Search (77 results, page 1 of 4)

0.09933432 = product of:
  0.19866864 = sum of:
    0.04966716 = product of:
      0.14900148 = sum of:
        0.14900148 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
          0.14900148 = score(doc=701,freq=2.0), product of:
            0.39767802 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046906993 = 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)
    0.14900148 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
      0.14900148 = score(doc=701,freq=2.0), product of:
        0.39767802 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046906993 = 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)
  0.5 = coord(2/4)

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.

0.057686016 = product of:
  0.11537203 = sum of:
    0.041913155 = weight(_text_:services in 3387) [ClassicSimilarity], result of:
      0.041913155 = score(doc=3387,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.2433798 = fieldWeight in 3387, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=3387)
    0.07345888 = sum of:
      0.035327382 = weight(_text_:management in 3387) [ClassicSimilarity], result of:
        0.035327382 = score(doc=3387,freq=2.0), product of:
          0.15810528 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046906993 = queryNorm
          0.22344214 = fieldWeight in 3387, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046875 = fieldNorm(doc=3387)
      0.038131498 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
        0.038131498 = score(doc=3387,freq=2.0), product of:
          0.1642603 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046906993 = 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(2/4)

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

0.050815754 = product of:
  0.10163151 = sum of:
    0.06985526 = weight(_text_:services in 539) [ClassicSimilarity], result of:
      0.06985526 = score(doc=539,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.405633 = fieldWeight in 539, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.078125 = fieldNorm(doc=539)
    0.03177625 = product of:
      0.0635525 = sum of:
        0.0635525 = weight(_text_:22 in 539) [ClassicSimilarity], result of:
          0.0635525 = score(doc=539,freq=2.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = 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)
  0.5 = coord(2/4)

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

0.039717898 = product of:
  0.079435796 = sum of:
    0.05588421 = weight(_text_:services in 4686) [ClassicSimilarity], result of:
      0.05588421 = score(doc=4686,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.3245064 = fieldWeight in 4686, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0625 = fieldNorm(doc=4686)
    0.023551589 = product of:
      0.047103178 = sum of:
        0.047103178 = weight(_text_:management in 4686) [ClassicSimilarity], result of:
          0.047103178 = score(doc=4686,freq=2.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.29792285 = fieldWeight in 4686, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0625 = fieldNorm(doc=4686)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

This document specifies usage scenarios, goals and requirements for a web ontology language. An ontology formally defines a common set of terms that are used to describe and represent a domain. Ontologies can be used by automated tools to power advanced services such as more accurate web search, intelligent software agents and knowledge management.

0.036253784 = product of:
  0.07250757 = sum of:
    0.041913155 = weight(_text_:services in 229) [ClassicSimilarity], result of:
      0.041913155 = score(doc=229,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.2433798 = fieldWeight in 229, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=229)
    0.030594414 = product of:
      0.06118883 = sum of:
        0.06118883 = weight(_text_:management in 229) [ClassicSimilarity], result of:
          0.06118883 = score(doc=229,freq=6.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.38701317 = fieldWeight in 229, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046875 = fieldNorm(doc=229)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.

Source

Web semantics: science, services and agents on the World Wide Web. 4(2006) no.1, S.14-28

0.03492763 = product of:
  0.13971052 = sum of:
    0.13971052 = weight(_text_:services in 7190) [ClassicSimilarity], result of:
      0.13971052 = score(doc=7190,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.811266 = fieldWeight in 7190, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.15625 = fieldNorm(doc=7190)
  0.25 = coord(1/4)

0.033446696 = product of:
  0.06689339 = sum of:
    0.041913155 = weight(_text_:services in 3379) [ClassicSimilarity], result of:
      0.041913155 = score(doc=3379,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.2433798 = fieldWeight in 3379, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=3379)
    0.024980234 = product of:
      0.049960468 = sum of:
        0.049960468 = weight(_text_:management in 3379) [ClassicSimilarity], result of:
          0.049960468 = score(doc=3379,freq=4.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.31599492 = fieldWeight in 3379, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046875 = fieldNorm(doc=3379)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The main motivation of this chapter was to gather existing requirements and solutions, and to present a generic architectural design of semantic digital libraries. This design is meant to answer a number of requirements, such as interoperability or ability to exchange resources and solutions, and set up the foundations for the best practices in the new domain of semantic digital libraries. We start by presenting the library from different high-level perspectives, i.e., user (see Sect. 2) and metadata (see Sect. 1) perspective; this overview narrows the scope and puts emphasis on certain aspects related to the system perspective, i.e., the architecture of the actual digital library management system. We conclude by presenting the system architecture from three perspectives: top-down layered architecture (see Sect. 3), vertical architecture of core services (see Sect. 4), and stack of enabling infrastructures (see Sect. 5); based upon the observations and evaluation of the contemporary state of the art presented in the previous sections, these last three subsections will describe an in-depth model of the digital library management system.

