Search (1449 results, page 2 of 73)

  • × year_i:[2000 TO 2010}
  1. Schmidt, I.; Müller, C.: Zaubernetz : Inhaltsstrukturen und Topic Maps als Potenzial neuer Informationstechnik (2000) 0.06 
    0.0625095 = product of:
  0.125019 = sum of:
    0.125019 = product of:
      0.250038 = sum of:
        0.250038 = weight(_text_:maps in 5477) [ClassicSimilarity], result of:
          0.250038 = score(doc=5477,freq=4.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.87800705 = fieldWeight in 5477, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.078125 = fieldNorm(doc=5477)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Object
    Topic maps
  2. Godby, C.J.; Reighart, R.R.; Miller, E.J.: Automatically Generated Topic Maps of World Wide Web Resources (2001) 0.06 
    0.0625095 = product of:
  0.125019 = sum of:
    0.125019 = product of:
      0.250038 = sum of:
        0.250038 = weight(_text_:maps in 1065) [ClassicSimilarity], result of:
          0.250038 = score(doc=1065,freq=4.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.87800705 = fieldWeight in 1065, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.078125 = fieldNorm(doc=1065)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Object
    Topic maps
  3. Pepper, S.: Topic maps (2009) 0.06 
    0.06188125 = product of:
  0.1237625 = sum of:
    0.1237625 = product of:
      0.247525 = sum of:
        0.247525 = weight(_text_:maps in 3149) [ClassicSimilarity], result of:
          0.247525 = score(doc=3149,freq=8.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.86918265 = fieldWeight in 3149, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3149)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Topic Maps is an international standard technology for describing knowledge structures and using them to improve the findability of information. It is based on a formal model that subsumes those of traditional finding aids such as indexes, glossaries, and thesauri, and extends them to cater for the additional complexities of digital information. Topic Maps is increasingly used in enterprise information integration, knowledge management, e-learning, and digital libraries, and as the foundation for Web-based information delivery solutions. This entry provides a comprehensive treatment of the core concepts, as well as describing the background and current status of the standard and its relationship to traditional knowledge organization techniques.
    Object
    Topic maps
  4. Will, L.D.: Publications on thesaurus construction and use : including some references to facet analysis, taxonomies, ontologies, topic maps and related issues (2005) 0.06 
    0.06188125 = product of:
  0.1237625 = sum of:
    0.1237625 = product of:
      0.247525 = sum of:
        0.247525 = weight(_text_:maps in 3192) [ClassicSimilarity], result of:
          0.247525 = score(doc=3192,freq=2.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.86918265 = fieldWeight in 3192, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.109375 = fieldNorm(doc=3192)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
  5. Weidemann, T.: Einmal um die ganze Welt (2007) 0.06 
    0.06188125 = product of:
  0.1237625 = sum of:
    0.1237625 = product of:
      0.247525 = sum of:
        0.247525 = weight(_text_:maps in 1421) [ClassicSimilarity], result of:
          0.247525 = score(doc=1421,freq=8.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.86918265 = fieldWeight in 1421, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1421)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Google Maps und Google Earth bringen die Welt zu Ihnen nach Hause. Sie können damit Kneipen suchen, Reisen planen oder einfach träumen. So nutzen Sie die Dienste optimal. Die ganze Welt auf Ihrem PC. Eine leistungsfähige Software, die Bilder der Erde aus allen Blickwinkeln und mit allen möglichen Zusatzinfos zeigt- und das für Privatanwender gratis: Google hat mit Google Earth und Google Maps das Internet wieder ein Stück mehr geprägt. Das Unternehmen trägt Kartendaten und Satellitenbilder aus der ganzen Welt zusammen und verbessert laufend die Genauigkeit. Eine engagierte Community programmiert dazu eine Vielzahl von Plug-ins und anderen Erweiterungen, die das Angebot noch vielfältiger und spannender machen. Wir zeigen Ihnen, was Sie mit dem Internet-Dienst Google Maps (http://maps.gooole.de) und der Software Google Earth (http://earth.googl.de) alles anfangen können.
