-
Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005)
0.21
0.20908675 = product of:
0.3345388 = sum of:
0.03263587 = product of:
0.09790761 = sum of:
0.09790761 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
0.09790761 = score(doc=701,freq=2.0), product of:
0.26131085 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030822188 = 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)
0.09790761 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
0.09790761 = score(doc=701,freq=2.0), product of:
0.26131085 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030822188 = 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)
0.09790761 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
0.09790761 = score(doc=701,freq=2.0), product of:
0.26131085 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030822188 = 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)
0.09790761 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
0.09790761 = score(doc=701,freq=2.0), product of:
0.26131085 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030822188 = 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)
0.008180073 = product of:
0.02454022 = sum of:
0.02454022 = weight(_text_:problem in 701) [ClassicSimilarity], result of:
0.02454022 = score(doc=701,freq=2.0), product of:
0.13082431 = queryWeight, product of:
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.030822188 = queryNorm
0.1875815 = fieldWeight in 701, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.33333334 = coord(1/3)
0.625 = coord(5/8)
- 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.
-
Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002)
0.00
0.0017399922 = product of:
0.013919937 = sum of:
0.013919937 = product of:
0.04175981 = sum of:
0.04175981 = weight(_text_:22 in 3406) [ClassicSimilarity], result of:
0.04175981 = score(doc=3406,freq=2.0), product of:
0.10793405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.030822188 = 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)
0.33333334 = coord(1/3)
0.125 = coord(1/8)
- Date
- 30. 5.2010 16:22:35
-
Schwarz, K.: Domain model enhanced search : a comparison of taxonomy, thesaurus and ontology (2005)
0.00
0.0014460464 = product of:
0.011568371 = sum of:
0.011568371 = product of:
0.034705114 = sum of:
0.034705114 = weight(_text_:problem in 4569) [ClassicSimilarity], result of:
0.034705114 = score(doc=4569,freq=4.0), product of:
0.13082431 = queryWeight, product of:
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.030822188 = queryNorm
0.2652803 = fieldWeight in 4569, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.03125 = fieldNorm(doc=4569)
0.33333334 = coord(1/3)
0.125 = coord(1/8)
- Abstract
- The results of this thesis are intended to support the information architect in designing a solution for improved search in a corporate environment. Specifically we have examined the type of search problems that require a domain model to enhance the search process. There are several approaches to modeling a domain. We have considered 3 different types of domain modeling schemes; taxonomy, thesaurus and ontology. The intention is to support the information architect in making an informed choice between one or more of these schemes. In our opinion the main criteria for this choice are the modeling characteristics of a scheme and the suitability for application in the search process. The second chapter is a discussion of modeling characteristics of each scheme, followed by a comparison between them. This should give an information architect an idea of which aspects of a domain can be modeled with each scheme. What is missing here is an indication of the effort required to model a domain with each scheme. There are too many factors that influence the amount of required effort, ranging from measurable factors like domain size and resource characteristics to cultural matters such as the willingness to share knowledge and the existence of a project champion in the team to keep the project running. The third chapter shows what role domain models can play in each part of the search process. This gives an idea of the problems that domain models can solve. We have split the search process into individual parts to show that domain models can be applied very differently in the process. The fourth chapter makes recommendations about the suitability of each individualdomain modeling scheme for improving search. Each scheme has particular characteristics that make it especially suitable for a domain or a search problem. In the appendix each case study is described in detail. These descriptions are intended to serve as a benchmark. The current problem of the enterprise can be compared to those described to see which case study is most similar, which solution was chosen, which problems arose and how they were dealt with. An important issue that we have not touched upon in this thesis is that of maintenance. The real problems of a domain model are revealed when it is applied in a search system and its deficits and wrong assumptions become clear. Adaptation and maintenance are always required. Unfortunately we have not been able to glean sufficient information about maintenance issues from our case studies to draw any meaningful conclusions.
-
Nix, M.: ¬Die praktische Einsetzbarkeit des CIDOC CRM in Informationssystemen im Bereich des Kulturerbes (2004)
0.00
0.0012781365 = product of:
0.010225092 = sum of:
0.010225092 = product of:
0.030675275 = sum of:
0.030675275 = weight(_text_:problem in 3742) [ClassicSimilarity], result of:
0.030675275 = score(doc=3742,freq=2.0), product of:
0.13082431 = queryWeight, product of:
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.030822188 = queryNorm
0.23447686 = fieldWeight in 3742, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.0390625 = fieldNorm(doc=3742)
0.33333334 = coord(1/3)
0.125 = coord(1/8)
- Abstract
- Es steht uns eine praktisch unbegrenzte Menge an Informationen über das World Wide Web zur Verfügung. Das Problem, das daraus erwächst, ist, diese Menge zu bewältigen und an die Information zu gelangen, die im Augenblick benötigt wird. Das überwältigende Angebot zwingt sowohl professionelle Anwender als auch Laien zu suchen, ungeachtet ihrer Ansprüche an die gewünschten Informationen. Um dieses Suchen effizienter zu gestalten, gibt es einerseits die Möglichkeit, leistungsstärkere Suchmaschinen zu entwickeln. Eine andere Möglichkeit ist, Daten besser zu strukturieren, um an die darin enthaltenen Informationen zu gelangen. Hoch strukturierte Daten sind maschinell verarbeitbar, sodass ein Teil der Sucharbeit automatisiert werden kann. Das Semantic Web ist die Vision eines weiterentwickelten World Wide Web, in dem derart strukturierten Daten von so genannten Softwareagenten verarbeitet werden. Die fortschreitende inhaltliche Strukturierung von Daten wird Semantisierung genannt. Im ersten Teil der Arbeit sollen einige wichtige Methoden der inhaltlichen Strukturierung von Daten skizziert werden, um die Stellung von Ontologien innerhalb der Semantisierung zu klären. Im dritten Kapitel wird der Aufbau und die Aufgabe des CIDOC Conceptual Reference Model (CRM), einer Domain Ontologie im Bereich des Kulturerbes dargestellt. Im darauf folgenden praktischen Teil werden verschiedene Ansätze zur Verwendung des CRM diskutiert und umgesetzt. Es wird ein Vorschlag zur Implementierung des Modells in XML erarbeitet. Das ist eine Möglichkeit, die dem Datentransport dient. Außerdem wird der Entwurf einer Klassenbibliothek in Java dargelegt, auf die die Verarbeitung und Nutzung des Modells innerhalb eines Informationssystems aufbauen kann.
-
Müller, T.: Wissensrepräsentation mit semantischen Netzen im Bereich Luftfahrt (2006)
0.00
8.699961E-4 = product of:
0.0069599687 = sum of:
0.0069599687 = product of:
0.020879906 = sum of:
0.020879906 = weight(_text_:22 in 1670) [ClassicSimilarity], result of:
0.020879906 = score(doc=1670,freq=2.0), product of:
0.10793405 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.030822188 = 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)
0.33333334 = coord(1/3)
0.125 = coord(1/8)
- Date
- 26. 9.2006 21:00:22