0.032057434 = product of:
  0.06411487 = sum of:
    0.049395125 = weight(_text_:services in 672) [ClassicSimilarity], result of:
      0.049395125 = score(doc=672,freq=4.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.28682584 = fieldWeight in 672, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0390625 = fieldNorm(doc=672)
    0.014719742 = product of:
      0.029439485 = sum of:
        0.029439485 = weight(_text_:management in 672) [ClassicSimilarity], result of:
          0.029439485 = score(doc=672,freq=2.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.18620178 = fieldWeight in 672, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0390625 = fieldNorm(doc=672)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

This chapter describes not so much what digital libraries are but what digital libraries with semantic support could and should be. It discusses the nature of Knowledge Organization Systems (KOS) and how KOS can support digital library users. It projects a vision for designers to make and for users to demand better digital libraries. What is a digital library? The term \Digital Library" (DL) is used to refer to a range of systems, from digital object and metadata repositories, reference-linking systems, archives, and content management systems to complex systems that integrate advanced digital library services and support for research and practice communities. A DL may offer many technology-enabled functions and services that support users, both as information producers and as information users. Many of these functions appear in information systems that would not normally be considered digital libraries, making boundaries even more blurry. Instead of pursuing the hopeless quest of coming up with the definition of digital library, we present a framework that allows a clear and somewhat standardized description of any information system so that users can select the system(s) that best meet their requirements. Section 2 gives a broad outline for more detail see the DELOS DL Reference Model.

0.022958733 = product of:
  0.045917466 = sum of:
    0.027942104 = weight(_text_:services in 2654) [ClassicSimilarity], result of:
      0.027942104 = score(doc=2654,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.1622532 = fieldWeight in 2654, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.03125 = fieldNorm(doc=2654)
    0.017975362 = product of:
      0.035950724 = sum of:
        0.035950724 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.035950724 = score(doc=2654,freq=4.0), product of:
            0.1642603 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046906993 = queryNorm
            0.21886435 = fieldWeight in 2654, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=2654)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.

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

0.019525139 = product of:
  0.078100555 = sum of:
    0.078100555 = weight(_text_:services in 2661) [ClassicSimilarity], result of:
      0.078100555 = score(doc=2661,freq=10.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.45351148 = fieldWeight in 2661, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2661)
  0.25 = coord(1/4)

Abstract

Research on semantic web services promises greater interoperability among software agents and web services by enabling content-based automated service discovery and interaction and by utilizing. Although this is to be based on use of shared ontologies published on the semantic web, services produced and described by different developers may well use different, perhaps partly overlapping, sets of ontologies. Interoperability will depend on ontology mappings and architectures supporting the associated translation processes. The question we ask is, does the traditional approach of introducing mediator agents to translate messages between requestors and services work in such an open environment? This article reviews some of the processing assumptions that were made in the development of the semantic web service modeling ontology OWL-S and argues that, as a practical matter, the translation function cannot always be isolated in mediators. Ontology mappings need to be published on the semantic web just as ontologies themselves are. The translation for service discovery, service process model interpretation, task negotiation, service invocation, and response interpretation may then be distributed to various places in the architecture so that translation can be done in the specific goal-oriented informational contexts of the agents performing these processes. We present arguments for assigning translation responsibility to particular agents in the cases of service invocation, response translation, and match- making.

0.017288294 = product of:
  0.069153175 = sum of:
    0.069153175 = weight(_text_:services in 538) [ClassicSimilarity], result of:
      0.069153175 = score(doc=538,freq=4.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.40155616 = fieldWeight in 538, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0546875 = fieldNorm(doc=538)
  0.25 = coord(1/4)

Abstract

The presentation will briefly introduce a series of major principles for bringing subject terminology to the network level. A closer look at one KOS in particular, the Dewey Decimal Classification, should help to gain more insight into the perceived difficulties and potential benefits of building taxonomy services out and on top of classic large-scale vocabularies or taxonomies.

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".

