Search (129 results, page 1 of 7)

0.03132359 = product of:
  0.10963256 = sum of:
    0.025709987 = weight(_text_:wide in 4404) [ClassicSimilarity], result of:
      0.025709987 = score(doc=4404,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.1958137 = fieldWeight in 4404, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.03125 = fieldNorm(doc=4404)
    0.05917687 = weight(_text_:web in 4404) [ClassicSimilarity], result of:
      0.05917687 = score(doc=4404,freq=36.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.6119082 = fieldWeight in 4404, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.03125 = fieldNorm(doc=4404)
    0.012762521 = weight(_text_:information in 4404) [ClassicSimilarity], result of:
      0.012762521 = score(doc=4404,freq=20.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.2453355 = fieldWeight in 4404, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=4404)
    0.0119831795 = weight(_text_:retrieval in 4404) [ClassicSimilarity], result of:
      0.0119831795 = score(doc=4404,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.13368362 = fieldWeight in 4404, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=4404)
  0.2857143 = coord(4/14)

Abstract

Significant progress has been made in technologies for publishing and distributing knowledge and information on the web. However, much of the published information is not organized, and it is hard to find answers to questions that require more than a keyword search. In general, one can say that the web is organizing itself. Information is often published in relatively ad hoc fashion. Typically, concern about the presentation of content has been limited to purely layout issues. This, combined with the fact that the representation language used on the World Wide Web (HTML) is mainly format-oriented, makes publishing on the WWW easy, giving it an enormous expressiveness. People add private, educational or organizational content to the web that is of an immensely diverse nature. Content on the web is growing closer to a real universal knowledge base, with one problem relatively undefined; the problem of the interpretation of its contents. Although widely acknowledged for its general and universal advantages, the increasing popularity of the web also shows us some major drawbacks. The developments of the information content on the web during the last year alone, clearly indicates the need for some changes. Perhaps one of the most significant problems with the web as a distributed information system is the difficulty of finding and comparing information.
Thus, there is a clear need for the web to become more semantic. The aim of introducing semantics into the web is to enhance the precision of search, but also enable the use of logical reasoning on web contents in order to answer queries. The CORPORUM OntoBuilder toolset is developed specifically for this task. It consists of a set of applications that can fulfil a variety of tasks, either as stand-alone tools, or augmenting each other. Important tasks that are dealt with by CORPORUM are related to document and information retrieval (find relevant documents, or support the user finding them), as well as information extraction (building a knowledge base from web documents to answer queries), information dissemination (summarizing strategies and information visualization), and automated document classification strategies. First versions of the toolset are encouraging in that they show large potential as a supportive technology for building up the Semantic Web. In this chapter, methods for transforming the current web into a semantic web are discussed, as well as a technical solution that can perform this task: the CORPORUM tool set. First, the toolset is introduced; followed by some pragmatic issues relating to the approach; then there will be a short overview of the theory in relation to CognIT's vision; and finally, a discussion on some of the applications that arose from the project.

Source

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

Theme

Semantic Web

0.027095761 = product of:
  0.09483516 = sum of:
    0.025709987 = weight(_text_:wide in 700) [ClassicSimilarity], result of:
      0.025709987 = score(doc=700,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.1958137 = fieldWeight in 700, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.03125 = fieldNorm(doc=700)
    0.039451245 = weight(_text_:web in 700) [ClassicSimilarity], result of:
      0.039451245 = score(doc=700,freq=16.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.4079388 = fieldWeight in 700, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.03125 = fieldNorm(doc=700)
    0.005707573 = weight(_text_:information in 700) [ClassicSimilarity], result of:
      0.005707573 = score(doc=700,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.10971737 = fieldWeight in 700, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=700)
    0.023966359 = weight(_text_:retrieval in 700) [ClassicSimilarity], result of:
      0.023966359 = score(doc=700,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.26736724 = fieldWeight in 700, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=700)
  0.2857143 = coord(4/14)

