Search (102 results, page 1 of 6)

0.029893843 = product of:
  0.059787687 = sum of:
    0.051374428 = weight(_text_:digitale in 3377) [ClassicSimilarity], result of:
      0.051374428 = score(doc=3377,freq=2.0), product of:
        0.18027179 = queryWeight, product of:
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.034944877 = queryNorm
        0.2849832 = fieldWeight in 3377, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3377)
    0.008413259 = weight(_text_:information in 3377) [ClassicSimilarity], result of:
      0.008413259 = score(doc=3377,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.13714671 = fieldWeight in 3377, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3377)
  0.5 = coord(2/4)

Abstract

The structural and syntactic web put in place in the early 90s is still much the same as what we use today: resources (web pages, files, etc.) connected by untyped hyperlinks. By untyped, we mean that there is no easy way for a computer to figure out what a link between two pages means - for example, on the W3C website, there are hundreds of links to the various organisations that are registered members of the association, but there is nothing explicitly saying that the link is to an organisation that is a "member of" the W3C or what type of organisation is represented by the link. On John's work page, he links to many papers he has written, but it does not explicitly say that he is the author of those papers or that he wrote such-and-such when he was working at a particular university. In fact, the Web was envisaged to be much more, as one can see from the image in Fig. 1 which is taken from Tim Berners Lee's original outline for the Web in 1989, entitled "Information Management: A Proposal". In this, all the resources are connected by links describing the type of relationships, e.g. "wrote", "describe", "refers to", etc. This is a precursor to the Semantic Web which we will come back to later.

Content

Vgl. die digitale Ausgabe unter: http://www.springerlink.com/content/l782q08436312x04/.

0.020549772 = product of:
  0.08219909 = sum of:
    0.08219909 = weight(_text_:digitale in 3376) [ClassicSimilarity], result of:
      0.08219909 = score(doc=3376,freq=2.0), product of:
        0.18027179 = queryWeight, product of:
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.034944877 = queryNorm
        0.45597312 = fieldWeight in 3376, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.0625 = fieldNorm(doc=3376)
  0.25 = coord(1/4)

Content

Vgl. die digitale Ausgabe unter: http://www.springerlink.com/content/g795215740578203/.

0.004655886 = product of:
  0.018623544 = sum of:
    0.018623544 = weight(_text_:information in 4329) [ClassicSimilarity], result of:
      0.018623544 = score(doc=4329,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = 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)
  0.25 = coord(1/4)

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

0.004289937 = product of:
  0.017159749 = sum of:
    0.017159749 = weight(_text_:information in 4406) [ClassicSimilarity], result of:
      0.017159749 = score(doc=4406,freq=26.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = 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)
  0.25 = coord(1/4)

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.

0.0042066295 = product of:
  0.016826518 = sum of:
    0.016826518 = weight(_text_:information in 6061) [ClassicSimilarity], result of:
      0.016826518 = score(doc=6061,freq=16.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.27429342 = fieldWeight in 6061, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6061)
  0.25 = coord(1/4)

Abstract

The middle of the 1990s are coined by the increased enthusiasm for the possibilities of the WWW, which has only recently deviated - at least in relation to scientific information - for the differentiated measuring of its advantages and disadvantages. Web Information Retrieval originated as a specialized discipline with great commercial significance (for an overview see Lewandowski 2005). Besides the new technological structure that enables the indexing and searching (in seconds) of unimaginable amounts of data worldwide, new assessment processes for the ranking of search results are being developed, which use the link structures of the Web. They are the main innovation with respect to the traditional "mother discipline" of Information Retrieval. From the beginning, link structures of Web pages are applied to commercial search engines in a wide array of variations. From the perspective of scientific information, link topology based approaches were in essence trying to solve a self-created problem: on the one hand, it quickly became clear that the openness of the Web led to an up-tonow unknown increase in available information, but this also caused the quality of the Web pages searched to become a problem - and with it the relevance of the results. The gatekeeper function of traditional information providers, which narrows down every user query to focus on high-quality sources was lacking. Therefore, the recognition of the "authoritativeness" of the Web pages by general search engines such as Google was one of the most important factors for their success.

