Search (83 results, page 1 of 5)

0.022994814 = product of:
  0.057487037 = sum of:
    0.025570497 = weight(_text_:und in 539) [ClassicSimilarity], result of:
      0.025570497 = score(doc=539,freq=2.0), product of:
        0.10442211 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.047114085 = queryNorm
        0.24487628 = fieldWeight in 539, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.078125 = fieldNorm(doc=539)
    0.03191654 = product of:
      0.06383308 = sum of:
        0.06383308 = weight(_text_:22 in 539) [ClassicSimilarity], result of:
          0.06383308 = score(doc=539,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = 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)
  0.4 = coord(2/5)

Date

26.12.2011 13:22:07

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.017203344 = product of:
  0.043008357 = sum of:
    0.02510901 = weight(_text_:information in 4329) [ClassicSimilarity], result of:
      0.02510901 = score(doc=4329,freq=10.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.3035872 = fieldWeight in 4329, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4329)
    0.017899347 = weight(_text_:und in 4329) [ClassicSimilarity], result of:
      0.017899347 = score(doc=4329,freq=2.0), product of:
        0.10442211 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.047114085 = queryNorm
        0.17141339 = fieldWeight in 4329, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4329)
  0.4 = coord(2/5)

Abstract

Plenty of contemporary attempts to search exist that are associated with the area of Semantic Web. But which of them qualify as information retrieval for the Semantic Web? Do such approaches exist? To answer these questions we take a look at the nature of the Semantic Web and Semantic Desktop and at definitions for information and data retrieval. We survey current approaches referred to by their authors as information retrieval for the Semantic Web or that use Semantic Web technology for search.

Content

Enthält einen Überblick über Modelle, Systeme und Projekte

Source

Lernen - Wissen - Adaption : workshop proceedings / LWA 2007, Halle, September 2007. Martin Luther University Halle-Wittenberg, Institute for Informatics, Databases and Information Systems. Hrsg.: Alexander Hinneburg

0.016268773 = product of:
  0.04067193 = sum of:
    0.021522008 = weight(_text_:information in 3261) [ClassicSimilarity], result of:
      0.021522008 = score(doc=3261,freq=10.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.2602176 = fieldWeight in 3261, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=3261)
    0.019149924 = product of:
      0.038299847 = sum of:
        0.038299847 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
          0.038299847 = score(doc=3261,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = 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)
  0.4 = coord(2/5)

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

0.015359918 = product of:
  0.038399793 = sum of:
    0.019249868 = weight(_text_:information in 4820) [ClassicSimilarity], result of:
      0.019249868 = score(doc=4820,freq=8.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.23274569 = fieldWeight in 4820, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4820)
    0.019149924 = product of:
      0.038299847 = sum of:
        0.038299847 = weight(_text_:22 in 4820) [ClassicSimilarity], result of:
          0.038299847 = score(doc=4820,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = 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)
  0.4 = coord(2/5)

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

0.015288765 = product of:
  0.03822191 = sum of:
    0.015880331 = weight(_text_:information in 4330) [ClassicSimilarity], result of:
      0.015880331 = score(doc=4330,freq=4.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.1920054 = fieldWeight in 4330, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4330)
    0.022341577 = product of:
      0.044683155 = sum of:
        0.044683155 = weight(_text_:22 in 4330) [ClassicSimilarity], result of:
          0.044683155 = score(doc=4330,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = queryNorm
            0.2708308 = fieldWeight in 4330, 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=4330)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

One vision of the Semantic Web is that it will be much like the Web we know today, except that documents will be enriched by annotations in machine understandable markup. These annotations will provide metadata about the documents as well as machine interpretable statements capturing some of the meaning of document content. We discuss how the information retrieval paradigm might be recast in such an environment. We suggest that retrieval can be tightly bound to inference. Doing so makes today's Web search engines useful to Semantic Web inference engines, and causes improvements in either retrieval or inference to lead directly to improvements in the other.