Abstract

The Semantic Web realization depends on the availability of a critical mass of metadata for the web content, associated with the respective formal knowledge about the world. We claim that the Semantic Web, at its current stage of development, is in a state of a critical need of metadata generation and usage schemata that are specific, well-defined and easy to understand. This paper introduces our vision for a holistic architecture for semantic annotation, indexing, and retrieval of documents with regard to extensive semantic repositories. A system (called KIM), implementing this concept, is presented in brief and it is used for the purposes of evaluation and demonstration. A particular schema for semantic annotation with respect to real-world entities is proposed. The underlying philosophy is that a practical semantic annotation is impossible without some particular knowledge modelling commitments. Our understanding is that a system for such semantic annotation should be based upon a simple model of real-world entity classes, complemented with extensive instance knowledge. To ensure the efficiency, ease of sharing, and reusability of the metadata, we introduce an upper-level ontology (of about 250 classes and 100 properties), which starts with some basic philosophical distinctions and then goes down to the most common entity types (people, companies, cities, etc.). Thus it encodes many of the domain-independent commonsense concepts and allows straightforward domain-specific extensions. On the basis of the ontology, a large-scale knowledge base of entity descriptions is bootstrapped, and further extended and maintained. Currently, the knowledge bases usually scales between 105 and 106 descriptions. Finally, this paper presents a semantically enhanced information extraction system, which provides automatic semantic annotation with references to classes in the ontology and to instances. The system has been running over a continuously growing document collection (currently about 0.5 million news articles), so it has been under constant testing and evaluation for some time now. On the basis of these semantic annotations, we perform semantic based indexing and retrieval where users can mix traditional information retrieval (IR) queries and ontology-based ones. We argue that such large-scale, fully automatic methods are essential for the transformation of the current largely textual web into a Semantic Web.

Source

Web semantics: science, services and agents on the World Wide Web. 2(2004) no.1, S.49-79

Theme

Semantic Web

0.026210284 = product of:
  0.122314654 = sum of:
    0.06458071 = weight(_text_:web in 4329) [ClassicSimilarity], result of:
      0.06458071 = score(doc=4329,freq=14.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.6677857 = fieldWeight in 4329, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4329)
    0.015792815 = weight(_text_:information in 4329) [ClassicSimilarity], result of:
      0.015792815 = score(doc=4329,freq=10.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.04194113 = weight(_text_:retrieval in 4329) [ClassicSimilarity], result of:
      0.04194113 = score(doc=4329,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.46789268 = fieldWeight in 4329, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4329)
  0.21428572 = coord(3/14)

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.

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

Theme

Semantic Web

0.022241924 = product of:
  0.10379565 = sum of:
    0.03856498 = weight(_text_:wide in 1638) [ClassicSimilarity], result of:
      0.03856498 = score(doc=1638,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.29372054 = fieldWeight in 1638, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=1638)
    0.05917687 = weight(_text_:web in 1638) [ClassicSimilarity], result of:
      0.05917687 = score(doc=1638,freq=16.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.6119082 = fieldWeight in 1638, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=1638)
    0.0060537956 = weight(_text_:information in 1638) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=1638,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 1638, 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=1638)
  0.21428572 = coord(3/14)

Abstract

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.

Source

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

Theme

Semantic Web

0.018461186 = product of:
  0.0861522 = sum of:
    0.057825863 = weight(_text_:web in 231) [ClassicSimilarity], result of:
      0.057825863 = score(doc=231,freq=22.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.59793836 = fieldWeight in 231, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=231)
    0.013347364 = weight(_text_:information in 231) [ClassicSimilarity], result of:
      0.013347364 = score(doc=231,freq=14.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.014978974 = weight(_text_:retrieval in 231) [ClassicSimilarity], result of:
      0.014978974 = score(doc=231,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 231, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=231)
  0.21428572 = coord(3/14)

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.