Source

Information und Sprache: Beiträge zu Informationswissenschaft, Computerlinguistik, Bibliothekswesen und verwandten Fächern. Festschrift für Harald H. Zimmermann. Herausgegeben von Ilse Harms, Heinz-Dirk Luckhardt und Hans W. Giessen

0.0042066295 = product of:
  0.016826518 = sum of:
    0.016826518 = weight(_text_:information in 2789) [ClassicSimilarity], result of:
      0.016826518 = score(doc=2789,freq=16.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.27429342 = fieldWeight in 2789, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2789)
  0.25 = coord(1/4)

Classification

ST 515 Informatik / Monographien / Einzelne Anwendungen der Datenverarbeitung / Wirtschaftsinformatik / Wissensmanagement, Information engineering

LCSH

Ontologies (Information retrieval)

RSWK

Semantic Web / Ontologie <Wissensverarbeitung> / Information Retrieval / Verteilung / Metadaten / Datenintegration

RVK

ST 515 Informatik / Monographien / Einzelne Anwendungen der Datenverarbeitung / Wirtschaftsinformatik / Wissensmanagement, Information engineering

Series

Advanced information and knowledge processing

Subject

Semantic Web / Ontologie <Wissensverarbeitung> / Information Retrieval / Verteilung / Metadaten / Datenintegration
Ontologies (Information retrieval)

0.004164351 = product of:
  0.016657405 = sum of:
    0.016657405 = weight(_text_:information in 4152) [ClassicSimilarity], result of:
      0.016657405 = score(doc=4152,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.27153665 = fieldWeight in 4152, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=4152)
  0.25 = coord(1/4)

Source

Journal of information science. 27(2001) no.?, S.377-384

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 4332) [ClassicSimilarity], result of:
      0.015963038 = score(doc=4332,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 4332, 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=4332)
  0.25 = coord(1/4)

Abstract

Das Semantic Web bezeichnet ein erweitertes World Wide Web (WWW), das die Bedeutung von präsentierten Inhalten in neuen standardisierten Sprachen wie RDF Schema und OWL modelliert. Diese Arbeit befasst sich mit dem Aspekt des Information Retrieval, d.h. es wird untersucht, in wie weit Methoden der Informationssuche sich auf modelliertes Wissen übertragen lassen. Die kennzeichnenden Merkmale von IR-Systemen wie vage Anfragen sowie die Unterstützung unsicheren Wissens werden im Kontext des Semantic Web behandelt. Im Fokus steht die Suche nach Fakten innerhalb einer Wissensdomäne, die entweder explizit modelliert sind oder implizit durch die Anwendung von Inferenz abgeleitet werden können. Aufbauend auf der an der Universität Duisburg-Essen entwickelten Retrievalmaschine PIRE wird die Anwendung unsicherer Inferenz mit probabilistischer Prädikatenlogik (pDatalog) implementiert.

Footnote

Zugl.: Dortmund, Univ., Dipl.-Arb., 2006 u.d.T.: Hüsken, Peter: Information-Retrieval im Semantic-Web.

RSWK

Information Retrieval / Semantic Web

Subject

Information Retrieval / Semantic Web

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 439) [ClassicSimilarity], result of:
      0.015963038 = score(doc=439,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 439, 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=439)
  0.25 = coord(1/4)

Abstract

This book constitutes the refereed proceedings of the Second European Semantic Web Conference, ESWC 2005, heldin Heraklion, Crete, Greece in May/June 2005. The 48 revised full papers presented were carefully reviewed and selected from 148 submissions. The papers are organized in topical sections on semantic Web services, languages, ontologies, reasoning and querying, search and information retrieval, user and communities, natural language for the semantic Web, annotation tools, and semantic Web applications.

LCSH

Information storage and retrieval systems
Information systems

Subject

Information storage and retrieval systems
Information systems

0.0039349417 = product of:
  0.015739767 = sum of:
    0.015739767 = weight(_text_:information in 231) [ClassicSimilarity], result of:
      0.015739767 = score(doc=231,freq=14.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = 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)
  0.25 = coord(1/4)

Abstract

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

0.003762524 = product of:
  0.015050096 = sum of:
    0.015050096 = weight(_text_:information in 4404) [ClassicSimilarity], result of:
      0.015050096 = score(doc=4404,freq=20.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = 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)
  0.25 = coord(1/4)

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.