Date

12. 2.2011 17:35:22

0.013511873 = product of:
  0.03377968 = sum of:
    0.015880331 = weight(_text_:information in 572) [ClassicSimilarity], result of:
      0.015880331 = score(doc=572,freq=4.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.1920054 = fieldWeight in 572, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=572)
    0.017899347 = weight(_text_:und in 572) [ClassicSimilarity], result of:
      0.017899347 = score(doc=572,freq=2.0), product of:
        0.10442211 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.047114085 = queryNorm
        0.17141339 = fieldWeight in 572, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0546875 = fieldNorm(doc=572)
  0.4 = coord(2/5)

Abstract

We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.013428268 = product of:
  0.03357067 = sum of:
    0.01122909 = weight(_text_:information in 2654) [ClassicSimilarity], result of:
      0.01122909 = score(doc=2654,freq=2.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.13576832 = fieldWeight in 2654, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2654)
    0.022341577 = product of:
      0.044683155 = sum of:
        0.044683155 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.044683155 = score(doc=2654,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = 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)
  0.4 = coord(2/5)

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

0.011651375 = product of:
  0.029128438 = sum of:
    0.01122909 = weight(_text_:information in 604) [ClassicSimilarity], result of:
      0.01122909 = score(doc=604,freq=2.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.13576832 = fieldWeight in 604, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=604)
    0.017899347 = weight(_text_:und in 604) [ClassicSimilarity], result of:
      0.017899347 = score(doc=604,freq=2.0), product of:
        0.10442211 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.047114085 = queryNorm
        0.17141339 = fieldWeight in 604, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0546875 = fieldNorm(doc=604)
  0.4 = coord(2/5)

Abstract

iQvoc is a new open source SKOS-XL vocabulary management tool developed by the Federal Environment Agency, Germany, and innoQ Deutschland GmbH. Its immediate purpose is maintaining and publishing reference vocabularies in the upcoming Linked Data cloud of environmental information, but it may be easily adapted to host any SKOS- XL compliant vocabulary. iQvoc is implemented as a Ruby on Rails application running on top of JRuby - the Java implementation of the Ruby Programming Language. To increase the user experience when editing content, iQvoc uses heavily the JavaScript library jQuery.

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.011509943 = product of:
  0.028774858 = sum of:
    0.009624934 = weight(_text_:information in 1652) [ClassicSimilarity], result of:
      0.009624934 = score(doc=1652,freq=2.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.116372846 = fieldWeight in 1652, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1652)
    0.019149924 = product of:
      0.038299847 = sum of:
        0.038299847 = weight(_text_:22 in 1652) [ClassicSimilarity], result of:
          0.038299847 = score(doc=1652,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = queryNorm
            0.23214069 = fieldWeight in 1652, 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=1652)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

This document is the formal definition of the CIDOC Conceptual Reference Model ("CRM"), a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous cultural heritage information. The CRM is the culmination of more than a decade of standards development work by the International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). Work on the CRM itself began in 1996 under the auspices of the ICOM-CIDOC Documentation Standards Working Group. Since 2000, development of the CRM has been officially delegated by ICOM-CIDOC to the CIDOC CRM Special Interest Group, which collaborates with the ISO working group ISO/TC46/SC4/WG9 to bring the CRM to the form and status of an International Standard.

Date

6. 8.2010 14:22:28

0.011509943 = product of:
  0.028774858 = sum of:
    0.009624934 = weight(_text_:information in 2655) [ClassicSimilarity], result of:
      0.009624934 = score(doc=2655,freq=2.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.116372846 = fieldWeight in 2655, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2655)
    0.019149924 = product of:
      0.038299847 = sum of:
        0.038299847 = weight(_text_:22 in 2655) [ClassicSimilarity], result of:
          0.038299847 = score(doc=2655,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = 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)
  0.4 = coord(2/5)

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

0.009986892 = product of:
  0.02496723 = sum of:
    0.009624934 = weight(_text_:information in 4641) [ClassicSimilarity], result of:
      0.009624934 = score(doc=4641,freq=2.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.116372846 = fieldWeight in 4641, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4641)
    0.015342298 = weight(_text_:und in 4641) [ClassicSimilarity], result of:
      0.015342298 = score(doc=4641,freq=2.0), product of:
        0.10442211 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.047114085 = queryNorm
        0.14692576 = fieldWeight in 4641, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.046875 = fieldNorm(doc=4641)
  0.4 = coord(2/5)

Abstract

This paper presents an overview of the work in progress at the W3C to produce a standard conversion of WordNet to the RDF/OWL representation language in use in the SemanticWeb community. Such a standard representation is useful to provide application developers a high-quality resource and to promote interoperability. Important requirements in this conversion process are that it should be complete and should stay close to WordNet's conceptual model. The paper explains the steps taken to produce the conversion and details design decisions such as the composition of the class hierarchy and properties, the addition of suitable OWL semantics and the chosen format of the URIs. Additional topics include a strategy to incorporate OWL and RDFS semantics in one schema such that both RDF(S) infrastructure and OWL infrastructure can interpret the information correctly, problems encountered in understanding the Prolog source files and the description of the two versions that are provided (Basic and Full) to accommodate different usages of WordNet.