Source

Proceeding ISWC'07/ASWC'07 : Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Ed.: K. Aberer et al

Theme

Semantic Web

0.017402858 = product of:
  0.08121333 = sum of:
    0.032137483 = weight(_text_:wide in 4403) [ClassicSimilarity], result of:
      0.032137483 = score(doc=4403,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.24476713 = fieldWeight in 4403, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4403)
    0.038986187 = weight(_text_:web in 4403) [ClassicSimilarity], result of:
      0.038986187 = score(doc=4403,freq=10.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.40312994 = fieldWeight in 4403, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4403)
    0.010089659 = weight(_text_:information in 4403) [ClassicSimilarity], result of:
      0.010089659 = score(doc=4403,freq=8.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.19395474 = fieldWeight in 4403, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4403)
  0.21428572 = coord(3/14)

Abstract

The resource description framework (RDF) is a W3C recommendation for the formulation of meta-data on the World Wide Web. RDF Schema (RDFS) extends this standard with the means to specify domain vocabulary and object structures. These techniques will enable the enrichment of the Web with machine-processable semantics, thus giving rise to what has been dubbed the Semantic Web. We have developed Sesame, an architecture for storage and querying of RDF and RDFS information. Sesame allows persistent storage of RDF data and schema information, and provides access methods to that information through export and querying modules. It features ways of caching information and offers support for concurrency control. This chapter is organized as follows: In Section 5.2 we discuss why a query language specifically tailored to RDF and RDFS is needed, over and above existing query languages such as XQuery. In Section 5.3 we look at Sesame's modular architecture in some detail. In Section 5.4 we give an overview of the SAIL API and a brief comparison to other RDF API approaches. Section 5.5 discusses our experiences with Sesame to date, and Section 5.6 looks into possible future developments. Finally, we provide our conclusions in Section 5.7.

Source

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

Theme

Semantic Web

0.015855953 = product of:
  0.07399444 = sum of:
    0.020922182 = weight(_text_:web in 2369) [ClassicSimilarity], result of:
      0.020922182 = score(doc=2369,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 2369, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2369)
    0.01712272 = weight(_text_:information in 2369) [ClassicSimilarity], result of:
      0.01712272 = score(doc=2369,freq=16.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.3291521 = fieldWeight in 2369, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2369)
    0.03594954 = weight(_text_:retrieval in 2369) [ClassicSimilarity], result of:
      0.03594954 = score(doc=2369,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.40105087 = fieldWeight in 2369, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=2369)
  0.21428572 = coord(3/14)

Abstract

As information becomes richer and more complex, alternative information-organization methods are needed to more effectively and efficiently retrieve information from various systems, including the Web. The objective of this study is to explore how a Topic Maps-based ontology approach affects users' searching performance. Forty participants participated in a task-based evaluation where two dependent variables, recall and search time, were measured. The results of this study indicate that a Topic Maps-based ontology information retrieval (TOIR) system has a significant and positive effect on both recall and search time, compared to a thesaurus-based information retrieval (TIR) system. These results suggest that the inclusion of a Topic Maps-based ontology is a beneficial approach to take when designing information retrieval systems.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.12, S.1898-1911

0.015473626 = product of:
  0.07221025 = sum of:
    0.036903262 = weight(_text_:web in 4406) [ClassicSimilarity], result of:
      0.036903262 = score(doc=4406,freq=14.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.38159183 = fieldWeight in 4406, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.03125 = fieldNorm(doc=4406)
    0.014551513 = weight(_text_:information in 4406) [ClassicSimilarity], result of:
      0.014551513 = score(doc=4406,freq=26.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.2797255 = fieldWeight in 4406, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=4406)
    0.020755477 = weight(_text_:retrieval in 4406) [ClassicSimilarity], result of:
      0.020755477 = score(doc=4406,freq=6.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23154683 = fieldWeight in 4406, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=4406)
  0.21428572 = coord(3/14)

Abstract

Important information is often scattered across Web and/or intranet resources. Traditional search engines return ranked retrieval lists that offer little or no information on the semantic relationships among documents. Knowledge workers spend a substantial amount of their time browsing and reading to find out how documents are related to one another and where each falls into the overall structure of the problem domain. Yet only when knowledge workers begin to locate the similarities and differences among pieces of information do they move into an essential part of their work: building relationships to create new knowledge. Information retrieval traditionally focuses on the relationship between a given query (or user profile) and the information store. On the other hand, exploitation of interrelationships between selected pieces of information (which can be facilitated by the use of ontologies) can put otherwise isolated information into a meaningful context. The implicit structures so revealed help users use and manage information more efficiently. Knowledge management tools are needed that integrate the resources dispersed across Web resources into a coherent corpus of interrelated information. Previous research in information integration has largely focused on integrating heterogeneous databases and knowledge bases, which represent information in a highly structured way, often by means of formal languages. In contrast, the Web consists to a large extent of unstructured or semi-structured natural language texts. As we have seen, ontologies offer an alternative way to cope with heterogeneous representations of Web resources. The domain model implicit in an ontology can be taken as a unifying structure for giving information a common representation and semantics. Once such a unifying structure exists, it can be exploited to improve browsing and retrieval performance in information access tools. QuizRDF is an example of such a tool.