0.014894212 = product of:
  0.029788423 = sum of:
    0.020956578 = weight(_text_:services in 2863) [ClassicSimilarity], result of:
      0.020956578 = score(doc=2863,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.1216899 = fieldWeight in 2863, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0234375 = fieldNorm(doc=2863)
    0.008831846 = product of:
      0.017663691 = sum of:
        0.017663691 = weight(_text_:management in 2863) [ClassicSimilarity], result of:
          0.017663691 = score(doc=2863,freq=2.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.11172107 = fieldWeight in 2863, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0234375 = fieldNorm(doc=2863)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Aimed at students and professionals within Library and Information Services (LIS), this book is about the power and potential of ontologies to enhance the electronic search process. The book will compare search strategies and results in the current search environment and demonstrate how these could be transformed using ontologies and concept searching. Simple descriptions, visual representations, and examples of ontologies will bring a full understanding of how these concept maps are constructed to enhance retrieval through natural language queries. Readers will gain a sense of how ontologies are currently being used and how they could be applied in the future, encouraging them to think about how their own work and their users' search experiences could be enhanced by the creation of a customized ontology. Key Features Written by a librarian, for librarians (most work on ontologies is written and read by people in computer science and knowledge management) Written by a librarian who has created her own ontology and performed research on its capabilities Written in easily understandable language, with concepts broken down to the basics The Author Ms. King is the Information Specialist at the Center on Media and Child Health at Children's Hospital Boston. She is a graduate of Smith College (B.A.) and Simmons College (M.L.I.S.). She is an active member of the Special Libraries Association, and was the recipient of the 2005 SLA Innovation in Technology Award for the creation of a customized media effects ontology used for semantic searching. Readership The book is aimed at practicing librarians and information professionals as well as graduate students of Library and Information Science. Contents Introduction Part 1: Understanding Ontologies - organising knowledge; what is an ontology? How are ontologies different from other knowledge representations? How are ontologies currently being used? Key concepts Ontologies in semantic search - determining whether a search was successful; what does semantic search have to offer? Semantic techniques; semantic searching behind the scenes; key concepts Creating an ontology - how to create an ontology; key concepts Building an ontology from existing components - choosing components; customizing your knowledge structure; key concepts Part 2: Semantic Technologies Natural language processing - tagging parts of speech; grammar-based NLP; statistical NLP; semantic analysis,; current applications of NLP; key concepts Using metadata to add semantic information - structured languages; metadata tagging; semantic tagging; key concepts Other semantic capabilities - semantic classification; synsets; topic maps; rules and inference; key concepts Part 3: Case Studies: Theory into Practice Biogen Idec: using semantics in drug discovery research - Biogen Idec's solution; the future The Center on Media and Child Health: using an ontology to explore the effects of media - building the ontology; choosing the source; implementing and comparing to Boolean search; the future Partners HealthCare System: semantic technologies to improve clinical decision support - the medical appointment; partners healthcare system's solution; lessons learned; the future MINDSWAP: using ontologies to aid terrorism; intelligence gathering - building, using and maintaining the ontology; sharing information with other experts; future plans Part 4: Advanced Topics Languages for expressing ontologies - XML; RDF; OWL; SKOS; Ontology language features - comparison chart Tools for building ontologies - basic criteria when evaluating ontologies Part 5: Transitions to the Future

0.014818538 = product of:
  0.059274152 = sum of:
    0.059274152 = weight(_text_:services in 6014) [ClassicSimilarity], result of:
      0.059274152 = score(doc=6014,freq=4.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.344191 = fieldWeight in 6014, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=6014)
  0.25 = coord(1/4)

Abstract

Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general idea is that data and services are semantically described with respect to ontologies, which are formal specifications of a domain of interest, and can thus be shared and reused in a way such that the shared meaning specified by the ontology remains formally the same across different parties and applications. As the cost of creating ontologies is relatively high, different proposals have emerged for learning ontologies from structured and unstructured resources. In this article we examine the maturity of techniques for ontology learning from textual resources, addressing the question whether the state-of-the-art is mature enough to produce ontologies 'on demand'.

0.014818538 = product of:
  0.059274152 = sum of:
    0.059274152 = weight(_text_:services in 3383) [ClassicSimilarity], result of:
      0.059274152 = score(doc=3383,freq=4.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.344191 = fieldWeight in 3383, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=3383)
  0.25 = coord(1/4)

Abstract

The initial research on semantic digital libraries resulted in the design and implementation of JeromeDL; current research on online social networking and information discovery delivered new sets of features that were implemented in JeromeDL. Eventually, this digital library has been redesigned to follow the architecture of a social semantic digital library. JeromeDL describes each resource using three types of metadata: structure, bibliographic and community. It delivers services leveraging each of these information types. Annotations based on the structure and legacy metadata, and bibliographic ontology are rendered to the users in one, mixed, representation of library resources. Community annotations are managed by separate services, such as social semantic collaborative filtering or blogging component

0.013971052 = product of:
  0.05588421 = sum of:
    0.05588421 = weight(_text_:services in 537) [ClassicSimilarity], result of:
      0.05588421 = score(doc=537,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.3245064 = fieldWeight in 537, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0625 = fieldNorm(doc=537)
  0.25 = coord(1/4)

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".