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.006657929 = product of:
  0.016644822 = sum of:
    0.006416623 = weight(_text_:information in 4639) [ClassicSimilarity], result of:
      0.006416623 = score(doc=4639,freq=2.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.0775819 = fieldWeight in 4639, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=4639)
    0.010228199 = weight(_text_:und in 4639) [ClassicSimilarity], result of:
      0.010228199 = score(doc=4639,freq=2.0), product of:
        0.10442211 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.047114085 = queryNorm
        0.09795051 = fieldWeight in 4639, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.03125 = fieldNorm(doc=4639)
  0.4 = coord(2/5)

Abstract

We refine the problem statement into three research questions. The first two focus on the problem of conversion of a vocabulary to a Semantic Web representation from its original format. Conversion of a vocabulary to a representation in a Semantic Web language is necessary to make the vocabulary available to SemanticWeb applications. In the last question we focus on integration of collection metadata schemas in a way that allows for vocabulary representations as produced by our methods. Academisch proefschrift ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, Dutch Research School for Information and Knowledge Systems.

Theme

Konzeption und Anwendung des Prinzips Thesaurus

0.005106646 = product of:
  0.025533231 = sum of:
    0.025533231 = product of:
      0.051066462 = sum of:
        0.051066462 = weight(_text_:22 in 4685) [ClassicSimilarity], result of:
          0.051066462 = score(doc=4685,freq=2.0), product of:
            0.1649855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047114085 = queryNorm
            0.30952093 = fieldWeight in 4685, 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=4685)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Date

14. 8.2011 13:33:22

0.004491636 = product of:
  0.02245818 = sum of:
    0.02245818 = weight(_text_:information in 4121) [ClassicSimilarity], result of:
      0.02245818 = score(doc=4121,freq=8.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.27153665 = fieldWeight in 4121, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4121)
  0.2 = coord(1/5)

Abstract

Intuition suggests that one way to enhance the information retrieval process would be the use of phrases to characterize the contents of text. A number of researchers, however, have noted that phrases alone do not improve retrieval effectiveness. In this paper we briefly review the use of phrases in information retrieval and then suggest extensions to this paradigm using semantic information. We claim that semantic processing, which can be viewed as expressing relations between the concepts represented by phrases, will in fact enhance retrieval effectiveness. The availability of the UMLS® domain model, which we exploit extensively, significantly contributes to the feasibility of this processing.

0.004491636 = product of:
  0.02245818 = sum of:
    0.02245818 = weight(_text_:information in 667) [ClassicSimilarity], result of:
      0.02245818 = score(doc=667,freq=8.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.27153665 = fieldWeight in 667, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=667)
  0.2 = coord(1/5)

Abstract

We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.

Series

¬The Information retrieval series, vol 43

Source

Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]

0.0042563058 = product of:
  0.02128153 = sum of:
    0.02128153 = weight(_text_:information in 758) [ClassicSimilarity], result of:
      0.02128153 = score(doc=758,freq=22.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.25731003 = fieldWeight in 758, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=758)
  0.2 = coord(1/5)

Abstract

Innovative research institutes rely on the availability of complete and accurate information about new research and development, and it is the business of information providers such as Elsevier to provide the required information in a cost-effective way. It is very likely that the semantic web will make an important contribution to this effort, since it facilitates access to an unprecedented quantity of data. However, with the unremitting growth of scientific information, integrating access to all this information remains a significant problem, not least because of the heterogeneity of the information sources involved - sources which may use different syntactic standards (syntactic heterogeneity), organize information in very different ways (structural heterogeneity) and even use different terminologies to refer to the same information (semantic heterogeneity). The ability to address these different kinds of heterogeneity is the key to integrated access. Thesauri have already proven to be a core technology to effective information access as they provide controlled vocabularies for indexing information, and thereby help to overcome some of the problems of free-text search by relating and grouping relevant terms in a specific domain. However, currently there is no open architecture which supports the use of these thesauri for querying other data sources. For example, when we move from the centralized and controlled use of EMTREE within EMBASE.com to a distributed setting, it becomes crucial to improve access to the thesaurus by means of a standardized representation using open data standards that allow for semantic qualifications. In general, mental models and keywords for accessing data diverge between subject areas and communities, and so many different ontologies have been developed. An ideal architecture must therefore support the disclosure of distributed and heterogeneous data sources through different ontologies. The aim of the DOPE project (Drug Ontology Project for Elsevier) is to investigate the possibility of providing access to multiple information sources in the area of life science through a single interface.