Source

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

Theme

Semantic Web

0.015341093 = product of:
  0.071591765 = sum of:
    0.029588435 = weight(_text_:web in 4708) [ClassicSimilarity], result of:
      0.029588435 = score(doc=4708,freq=4.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.3059541 = fieldWeight in 4708, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=4708)
    0.0060537956 = weight(_text_:information in 4708) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=4708,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 4708, 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=4708)
    0.03594954 = weight(_text_:retrieval in 4708) [ClassicSimilarity], result of:
      0.03594954 = score(doc=4708,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.40105087 = fieldWeight in 4708, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4708)
  0.21428572 = coord(3/14)

Abstract

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontologybased KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.

Source

The Semantic Web: research and applications ; second European Semantic WebConference, ESWC 2005, Heraklion, Crete, Greece, May 29 - June 1, 2005 ; proceedings. Eds.: A. Gómez-Pérez u. J. Euzenat

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.014021375 = product of:
  0.065433085 = sum of:
    0.020922182 = weight(_text_:web in 4318) [ClassicSimilarity], result of:
      0.020922182 = score(doc=4318,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 4318, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=4318)
    0.00856136 = weight(_text_:information in 4318) [ClassicSimilarity], result of:
      0.00856136 = score(doc=4318,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16457605 = fieldWeight in 4318, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4318)
    0.03594954 = weight(_text_:retrieval in 4318) [ClassicSimilarity], result of:
      0.03594954 = score(doc=4318,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.40105087 = fieldWeight in 4318, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4318)
  0.21428572 = coord(3/14)

Abstract

This paper presents a novel hybrid approach for visual information retrieval (VIR) that combines shape analysis of objects in image with their indexing by textual descriptions. The principal goal of presented technique is applying Two Segments Turning Function (2STF) proposed by authors for efficient invariant to spatial variations shape processing and implementation of semantic Web approaches for ontology-based user-oriented annotations of multimedia information. In the proposed approach the user's textual queries are converted to image features, which are used for images searching, indexing, interpretation, and retrieval. A decision about similarity between retrieved image and user's query is taken computing the shape convergence to 2STF combining it with matching the ontological annotations of objects in image and providing in this way automatic definition of the machine-understandable semantics. In order to evaluate the proposed approach the Image Retrieval by Ontological Description of Shapes system has been designed and tested using some standard image domains.

0.012830523 = product of:
  0.08981366 = sum of:
    0.03856498 = weight(_text_:wide in 4865) [ClassicSimilarity], result of:
      0.03856498 = score(doc=4865,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.29372054 = fieldWeight in 4865, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=4865)
    0.051248677 = weight(_text_:web in 4865) [ClassicSimilarity], result of:
      0.051248677 = score(doc=4865,freq=12.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.5299281 = fieldWeight in 4865, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=4865)
  0.14285715 = coord(2/14)

Abstract

Content Management Systeme (CMS) sind in vielen Organisationen bereits seit längerer Zeit fester Bestandteil zur Verwaltung und kollaborativen Bearbeitung von Text- und Multimedia-Inhalten. Im Zuge der rasch ansteigenden Fülle an Informationen und somit auch Wissen wird die Überschaubarkeit der Datenbestände jedoch massiv eingeschränkt. Diese und zusätzliche Anforderungen, wie automatisch Datenquellen aus dem World Wide Web (WWW) zu extrahieren, lassen traditionelle CMS immer mehr an ihre Grenzen stoßen. Dieser Beitrag diskutiert die neuen Herausforderungen an traditionelle CMS und bietet Lösungsvorschläge, wie CMS kombiniert mit semantischen Technologien diesen Herausforderungen begegnen können. Die Autoren stellen eine generische Systemarchitektur für Content Management Systeme vor, die einerseits Inhalte für das Semantic Web generieren, andererseits Content aus dem Web 2.0 syndizieren können und bei der Aufbereitung des Content den User mittels semantischer Technologien wie Reasoning oder Informationsextraktion unterstützen. Dabei wird auf Erfahrungen bei der prototypischen Implementierung von semantischer Technologie in ein bestehendes CMS System zurückgegriffen.

