Search (210 results, page 1 of 11)

0.07385479 = product of:
  0.14770958 = sum of:
    0.14770958 = sum of:
      0.07700737 = weight(_text_:systems in 539) [ClassicSimilarity], result of:
        0.07700737 = score(doc=539,freq=4.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.48018348 = fieldWeight in 539, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.078125 = fieldNorm(doc=539)
      0.0707022 = weight(_text_:22 in 539) [ClassicSimilarity], result of:
        0.0707022 = score(doc=539,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.38690117 = fieldWeight in 539, 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=539)
  0.5 = coord(1/2)

Content

Präsentation während der Veranstaltung "Networked Knowledge Organization Systems and Services: The 6th European Networked Knowledge Organization Systems (NKOS) Workshop, Workshop at the 11th ECDL Conference, Budapest, Hungary, September 21st 2007".

Date

26.12.2011 13:22:07

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.

0.06614524 = sum of:
  0.055254754 = product of:
    0.16576426 = sum of:
      0.16576426 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
        0.16576426 = score(doc=5820,freq=2.0), product of:
          0.4424171 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.052184064 = queryNorm
          0.3746787 = fieldWeight in 5820, 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=5820)
    0.33333334 = coord(1/3)
  0.010890487 = product of:
    0.021780973 = sum of:
      0.021780973 = weight(_text_:systems in 5820) [ClassicSimilarity], result of:
        0.021780973 = score(doc=5820,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.1358164 = fieldWeight in 5820, 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=5820)
    0.5 = coord(1/2)

Abstract

The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

0.06257732 = product of:
  0.12515464 = sum of:
    0.12515464 = sum of:
      0.054452434 = weight(_text_:systems in 6089) [ClassicSimilarity], result of:
        0.054452434 = score(doc=6089,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.339541 = fieldWeight in 6089, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.078125 = fieldNorm(doc=6089)
      0.0707022 = weight(_text_:22 in 6089) [ClassicSimilarity], result of:
        0.0707022 = score(doc=6089,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.38690117 = fieldWeight in 6089, 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=6089)
  0.5 = coord(1/2)

Pages

S.11-22

Source

Compatibility and integration of order systems: Research Seminar Proceedings of the TIP/ISKO Meeting, Warsaw, 13-15 September 1995

0.053882122 = product of:
  0.107764244 = sum of:
    0.107764244 = sum of:
      0.06534292 = weight(_text_:systems in 4820) [ClassicSimilarity], result of:
        0.06534292 = score(doc=4820,freq=8.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.4074492 = fieldWeight in 4820, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.046875 = fieldNorm(doc=4820)
      0.042421322 = weight(_text_:22 in 4820) [ClassicSimilarity], result of:
        0.042421322 = score(doc=4820,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.23214069 = fieldWeight in 4820, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=4820)
  0.5 = coord(1/2)

Abstract

One of the major problems facing systems for Computer Aided Design (CAD), Architecture Engineering and Construction (AEC) and Geographic Information Systems (GIS) applications today is the lack of interoperability among the various systems. When integrating software applications, substantial di culties can arise in translating information from one application to the other. In this paper, we focus on semantic di culties that arise in software integration. Applications may use di erent terminologies to describe the same domain. Even when appli-cations use the same terminology, they often associate di erent semantics with the terms. This obstructs information exchange among applications. To cir-cumvent this obstacle, we need some way of explicitly specifying the semantics for each terminology in an unambiguous fashion. Ontologies can provide such specification. It will be the task of this paper to explain what ontologies are and how they can be used to facilitate interoperability between software systems used in computer aided design, architecture engineering and construction, and geographic information processing.

