Search (5 results, page 1 of 1)

0.06614524 = sum of:
  0.055254754 = product of:
    0.16576426 = sum of:
      0.16576426 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
        0.16576426 = score(doc=701,freq=2.0), product of:
          0.4424171 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.052184064 = queryNorm
          0.3746787 = fieldWeight in 701, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.03125 = fieldNorm(doc=701)
    0.33333334 = coord(1/3)
  0.010890487 = product of:
    0.021780973 = sum of:
      0.021780973 = weight(_text_:systems in 701) [ClassicSimilarity], result of:
        0.021780973 = score(doc=701,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.1358164 = fieldWeight in 701, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.03125 = fieldNorm(doc=701)
    0.5 = coord(1/2)

Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

0.01767555 = product of:
  0.0353511 = sum of:
    0.0353511 = product of:
      0.0707022 = sum of:
        0.0707022 = weight(_text_:22 in 3406) [ClassicSimilarity], result of:
          0.0707022 = score(doc=3406,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = queryNorm
            0.38690117 = fieldWeight in 3406, 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=3406)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

30. 5.2010 16:22:35

0.008837775 = product of:
  0.01767555 = sum of:
    0.01767555 = product of:
      0.0353511 = sum of:
        0.0353511 = weight(_text_:22 in 1670) [ClassicSimilarity], result of:
          0.0353511 = score(doc=1670,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = queryNorm
            0.19345059 = fieldWeight in 1670, 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=1670)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

26. 9.2006 21:00:22

0.0054452433 = product of:
  0.010890487 = sum of:
    0.010890487 = product of:
      0.021780973 = sum of:
        0.021780973 = weight(_text_:systems in 4498) [ClassicSimilarity], result of:
          0.021780973 = score(doc=4498,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.1358164 = fieldWeight in 4498, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03125 = fieldNorm(doc=4498)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Die auf Basis vernetzter Computer realisierbare Möglichkeit einer universalen Enzyklopädie führt aufgrund der dabei technisch notwendigen Reduktion auf nur eine Sorte Repräsentanten zu Systemen, bei denen entweder nur Gegenstände repräsentiert werden, die auch Begriffe vertreten, oder nur Begriffe, die auch Gegenstände vertreten. In der Dissertation werden als Beispiele solcher Repräsentationssysteme die logischen Systeme von Gottlob Frege und Johann Heinrich Lambert untersucht. Freges System, basierend auf der Annahme der Objektivität von Bedeutungen, war durch die Nachweisbarkeit einer Antinomie gescheitert, weshalb von Philosophen im 20. Jahrhundert die Existenz einer objektiven Bedeutung von Ausdrücken und die Übersetzbarkeit der Gedanken aus den natürlichen Sprachen in eine formale Sprache in Frage gestellt wurde. In der Dissertation wird nachgewiesen, daß diese Konsequenz voreilig war und daß die Antinomie auch bei Annahme der Objektivität von Wissen erst durch zwei Zusatzforderungen in Freges Logik ausgelöst wird: die eineindeutige Zuordnung eines Gegenstands zu jedem Begriff sowie die scharfen Begrenzung der Begriffe, die zur Abgeschlossenheit des Systems zwingt. Als Alternative wird das Begriffssystem Lamberts diskutiert, bei dem jeder Gegenstand durch einen Begriff und gleichwertig durch Gesamtheiten von Begriffen vertreten wird und Begriffe durch Gesamtheiten von Begriffen ersetzbar sind. Beide die Antinomie auslösenden Bedingungen sind hier nicht vorhanden, zugleich ist die fortschreitende Entwicklung von Wissen repräsentierbar. Durch die mengentheoretische Rekonstruktion des Begriffssystems Lamberts in der Dissertation wird dessen praktische Nutzbarkeit gezeigt. Resultat der Dissertation ist der Nachweis, daß es Repräsentationssysteme gibt, die nicht auf die für die Prüfung der Verbindlichkeit der Einträge in die Enzyklopädie notwendige Annahme der Verobjektivierbarkeit von Wissen verzichten müssen, weil ihnen nicht jene die Antinomie auslösenden Voraussetzungen zugrunde liegen.

0.0054452433 = product of:
  0.010890487 = sum of:
    0.010890487 = product of:
      0.021780973 = sum of:
        0.021780973 = weight(_text_:systems in 1154) [ClassicSimilarity], result of:
          0.021780973 = score(doc=1154,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.1358164 = fieldWeight in 1154, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03125 = fieldNorm(doc=1154)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.