0.003606434 = product of:
  0.014425736 = sum of:
    0.014425736 = weight(_text_:information in 696) [ClassicSimilarity], result of:
      0.014425736 = score(doc=696,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23515764 = fieldWeight in 696, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=696)
  0.25 = coord(1/4)

Abstract

We describe an apporach to retrieval of documents that consist of both free text and semantically enriched markup. In particular, we present the design and implementation prototype of a framework in which both documents and queries can be marked up with statements in the DAML+OIL semantic web language. These statement provide both structured and semi-structured information about the documents and their content. We claim that indexing text and semantic markup will significantly improve retrieval performance. Outr approach allows inferencing to be done over this information at several points: when a document is indexed,when a query is processed and when query results are evaluated.

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 3295) [ClassicSimilarity], result of:
      0.014277775 = score(doc=3295,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 3295, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=3295)
  0.25 = coord(1/4)

Abstract

Ein Schwerpunkt der Online Information 2004 bildete das Thema "Search": Wie wird die Suche in 2005 aussehen? Welche Bedeutung haben Taxonomien? Wie verändern sich Suchfunktionen?

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 3358) [ClassicSimilarity], result of:
      0.014277775 = score(doc=3358,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 3358, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=3358)
  0.25 = coord(1/4)

Abstract

The coming Internet revolution will profoundly affect scientific information

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 4322) [ClassicSimilarity], result of:
      0.014277775 = score(doc=4322,freq=8.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 4322, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4322)
  0.25 = coord(1/4)

Abstract

Dieses Whitepaper befasst sich mit der Integration semantischer Technologien in bestehende Ansätze des Information Retrieval und die damit verbundenen weitreichenden Auswirkungen auf Effizienz und Effektivität von Suche und Navigation in Dokumenten. Nach einer Einbettung in die Problematik des Wissensmanagement aus Sicht der Informationstechnik folgt ein Überblick zu den Methoden des Information Retrieval. Anschließend werden die semantischen Technologien "Wissen modellieren - Ontologie" und "Neues Wissen ableiten - Inferenz" vorgestellt. Ein Integrationsansatz wird im Folgenden diskutiert und die entstehenden Mehrwerte präsentiert. Insbesondere ergeben sich Erweiterungen hinsichtlich einer verfeinerten Suchunterstützung und einer kontextbezogenen Navigation sowie die Möglichkeiten der Auswertung von regelbasierten Zusammenhängen und einfache Integration von strukturierten Informationsquellen. Das Whitepaper schließt mit einem Ausblick auf die zukünftige Entwicklung des WWW hin zu einem Semantic Web und die damit verbundenen Implikationen für semantische Technologien.

Content

Inhalt: 1. Einführung - 2. Wissensmanagement - 3. Information Retrieval - 3.1. Methoden und Techniken - 3.2. Information Retrieval in der Anwendung - 4. Semantische Ansätze - 4.1. Wissen modellieren - Ontologie - 4.2. Neues Wissen inferieren - 5. Knowledge Retrieval in der Anwendung - 6. Zukunftsaussichten - 7. Fazit

0.0035694435 = product of:
  0.014277774 = sum of:
    0.014277774 = weight(_text_:information in 701) [ClassicSimilarity], result of:
      0.014277774 = score(doc=701,freq=18.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274568 = fieldWeight in 701, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
  0.25 = coord(1/4)

Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

0.0033653039 = product of:
  0.013461215 = sum of:
    0.013461215 = weight(_text_:information in 245) [ClassicSimilarity], result of:
      0.013461215 = score(doc=245,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.21943474 = fieldWeight in 245, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=245)
  0.25 = coord(1/4)

Abstract

PhysNet (www.physnet.net) is a portal for Physics run since 1995 and continuously being developed; it today uses an OWLLite ontology and mySQL database for storing triples with the facts, such as department information, postal addresses, GPS coordinates, URLs of publication repositories, etc. The article focuses on the structure and the development of the underlying ontology; it also gives a detailed overview of an online web-based editorial tool, to maintain the facts database.