Date

3.12.2016 18:39:22

0.051698353 = product of:
  0.10339671 = sum of:
    0.10339671 = sum of:
      0.053905163 = weight(_text_:systems in 3694) [ClassicSimilarity], result of:
        0.053905163 = score(doc=3694,freq=4.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.33612844 = fieldWeight in 3694, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.0546875 = fieldNorm(doc=3694)
      0.049491543 = weight(_text_:22 in 3694) [ClassicSimilarity], result of:
        0.049491543 = score(doc=3694,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.2708308 = fieldWeight in 3694, 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=3694)
  0.5 = coord(1/2)

Abstract

A method to develop a prototype domain ontology has been described. The domain selected for the study is Accelerator Driven Systems. This is a multidisciplinary and interdisciplinary subject comprising Nuclear Physics, Nuclear and Reactor Engineering, Reactor Fuels and Radioactive Waste Management. Since Accelerator Driven Systems is a vast topic, select areas in it were singled out for the study. Both qualitative and quantitative methods such as Content analysis, Facet analysis and Clustering were used, to develop the web-based model.

Date

22. 7.2010 19:41:16

0.050061855 = product of:
  0.10012371 = sum of:
    0.10012371 = sum of:
      0.043561947 = weight(_text_:systems in 4476) [ClassicSimilarity], result of:
        0.043561947 = score(doc=4476,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.2716328 = fieldWeight in 4476, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.0625 = fieldNorm(doc=4476)
      0.056561764 = weight(_text_:22 in 4476) [ClassicSimilarity], result of:
        0.056561764 = score(doc=4476,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.30952093 = fieldWeight in 4476, 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=4476)
  0.5 = coord(1/2)

Abstract

Describes the types of representations used in different theories of abstractions. Shows how the type of mapping between these representations has been increasingly generalised. Discusses desirable properties preserved by such mappings and identifies how these properties are influenced by the mappings and the presentations defined. Surveys programs made in understanding the complexity reduction associated with abstraction. Focuses on formal models of how abstraction reduces the search space. Presents some of the systems that implement abstraction. shows how the efforts in this area have focused on the mechanisation of languages for the declarative representation of abstraction.

Date

1.10.2018 14:13:22

0.049504973 = product of:
  0.099009946 = sum of:
    0.099009946 = sum of:
      0.056588627 = weight(_text_:systems in 2655) [ClassicSimilarity], result of:
        0.056588627 = score(doc=2655,freq=6.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.35286134 = fieldWeight in 2655, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.046875 = fieldNorm(doc=2655)
      0.042421322 = weight(_text_:22 in 2655) [ClassicSimilarity], result of:
        0.042421322 = score(doc=2655,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.23214069 = fieldWeight in 2655, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2655)
  0.5 = coord(1/2)

Abstract

The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.

Date

22. 1.2016 17:30:31

0.044312872 = product of:
  0.088625744 = sum of:
    0.088625744 = sum of:
      0.04620442 = weight(_text_:systems in 2623) [ClassicSimilarity], result of:
        0.04620442 = score(doc=2623,freq=4.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.28811008 = fieldWeight in 2623, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.046875 = fieldNorm(doc=2623)
      0.042421322 = weight(_text_:22 in 2623) [ClassicSimilarity], result of:
        0.042421322 = score(doc=2623,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.23214069 = fieldWeight in 2623, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2623)
  0.5 = coord(1/2)

Abstract

Contemporary retrieval systems, which search across collections, usually ignore collection-level metadata. Alternative approaches, exploiting collection-level information, will require an understanding of the various kinds of relationships that can obtain between collection-level and item-level metadata. This paper outlines the problem and describes a project that is developing a logic-based framework for classifying collection/item metadata relationships. This framework will support (i) metadata specification developers defining metadata elements, (ii) metadata creators describing objects, and (iii) system designers implementing systems that take advantage of collection-level metadata. We present three examples of collection/item metadata relationship categories, attribute/value-propagation, value-propagation, and value-constraint and show that even in these simple cases a precise formulation requires modal notions in addition to first-order logic. These formulations are related to recent work in information retrieval and ontology evaluation.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

0.044312872 = product of:
  0.088625744 = sum of:
    0.088625744 = sum of:
      0.04620442 = weight(_text_:systems in 3261) [ClassicSimilarity], result of:
        0.04620442 = score(doc=3261,freq=4.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.28811008 = fieldWeight in 3261, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.046875 = fieldNorm(doc=3261)
      0.042421322 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
        0.042421322 = score(doc=3261,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.23214069 = fieldWeight in 3261, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=3261)
  0.5 = coord(1/2)