Object

Web 2.0

Source

Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini

0.012177391 = product of:
  0.056827825 = sum of:
    0.020922182 = weight(_text_:web in 3218) [ClassicSimilarity], result of:
      0.020922182 = score(doc=3218,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 3218, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=3218)
    0.0104854815 = weight(_text_:information in 3218) [ClassicSimilarity], result of:
      0.0104854815 = score(doc=3218,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.20156369 = fieldWeight in 3218, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=3218)
    0.025420163 = weight(_text_:retrieval in 3218) [ClassicSimilarity], result of:
      0.025420163 = score(doc=3218,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.2835858 = fieldWeight in 3218, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3218)
  0.21428572 = coord(3/14)

Abstract

Begriffsorientiertes Information Retrieval bedarf einer informationswissenschaftlichen Theorie der Begriffe sowie der semantischen Relationen. Ein Begriff wird durch seine Intension und Extension sowie durch Definitionen bestimmt. Dem Problem der Vagheit begegnen wir durch die Einführung von Prototypen. Wichtige Definitionsarten sind die Begriffserklärung (nach Aristoteles) und die Definition über Familienähnlichkeiten (im Sinne Wittgensteins). Wir modellieren Begriffe als Frames (in der Version von Barsalou). Die zentrale paradigmatische Relation in Wissensordnungen ist die Hierarchie, die in verschiedene Arten zu gliedern ist: Hyponymie zerfällt in die Taxonomie und die einfache Hyponymie, Meronymie in eine ganze Reihe unterschiedlicher Teil-Ganzes-Beziehungen. Wichtig für praktische Anwendungen ist die Transitivität der jeweiligen Relation. Eine unspezifische Assoziationsrelation ist bei den angepeilten Anwendungen wenig hilfreich und wird durch ein Bündel von generalisierbaren und fachspezifischen Relationen ersetzt. Unser Ansatz fundiert neue Optionen der Anwendung von Wissensordnungen in der Informationspraxis neben ihrem "klassischen" Einsatz beim Information Retrieval: Erweiterung von Suchanfragen (Anwendung der semantischen Nähe), automatisches Schlussfolgern (Anwendung der terminologischen Logik in Vorbereitung eines semantischen Web) und automatische Berechnungen (bei Funktionalbegriffen mit numerischen Wertangaben).

Source

Information - Wissenschaft und Praxis. 60(2009) H.8, S.403-420

0.012091472 = product of:
  0.056426868 = sum of:
    0.03019857 = weight(_text_:web in 3280) [ClassicSimilarity], result of:
      0.03019857 = score(doc=3280,freq=6.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.3122631 = fieldWeight in 3280, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3280)
    0.0050448296 = weight(_text_:information in 3280) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=3280,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 3280, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3280)
    0.021183468 = weight(_text_:retrieval in 3280) [ClassicSimilarity], result of:
      0.021183468 = score(doc=3280,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23632148 = fieldWeight in 3280, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3280)
  0.21428572 = coord(3/14)

Abstract

This publication shows how the gap between the HTML based internet and the RDF based vision of the semantic web might be bridged, by linking words in texts to concepts of ontologies. Most current search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. However, the indexes do not contain synonyms, cannot differentiate between homonyms ('mouse' as a pointing vs. 'mouse' as an animal) and users receive different search results when they use different conjugation forms of the same word. In this publication, we present a system that uses ontologies and Natural Language Processing techniques to index texts, and thus supports word sense disambiguation and the retrieval of texts that contain equivalent words, by indexing them to concepts of ontologies. For this purpose, we developed fully automated methods for mapping equivalent concepts of imported RDF ontologies (for this prototype WordNet, SUMO and OpenCyc). These methods will thus allow the seamless integration of domain specific ontologies for concept based information retrieval in different domains. To demonstrate the practical workability of this approach, a set of web pages that contain synonyms and homonyms were indexed and can be queried via a search engine like query frontend. However, the ontology based indexing approach can also be used for other data mining applications such text clustering, relation mining and for searching free text fields in biological databases. The ontology alignment methods and some of the text mining principles described in this publication are now incorporated into the ONDEX system http://ondex.sourceforge.net/.