Theme

Information Gateway

0.0033256328 = product of:
  0.013302531 = sum of:
    0.013302531 = weight(_text_:information in 246) [ClassicSimilarity], result of:
      0.013302531 = score(doc=246,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.21684799 = fieldWeight in 246, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=246)
  0.25 = coord(1/4)

Abstract

Semantic Web tools provide new and significant opportunities for organizing and improving the utility of biomedical information. As librarians become more involved with biomedical information, it is important for them, particularly catalogers, to be part of research teams that are employing these techniques and developing a high level interoperable biomedical infrastructure. To illustrate these principles, we used Semantic Web tools to create a knowledge model for human visual phenotypes (observable characteristics). This is an important foundation for generating associations between genomics and clinical medicine. In turn this can allow customized medical therapies and provide insights into the molecular basis of disease. The knowledge model incorporates a wide variety of clinical and genomic data including examination findings, demographics, laboratory tests, imaging and variations in DNA sequence. Information organization, storage and retrieval are facilitated through the use of metadata and the ability to make computable statements in the visual science domain. This paper presents our work, discusses the value of Semantic Web technologies in biomedicine, and identifies several important roles that library and information scientists can play in developing a more powerful biomedical information infrastructure.

0.0033256328 = product of:
  0.013302531 = sum of:
    0.013302531 = weight(_text_:information in 1952) [ClassicSimilarity], result of:
      0.013302531 = score(doc=1952,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.21684799 = fieldWeight in 1952, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1952)
  0.25 = coord(1/4)

LCSH

Knowledge representation (Information theory)
Conceptual structures (Information theory)

Series

International handbook on information systems

Subject

Knowledge representation (Information theory)
Conceptual structures (Information theory)

0.0031232631 = product of:
  0.0124930525 = sum of:
    0.0124930525 = weight(_text_:information in 4409) [ClassicSimilarity], result of:
      0.0124930525 = score(doc=4409,freq=18.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.20365247 = fieldWeight in 4409, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4409)
  0.25 = coord(1/4)

Abstract

We saw in the introduction how the Semantic Web makes possible a new generation of knowledge management tools. We now turn our attention more specifically to Semantic Web based support for virtual communities of practice. The notion of communities of practice has attracted much attention in the field of knowledge management. Communities of practice are groups within (or sometimes across) organizations who share a common set of information needs or problems. They are typically not a formal organizational unit but an informal network, each sharing in part a common agenda and shared interests or issues. In one example it was found that a lot of knowledge sharing among copier engineers took place through informal exchanges, often around a water cooler. As well as local, geographically based communities, trends towards flexible working and globalisation have led to interest in supporting dispersed communities using Internet technology. The challenge for organizations is to support such communities and make them effective. Provided with an ontology meeting the needs of a particular community of practice, knowledge management tools can arrange knowledge assets into the predefined conceptual classes of the ontology, allowing more natural and intuitive access to knowledge. Knowledge management tools must give users the ability to organize information into a controllable asset. Building an intranet-based store of information is not sufficient for knowledge management; the relationships within the stored information are vital. These relationships cover such diverse issues as relative importance, context, sequence, significance, causality and association. The potential for knowledge management tools is vast; not only can they make better use of the raw information already available, but they can sift, abstract and help to share new information, and present it to users in new and compelling ways.
In this chapter, we describe the OntoShare system which facilitates and encourages the sharing of information between communities of practice within (or perhaps across) organizations and which encourages people - who may not previously have known of each other's existence in a large organization - to make contact where there are mutual concerns or interests. As users contribute information to the community, a knowledge resource annotated with meta-data is created. Ontologies defined using the resource description framework (RDF) and RDF Schema (RDFS) are used in this process. RDF is a W3C recommendation for the formulation of meta-data for WWW resources. RDF(S) extends this standard with the means to specify domain vocabulary and object structures - that is, concepts and the relationships that hold between them. In the next section, we describe in detail the way in which OntoShare can be used to share and retrieve knowledge and how that knowledge is represented in an RDF-based ontology. We then proceed to discuss in Section 10.3 how the ontologies in OntoShare evolve over time based on user interaction with the system and motivate our approach to user-based creation of RDF-annotated information resources. The way in which OntoShare can help to locate expertise within an organization is then described, followed by a discussion of the sociotechnical issues of deploying such a tool. Finally, a planned evaluation exercise and avenues for further research are outlined.