0.012348781 = product of:
  0.049395125 = sum of:
    0.049395125 = weight(_text_:services in 1078) [ClassicSimilarity], result of:
      0.049395125 = score(doc=1078,freq=4.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.28682584 = fieldWeight in 1078, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1078)
  0.25 = coord(1/4)

Abstract

Domain ontologies very rarely model verbs as relations holding between concepts. However, the role of the verb as a central connecting element between concepts is undeniable. Verbs specify the interaction between the participants of some action or event by expressing relations between them. In parallel, it can be argued from an ontology engineering point of view that verbs express a relation between two classes that specify domain and range. The work described here is concerned with relation extraction for ontology extension along these lines. We describe a system (RelExt) that is capable of automatically identifying highly relevant triples (pairs of concepts connected by a relation) over concepts from an existing ontology. RelExt works by extracting relevant verbs and their grammatical arguments (i.e. terms) from a domain-specific text collection and computing corresponding relations through a combination of linguistic and statistical processing. The paper includes a detailed description of the system architecture and evaluation results on a constructed benchmark. RelExt has been developed in the context of the SmartWeb project, which aims at providing intelligent information services via mobile broadband devices on the FIFA World Cup that will be hosted in Germany in 2006. Such services include location based navigational information as well as question answering in the football domain.

0.012224671 = product of:
  0.048898686 = sum of:
    0.048898686 = weight(_text_:services in 504) [ClassicSimilarity], result of:
      0.048898686 = score(doc=504,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.28394312 = fieldWeight in 504, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.0546875 = fieldNorm(doc=504)
  0.25 = coord(1/4)

Abstract

This article introduces the Simple Knowledge Organisation System (SKOS), a Semantic Web language for representing controlled structured vocabularies, including thesauri, classification schemes, subject heading systems and taxonomies. SKOS provides a framework for publishing thesauri, classification schemes, and subject indexes on the Web, and for applying these systems to resource collections that are part of the SemanticWeb. SemanticWeb applications may harvest and merge SKOS data, to integrate and enhances retrieval service across multiple collections (e.g. libraries). This article also describes some alternatives for integrating Semantic Web services based on the Resource Description Framework (RDF) and SKOS into a distributed enterprise architecture.

0.011775794 = product of:
  0.047103178 = sum of:
    0.047103178 = product of:
      0.094206356 = sum of:
        0.094206356 = weight(_text_:management in 4407) [ClassicSimilarity], result of:
          0.094206356 = score(doc=4407,freq=8.0), product of:
            0.15810528 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046906993 = queryNorm
            0.5958457 = fieldWeight in 4407, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0625 = fieldNorm(doc=4407)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

The global economy is rapidly becoming more and more knowledge intensive. Knowledge is now widely recognized as the fourth production factor, on an equal footing with the traditional production factors of labour, capital and materials. Managing knowledge is as important as the traditional management of labour, capital and materials. In this book, we have shown how Semantic Web technology can make an important contribution to knowledge management.

Source

Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a

0.010478289 = product of:
  0.041913155 = sum of:
    0.041913155 = weight(_text_:services in 1638) [ClassicSimilarity], result of:
      0.041913155 = score(doc=1638,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.2433798 = fieldWeight in 1638, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=1638)
  0.25 = coord(1/4)

Source

Web semantics: science, services and agents on the World Wide Web. 7(2009) no.3, S.177-188

0.010478289 = product of:
  0.041913155 = sum of:
    0.041913155 = weight(_text_:services in 2519) [ClassicSimilarity], result of:
      0.041913155 = score(doc=2519,freq=2.0), product of:
        0.17221296 = queryWeight, product of:
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046906993 = queryNorm
        0.2433798 = fieldWeight in 2519, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6713707 = idf(docFreq=3057, maxDocs=44218)
          0.046875 = fieldNorm(doc=2519)
  0.25 = coord(1/4)

Abstract

Workforce development is a multidisciplinary domain in which policy, laws and regulations, social services, training and education, and information technology and systems are heavily involved. It is essential to have a semantic base accepted by the workforce development community for knowledge sharing and exchange. This paper describes how such a semantic base-the Workforce Open Knowledge Exchange (WOKE) Ontology-was built by using the adaptive modeling approach. The focus of this paper is to address questions such as how ontology designers should extract and model concepts obtained from different sources and what methodologies are useful along the steps of ontology development. The paper proposes a methodology framework "adaptive modeling" and explains the methodology through examples and some lessons learned from the process of developing the WOKE ontology.