Abstract

Ontologies improve IR systems regarding its retrieval and presentation of information, which make the task of finding information more effective, efficient, and interactive. In this paper we argue that ontologies also greatly improve the engineering of such systems. We created a framework that uses ontology to drive the process of engineering an IR system. We developed a prototype that shows how a domain specialist without knowledge in the IR field can build an IR system with interactive components. The resulting system provides support for users not only to find their information needs but also to extend their state of knowledge. This way, our approach to ontology-enabled information retrieval addresses both the engineering aspect described here and also the usability aspect described elsewhere.

Date

28.11.2016 12:43:22

0.043804124 = product of:
  0.08760825 = sum of:
    0.08760825 = sum of:
      0.038116705 = weight(_text_:systems in 5095) [ClassicSimilarity], result of:
        0.038116705 = score(doc=5095,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.23767869 = fieldWeight in 5095, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.0546875 = fieldNorm(doc=5095)
      0.049491543 = weight(_text_:22 in 5095) [ClassicSimilarity], result of:
        0.049491543 = score(doc=5095,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = 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)
  0.5 = coord(1/2)

Abstract

This paper presents an abstract formalization of the notion of "facets". Facets are relational structures of units, relations and other facets selected for a certain purpose. Facets can be used to structure large knowledge representation systems into a hierarchical arrangement of consistent and independent subsystems (facets) that facilitate flexibility and combinations of different viewpoints or aspects. This paper describes the basic notions, facet characteristics and construction mechanisms. It then explicates the theory in an example of a faceted information retrieval system (FaIR)

Date

22. 1.2016 17:47:06

0.043804124 = product of:
  0.08760825 = sum of:
    0.08760825 = sum of:
      0.038116705 = weight(_text_:systems in 2654) [ClassicSimilarity], result of:
        0.038116705 = score(doc=2654,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.23767869 = fieldWeight in 2654, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.0546875 = fieldNorm(doc=2654)
      0.049491543 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
        0.049491543 = score(doc=2654,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = 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)
  0.5 = coord(1/2)

Abstract

Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.

Date

22. 1.2016 17:30:31

0.041441064 = product of:
  0.08288213 = sum of:
    0.08288213 = product of:
      0.24864638 = sum of:
        0.24864638 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
          0.24864638 = score(doc=400,freq=2.0), product of:
            0.4424171 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052184064 = queryNorm
            0.56201804 = fieldWeight in 400, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=400)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

0.03754639 = product of:
  0.07509278 = sum of:
    0.07509278 = sum of:
      0.03267146 = weight(_text_:systems in 3387) [ClassicSimilarity], result of:
        0.03267146 = score(doc=3387,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.2037246 = fieldWeight in 3387, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.046875 = fieldNorm(doc=3387)
      0.042421322 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
        0.042421322 = score(doc=3387,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.23214069 = fieldWeight in 3387, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=3387)
  0.5 = coord(1/2)

Abstract

Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.

Date

1. 8.2010 12:35:22

0.03754639 = product of:
  0.07509278 = sum of:
    0.07509278 = sum of:
      0.03267146 = weight(_text_:systems in 2230) [ClassicSimilarity], result of:
        0.03267146 = score(doc=2230,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.2037246 = fieldWeight in 2230, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.046875 = fieldNorm(doc=2230)
      0.042421322 = weight(_text_:22 in 2230) [ClassicSimilarity], result of:
        0.042421322 = score(doc=2230,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.23214069 = fieldWeight in 2230, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2230)
  0.5 = coord(1/2)

Abstract

We present a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and occurrence levels. Concepts and relationships among them are represented at the conceptual level. The expression level represents natural language expressions for concepts. Each expression has one or more matching models at the occurrence level. Each model specifies the matching of the expression in database indices built in varying ways. The data model supports a concept-based query expansion and formulation tool, the ExpansionTool, for environments providing heterogeneous IR systems. Expansion is controlled by adjustable matching reliability.