0.011537242 = product of:
  0.08076069 = sum of:
    0.05979012 = weight(_text_:web in 504) [ClassicSimilarity], result of:
      0.05979012 = score(doc=504,freq=12.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.6182494 = fieldWeight in 504, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0546875 = fieldNorm(doc=504)
    0.020970564 = weight(_text_:retrieval in 504) [ClassicSimilarity], result of:
      0.020970564 = score(doc=504,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23394634 = fieldWeight in 504, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=504)
  0.14285715 = coord(2/14)

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.

Footnote

Simultaneously published as Knitting the Semantic Web

Theme

Semantic Web

0.01148705 = product of:
  0.08040935 = sum of:
    0.03856498 = weight(_text_:wide in 229) [ClassicSimilarity], result of:
      0.03856498 = score(doc=229,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.29372054 = fieldWeight in 229, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=229)
    0.041844364 = weight(_text_:web in 229) [ClassicSimilarity], result of:
      0.041844364 = score(doc=229,freq=8.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.43268442 = fieldWeight in 229, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=229)
  0.14285715 = coord(2/14)

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

Theme

Semantic Web

0.0112928 = product of:
  0.052699734 = sum of:
    0.013536699 = weight(_text_:information in 3261) [ClassicSimilarity], result of:
      0.013536699 = score(doc=3261,freq=10.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.031133216 = weight(_text_:retrieval in 3261) [ClassicSimilarity], result of:
      0.031133216 = score(doc=3261,freq=6.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.34732026 = fieldWeight in 3261, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3261)
    0.008029819 = product of:
      0.024089456 = sum of:
        0.024089456 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
          0.024089456 = score(doc=3261,freq=2.0), product of:
            0.103770934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029633347 = 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)
      0.33333334 = coord(1/3)
  0.21428572 = coord(3/14)

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

0.010692102 = product of:
  0.07484471 = sum of:
    0.032137483 = weight(_text_:wide in 3244) [ClassicSimilarity], result of:
      0.032137483 = score(doc=3244,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.24476713 = fieldWeight in 3244, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3244)
    0.042707227 = weight(_text_:web in 3244) [ClassicSimilarity], result of:
      0.042707227 = score(doc=3244,freq=12.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.4416067 = fieldWeight in 3244, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3244)
  0.14285715 = coord(2/14)

Abstract

This chapter discusses OIL and DAML1OIL, currently the most prominent ontology languages for the Semantic Web. The chapter starts by discussing the pyramid of languages that underlie the architecture of the Semantic Web (XML, RDF, RDFS). In section 2.2, we briefly describe XML, RDF and RDFS. We then discuss in more detail OIL and DAML1OIL, the first proposals for languages at the ontology layer of the semantic pyramid. For OIL (and to some extent DAML1OIL) we discuss the general design motivations (Section 2.3), describe the constructions in the language (Section 2.4), and the various syntactic forms of these languages (Section 2.5). Section 2.6 discusses the layered architecture of the language, section 2.7 briefly mentions the formal semantics, section 2.8 discusses the transition from OIL to DAML+OIL, and section 2.9 concludes with our experience with the language to date and future development in the context of the World Wide Web Consortium (W3C). This chapter is not intended to give full and formal definitions of either the syntax or the semantics of OIL or DAML1OIL. Such definitions are already available elsewhere: http://www.ontoknowledge.org/oil/ for OIL and http://www.w3.org/submission/2001/12/ for DAML1OIL.

