user study was carried out to compare the most promising models. It was found that some general difficulties rule out the possibility of defining flawless models of relevance propagation that only take into account structural aspects of an ontology. However, there is a clear indication that including transitive relations induced by the nonhierarchical components of the ontology results in relevance propagation models that are superior to more basic approaches.