Source

Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al

0.03128866 = product of:
  0.06257732 = sum of:
    0.06257732 = sum of:
      0.027226217 = weight(_text_:systems in 633) [ClassicSimilarity], result of:
        0.027226217 = score(doc=633,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.1697705 = fieldWeight in 633, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.0390625 = fieldNorm(doc=633)
      0.0353511 = weight(_text_:22 in 633) [ClassicSimilarity], result of:
        0.0353511 = score(doc=633,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.19345059 = fieldWeight in 633, 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=633)
  0.5 = coord(1/2)

Abstract

Considering the characteristics of hypertext systems and problems such as cognitive overload and the disorientation of users, this project studies subject hypertext documents that have undergone conceptual structuring using facets for content representation and improvement of information retrieval during navigation. The main objective was to assess the possibility of the application of topic map technology for automating the compatibilization process of these structures. For this purpose, two dissertations from the UFMG Information Science Post-Graduation Program were adopted as samples. Both dissertations had been duly analyzed and structured on the MHTX (Hypertextual Map) prototype database. The faceted structures of both dissertations, which had been represented in conceptual maps, were then converted into topic maps. It was then possible to use the merge property of the topic maps to promote the semantic interrelationship between the maps and, consequently, between the hypertextual information resources proper. The merge results were then analyzed in the light of theories dealing with the compatibilization of languages developed within the realm of information technology and librarianship from the 1960s on. The main goals accomplished were: (a) the detailed conceptualization of the merge process of the topic maps, considering the possible compatibilization levels and the applicability of this technology in the integration of faceted structures; and (b) the production of a detailed sequence of steps that may be used in the implementation of topic maps based on faceted structures.

Date

22. 2.2013 11:39:23

0.03128866 = product of:
  0.06257732 = sum of:
    0.06257732 = sum of:
      0.027226217 = weight(_text_:systems in 2645) [ClassicSimilarity], result of:
        0.027226217 = score(doc=2645,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.1697705 = fieldWeight in 2645, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2645)
      0.0353511 = weight(_text_:22 in 2645) [ClassicSimilarity], result of:
        0.0353511 = score(doc=2645,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.19345059 = fieldWeight in 2645, 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=2645)
  0.5 = coord(1/2)

Abstract

Major efforts have been conducted on ontology learning, that is, semiautomatic processes for the construction of domain ontologies from diverse sources of information. In the past few years, a research trend has focused on the construction of educational ontologies, that is, ontologies to be used for educational purposes. The identification of the terminology is crucial to build ontologies. Term extraction techniques allow the identification of the domain-related terms from electronic resources. This paper presents LiTeWi, a novel method that combines current unsupervised term extraction approaches for creating educational ontologies for technology supported learning systems from electronic textbooks. LiTeWi uses Wikipedia as an additional information source. Wikipedia contains more than 30 million articles covering the terminology of nearly every domain in 288 languages, which makes it an appropriate generic corpus for term extraction. Furthermore, given that its content is available in several languages, it promotes both domain and language independence. LiTeWi is aimed at being used by teachers, who usually develop their didactic material from textbooks. To evaluate its performance, LiTeWi was tuned up using a textbook on object oriented programming and then tested with two textbooks of different domains-astronomy and molecular biology.

Date

22. 1.2016 12:38:14

0.029541915 = product of:
  0.05908383 = sum of:
    0.05908383 = sum of:
      0.030802948 = weight(_text_:systems in 1436) [ClassicSimilarity], result of:
        0.030802948 = score(doc=1436,freq=4.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.19207339 = fieldWeight in 1436, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.03125 = fieldNorm(doc=1436)
      0.028280882 = weight(_text_:22 in 1436) [ClassicSimilarity], result of:
        0.028280882 = score(doc=1436,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.15476047 = fieldWeight in 1436, 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=1436)
  0.5 = coord(1/2)