Source

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

Theme

Semantic Web

0.010686181 = product of:
  0.07480326 = sum of:
    0.03856498 = weight(_text_:wide in 3404) [ClassicSimilarity], result of:
      0.03856498 = score(doc=3404,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.29372054 = fieldWeight in 3404, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=3404)
    0.036238287 = weight(_text_:web in 3404) [ClassicSimilarity], result of:
      0.036238287 = score(doc=3404,freq=6.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.37471575 = fieldWeight in 3404, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=3404)
  0.14285715 = coord(2/14)

Source

Web semantics: science, services and agents on the World Wide Web. 6(2008) no.3, S.203-217

Theme

Semantic Web

0.010169638 = product of:
  0.047458313 = sum of:
    0.020922182 = weight(_text_:web in 1597) [ClassicSimilarity], result of:
      0.020922182 = score(doc=1597,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 1597, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=1597)
    0.00856136 = weight(_text_:information in 1597) [ClassicSimilarity], result of:
      0.00856136 = score(doc=1597,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16457605 = fieldWeight in 1597, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1597)
    0.01797477 = weight(_text_:retrieval in 1597) [ClassicSimilarity], result of:
      0.01797477 = score(doc=1597,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.20052543 = fieldWeight in 1597, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1597)
  0.21428572 = coord(3/14)

Abstract

Die populären Web 2.0-Dienste werden von Prosumern - Produzenten und gleichsam Konsumenten - nicht nur dazu genutzt, Inhalte zu produzieren, sondern auch, um sie inhaltlich zu erschließen. Folksonomies erlauben es dem Nutzer, Dokumente mit eigenen Schlagworten, sog. Tags, zu beschreiben, ohne dabei auf gewisse Regeln oder Vorgaben achten zu müssen. Neben einigen Vorteilen zeigen Folksonomies aber auch zahlreiche Schwächen (u. a. einen Mangel an Präzision). Um diesen Nachteilen größtenteils entgegenzuwirken, schlagen wir eine Interpretation der Tags als natürlichsprachige Wörter vor. Dadurch ist es uns möglich, Methoden des Natural Language Processing (NLP) auf die Tags anzuwenden und so linguistische Probleme der Tags zu beseitigen. Darüber hinaus diskutieren wir Ansätze und weitere Vorschläge (Tagverteilungen, Kollaboration und akteurspezifische Aspekte) hinsichtlich eines Relevance Rankings von getaggten Dokumenten. Neben Vorschlägen auf ähnliche Dokumente ("more like this!") erlauben Folksonomies auch Hinweise auf verwandte Nutzer und damit auf Communities ("more like me!").

Source

Information - Wissenschaft und Praxis. 59(2008) H.2, S.77-90

0.010160525 = product of:
  0.071123675 = sum of:
    0.032137483 = weight(_text_:wide in 3084) [ClassicSimilarity], result of:
      0.032137483 = score(doc=3084,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.24476713 = fieldWeight in 3084, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3084)
    0.038986187 = weight(_text_:web in 3084) [ClassicSimilarity], result of:
      0.038986187 = score(doc=3084,freq=10.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.40312994 = fieldWeight in 3084, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3084)
  0.14285715 = coord(2/14)

Abstract

Ontologies have moved beyond the domains of library science, philosophy, and knowledge representation. They are now the concerns of marketing departments, CEOs, and mainstream business. Research analyst companies such as Forrester Research report on the critical roles of ontologies in support of browsing and search for e-commerce and in support of interoperability for facilitation of knowledge management and configuration. One now sees ontologies used as central controlled vocabularies that are integrated into catalogues, databases, web publications, knowledge management applications, etc. Large ontologies are essential components in many online applications including search (such as Yahoo and Lycos), e-commerce (such as Amazon and eBay), configuration (such as Dell and PC-Order), etc. One also sees ontologies that have long life spans, sometimes in multiple projects (such as UMLS, SIC codes, etc.). Such diverse usage generates many implications for ontology environments. In this paper, we will discuss ontologies and requirements in their current instantiations on the web today. We will describe some desirable properties of ontologies. We will also discuss how both simple and complex ontologies are being and may be used to support varied applications. We will conclude with a discussion of emerging trends in ontologies and their environments and briefly mention our evolving ontology evolution environment.

Source

Spinning the Semantic Web: bringing the World Wide Web to its full potential. Eds.: D. Fensel u.a

Theme

Semantic Web