Search (24 results, page 2 of 2)

0.0061584353 = product of:
  0.018475305 = sum of:
    0.018475305 = product of:
      0.03695061 = sum of:
        0.03695061 = weight(_text_:processing in 4746) [ClassicSimilarity], result of:
          0.03695061 = score(doc=4746,freq=2.0), product of:
            0.20653816 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.051020417 = queryNorm
            0.17890452 = fieldWeight in 4746, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.03125 = fieldNorm(doc=4746)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable.

0.0061584353 = product of:
  0.018475305 = sum of:
    0.018475305 = product of:
      0.03695061 = sum of:
        0.03695061 = weight(_text_:processing in 1154) [ClassicSimilarity], result of:
          0.03695061 = score(doc=1154,freq=2.0), product of:
            0.20653816 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.051020417 = queryNorm
            0.17890452 = fieldWeight in 1154, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.03125 = fieldNorm(doc=1154)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

In this thesis, we will present methods for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently found in information retrieval. This utilization of ontologies has a number of challenges. Our focus is on the use of similarity measures derived from the knowledge about relations between concepts in ontologies, the recognition of semantic information in texts and the mapping of this knowledge into the ontologies in use, as well as how to fuse together the ideas of ontological similarity and ontological indexing into a realistic information retrieval scenario. To achieve the recognition of semantic knowledge in a text, shallow natural language processing is used during indexing that reveals knowledge to the level of noun phrases. Furthermore, we briefly cover the identification of semantic relations inside and between noun phrases, as well as discuss which kind of problems are caused by an increase in compoundness with respect to the structure of concepts in the evaluation of queries. Measuring similarity between concepts based on distances in the structure of the ontology is discussed. In addition, a shared nodes measure is introduced and, based on a set of intuitive similarity properties, compared to a number of different measures. In this comparison the shared nodes measure appears to be superior, though more computationally complex. Some of the major problems of shared nodes which relate to the way relations differ with respect to the degree they bring the concepts they connect closer are discussed. A generalized measure called weighted shared nodes is introduced to deal with these problems. Finally, the utilization of concept similarity in query evaluation is discussed. A semantic expansion approach that incorporates concept similarity is introduced and a generalized fuzzy set retrieval model that applies expansion during query evaluation is presented. While not commonly used in present information retrieval systems, it appears that the fuzzy set model comprises the flexibility needed when generalizing to an ontology-based retrieval model and, with the introduction of a hierarchical fuzzy aggregation principle, compound concepts can be handled in a straightforward and natural manner.

0.0057604685 = product of:
  0.017281406 = sum of:
    0.017281406 = product of:
      0.03456281 = sum of:
        0.03456281 = weight(_text_:22 in 595) [ClassicSimilarity], result of:
          0.03456281 = score(doc=595,freq=2.0), product of:
            0.1786648 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051020417 = queryNorm
            0.19345059 = fieldWeight in 595, 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=595)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

3. 2.2013 18:00:22

0.004608375 = product of:
  0.013825124 = sum of:
    0.013825124 = product of:
      0.027650248 = sum of:
        0.027650248 = weight(_text_:22 in 4399) [ClassicSimilarity], result of:
          0.027650248 = score(doc=4399,freq=2.0), product of:
            0.1786648 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051020417 = queryNorm
            0.15476047 = fieldWeight in 4399, 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=4399)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

20. 1.2015 18:30:22