Abstract

The development of complex projects in the aerospace industry is based on the collaboration of geographically distributed teams and companies. In this context, the need of sharing different types of data and information is a key factor to assure the successful execution of the projects. In the case of European projects, the ECSS standards provide a normative framework that specifies, among other requirements, the different document types, information items and artifacts that need to be generated. The specification of the characteristics of these information items are usually incorporated as annex to the different ECSS standards, and they provide the intended purpose, scope, and structure of the documents and information items. In these standards, documents or deliverables should not be considered as independent items, but as the results of packaging different information artifacts for their delivery between the involved parties. Successful information integration and knowledge exchange cannot be based exclusively on the conceptual definition of information types. It also requires the definition of methods and techniques for serializing and exchanging these documents and artifacts. This area is not covered by ECSS standards, and the definition of these data schemas would improve the opportunity for improving collaboration processes among companies. This paper describes the development of an OWL-based ontology to manage the different artifacts and information items requested in the European Space Agency (ESA) ECSS standards for SW development. The ECSS set of standards is the main reference in aerospace projects in Europe, and in addition to engineering and managerial requirements they provide a set of DRD (Document Requirements Documents) with the structure of the different documents and records necessary to manage projects and describe intermediate information products and final deliverables. Information integration is a must-have in aerospace projects, where different players need to collaborate and share data during the life cycle of the products about requirements, design elements, problems, etc. The proposed ontology provides the basis for building advanced information systems where the information coming from different companies and institutions can be integrated into a coherent set of related data. It also provides a conceptual framework to enable the development of interfaces and gateways between the different tools and information systems used by the different players in aerospace projects.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.027226217 = product of:
  0.054452434 = sum of:
    0.054452434 = product of:
      0.10890487 = sum of:
        0.10890487 = weight(_text_:systems in 4319) [ClassicSimilarity], result of:
          0.10890487 = score(doc=4319,freq=32.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.679082 = fieldWeight in 4319, product of:
              5.656854 = tf(freq=32.0), with freq of:
                32.0 = termFreq=32.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4319)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Software Engineering, Computer Engineering, and Systems Engineering and Sciences. Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering includes selected papers form the conference proceedings of the International Conference on Systems, Computing Sciences and Software Engineering (SCSS 2007) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2007).

Content

Inhalt: Image and Pattern Recognition: Compression, Image processing, Signal Processing Architectures, Signal Processing for Communication, Signal Processing Implementation, Speech Compression, and Video Coding Architectures. Languages and Systems: Algorithms, Databases, Embedded Systems and Applications, File Systems and I/O, Geographical Information Systems, Kernel and OS Structures, Knowledge Based Systems, Modeling and Simulation, Object Based Software Engineering, Programming Languages, and Programming Models and tools. Parallel Processing: Distributed Scheduling, Multiprocessing, Real-time Systems, Simulation Modeling and Development, and Web Applications. New trends in computing: Computers for People of Special Needs, Fuzzy Inference, Human Computer Interaction, Incremental Learning, Internet-based Computing Models, Machine Intelligence, Natural Language Processing, Neural Networks, and Online Decision Support System

LCSH

Computer Systems Organization and Communication Networks
Software Engineering/Programming and Operating Systems

Subject

Computer Systems Organization and Communication Networks
Software Engineering/Programming and Operating Systems

0.021902062 = product of:
  0.043804124 = sum of:
    0.043804124 = sum of:
      0.019058352 = weight(_text_:systems in 1633) [ClassicSimilarity], result of:
        0.019058352 = score(doc=1633,freq=2.0), product of:
          0.16037072 = queryWeight, product of:
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.052184064 = queryNorm
          0.118839346 = fieldWeight in 1633, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0731742 = idf(docFreq=5561, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1633)
      0.024745772 = weight(_text_:22 in 1633) [ClassicSimilarity], result of:
        0.024745772 = score(doc=1633,freq=2.0), product of:
          0.1827397 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052184064 = queryNorm
          0.1354154 = fieldWeight in 1633, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1633)
  0.5 = coord(1/2)

Abstract

Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

Date

20. 1.2015 18:30:22