0.0042441967 = product of:
  0.021220984 = sum of:
    0.021220984 = weight(_text_:information in 231) [ClassicSimilarity], result of:
      0.021220984 = score(doc=231,freq=14.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.256578 = fieldWeight in 231, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=231)
  0.2 = coord(1/5)

Abstract

As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

0.003889871 = product of:
  0.019449355 = sum of:
    0.019449355 = weight(_text_:information in 3886) [ClassicSimilarity], result of:
      0.019449355 = score(doc=3886,freq=6.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.23515764 = fieldWeight in 3886, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3886)
  0.2 = coord(1/5)

Abstract

This paper is about the relationship between the topic map and RDF standards families. It compares the two technologies and looks at ways to make it easier for users to live in a world where both technologies are used. This is done by looking at how to convert information back and forth between the two technologies, how to convert schema information, and how to do queries across both information representations. Ways to achieve all of these goals are presented. This paper extends and improves on earlier work on the same subject, described in [Garshol01b]. This paper was first published in the proceedings of XML Europe 2003, 5-8 May 2003, organized by IDEAlliance, London, UK.

0.003889871 = product of:
  0.019449355 = sum of:
    0.019449355 = weight(_text_:information in 4724) [ClassicSimilarity], result of:
      0.019449355 = score(doc=4724,freq=6.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.23515764 = fieldWeight in 4724, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4724)
  0.2 = coord(1/5)

Abstract

Topic maps are a new ISO standard for describing knowledge structures and associating them with information resources. As such they constitute an enabling technology for knowledge management. Dubbed "the GPS of the information universe", topic maps are also destined to provide powerful new ways of navigating large and interconnected corpora. While it is possible to represent immensely complex structures using topic maps, the basic concepts of the model - Topics, Associations, and Occurrences (TAO) - are easily grasped. This paper provides a non-technical introduction to these and other concepts (the IFS and BUTS of topic maps), relating them to things that are familiar to all of us from the realms of publishing and information management, and attempting to convey some idea of the uses to which topic maps will be put in the future.

0.0038499737 = product of:
  0.019249868 = sum of:
    0.019249868 = weight(_text_:information in 2799) [ClassicSimilarity], result of:
      0.019249868 = score(doc=2799,freq=18.0), product of:
        0.08270773 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047114085 = queryNorm
        0.23274568 = fieldWeight in 2799, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=2799)
  0.2 = coord(1/5)

Abstract

Grounded on a strong belief that ontologies enhance the performance of information retrieval systems, there has been an upsurge of interest in ontologies. Its importance is identified in diverse research fields such as knowledge engineering, knowledge representation, qualitative modeling, language engineering, database design, information integration, object-oriented analysis, information retrieval and extraction, knowledge management and agent-based systems design (Guarino, 1998). While the role-played by ontologies, automatically lends a place of legitimacy for these tools, research in this area gains greater significance in the wake of various challenges faced in the contemporary digital environment. With the objective of overcoming various pitfalls associated with current search mechanisms, ontologies are increasingly used for developing efficient information retrieval systems. An indicator of research interest in the area of ontology is the Swoogle, a search engine for Semantic Web documents, terms and data found on the Web (Ding, Li et al, 2004). Given the complex nature of the digital content archived in digital libraries, ontologies can be employed for designing efficient forms of information retrieval in digital libraries. Knowledge representation assumes greater significance due to its crucial role in ontology development. These systems aid in developing intelligent information systems, wherein the notion of intelligence implies the ability of the system to find implicit consequences of its explicitly represented knowledge (Baader and Nutt, 2003). Knowledge representation formalisms such as 'Description Logics' are used to obtain explicit knowledge representation of the subject domain. These representations are developed into ontologies, which are used for developing intelligent information systems. Against this backdrop, the paper examines the use of Description Logics for conceptually modeling a chosen domain, which would be utilized for developing domain ontologies. The knowledge representation languages identified for this purpose are Web Ontology Language (OWL) and KArlsruhe ONtology (KAON) language. Drawing upon the various technical constructs in developing ontology-based information systems, the paper explains the working of the prototypes and also presents a comparative study of the two prototypes.

Theme

Information Gateway