    Object
    Google Maps
  6. Rapp, B.A.; Wheeler, D.L.: Bioinformatics resources from the National Center for Biotechnology Information : an integrated foundation for discovery (2005) 0.06 
    0.061366733 = product of:
  0.12273347 = sum of:
    0.12273347 = sum of:
      0.08840178 = weight(_text_:maps in 5265) [ClassicSimilarity], result of:
        0.08840178 = score(doc=5265,freq=2.0), product of:
          0.28477904 = queryWeight, product of:
            5.619245 = idf(docFreq=435, maxDocs=44218)
            0.050679237 = queryNorm
          0.31042236 = fieldWeight in 5265, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.619245 = idf(docFreq=435, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5265)
      0.034331687 = weight(_text_:22 in 5265) [ClassicSimilarity], result of:
        0.034331687 = score(doc=5265,freq=2.0), product of:
          0.17747006 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050679237 = queryNorm
          0.19345059 = fieldWeight in 5265, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5265)
  0.5 = coord(1/2)
    Abstract
    The National Center for Biotechnology Information (NCBI) provides access to more than 30 publicly available molecular biology resources, offering an effective discovery space through high levels of data integration among large-scale data repositories. The foundation for many services is GenBank®, a public repository of DNA sequences from more than 133,000 different organisms. GenBank is accessible through the Entrez retrieval system, which integrates data from the major DNA and protein sequence databases, along with resources for taxonomy, genome maps, sequence variation, gene expression, gene function and phenotypes, protein structure and domain information, and the biomedical literature via PubMed®. Computational tools allow scientists to analyze vast quantities of diverse data. The BLAST® sequence similarity programs are instrumental in identifying genes and genetic features. Other tools support mapping disease loci to the genome, identifying new genes, comparing genomes, and relating sequence data to model protein structures. A basic research program in computational molecular biology enhances the database and software tool development initiatives. Future plans include further data integration, enhanced genome annotation and protein classification, additional data types, and links to a wider range of resources.
    Date
    22. 7.2006 14:58:34
  7. Meho, L.I.; Rogers, Y.: Citation counting, citation ranking, and h-index of human-computer interaction researchers : a comparison of Scopus and Web of Science (2008) 0.06 
    0.061366733 = product of:
  0.12273347 = sum of:
    0.12273347 = sum of:
      0.08840178 = weight(_text_:maps in 2352) [ClassicSimilarity], result of:
        0.08840178 = score(doc=2352,freq=2.0), product of:
          0.28477904 = queryWeight, product of:
            5.619245 = idf(docFreq=435, maxDocs=44218)
            0.050679237 = queryNorm
          0.31042236 = fieldWeight in 2352, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.619245 = idf(docFreq=435, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2352)
      0.034331687 = weight(_text_:22 in 2352) [ClassicSimilarity], result of:
        0.034331687 = score(doc=2352,freq=2.0), product of:
          0.17747006 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050679237 = queryNorm
          0.19345059 = fieldWeight in 2352, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2352)
  0.5 = coord(1/2)
    Abstract
    This study examines the differences between Scopus and Web of Science in the citation counting, citation ranking, and h-index of 22 top human-computer interaction (HCI) researchers from EQUATOR - a large British Interdisciplinary Research Collaboration project. Results indicate that Scopus provides significantly more coverage of HCI literature than Web of Science, primarily due to coverage of relevant ACM and IEEE peer-reviewed conference proceedings. No significant differences exist between the two databases if citations in journals only are compared. Although broader coverage of the literature does not significantly alter the relative citation ranking of individual researchers, Scopus helps distinguish between the researchers in a more nuanced fashion than Web of Science in both citation counting and h-index. Scopus also generates significantly different maps of citation networks of individual scholars than those generated by Web of Science. The study also presents a comparison of h-index scores based on Google Scholar with those based on the union of Scopus and Web of Science. The study concludes that Scopus can be used as a sole data source for citation-based research and evaluation in HCI, especially when citations in conference proceedings are sought, and that researchers should manually calculate h scores instead of relying on system calculations.
  8. Smolnik, S.; Nastansky, L.: K-Discovery : Identifikation von verteilten Wissensstrukturen in einer prozessorientierten Groupware-Umgebung (2004) 0.06 
    0.05930172 = product of:
  0.11860344 = sum of:
    0.11860344 = product of:
      0.23720688 = sum of:
        0.23720688 = weight(_text_:maps in 620) [ClassicSimilarity], result of:
          0.23720688 = score(doc=620,freq=10.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.8329506 = fieldWeight in 620, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=620)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Verschiedene Szenarien in Groupware-basierten Umgebungen verdeutlichen die Probleme, Wissensstrukturen im Allgemeinen und organisationale Wissensstrukturen im Speziellen zu identifizieren. Durch den Einsatz von Topic Maps, definiert im ISOStandard "ISO/IEC 13250 Topic Maps", in Groupware-basierten organisationalen Wissensbasen wird es möglich, die Lücke zwischen Wissen und Information zu schließen. In diesem Beitrag werden die Ziele des Forschungsprojektes K-Discovery - der Einsatz von Topic Maps in Groupware-basierten Umgebungen - vorgestellt. Aufbauend auf diesen Zielen wird ein Architekturmodell sowie zwei Implementationsansätze präsentiert, in dem durch den Einsatz von Topic Maps in einer prozessorientierten GroupwareUmgebung Wissensstrukturen generiert werden. Der Beitrag schließt mit einigen abschließenden Ausführungen.
    Object
    Topic maps
  9. Widhalm, R.; Mueck, T.A.: Merging topics in well-formed XML topic maps (2003) 0.06 
    0.05930172 = product of:
  0.11860344 = sum of:
    0.11860344 = product of:
      0.23720688 = sum of:
        0.23720688 = weight(_text_:maps in 2186) [ClassicSimilarity], result of:
          0.23720688 = score(doc=2186,freq=10.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.8329506 = fieldWeight in 2186, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=2186)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Topic Maps are a standardized modelling approach for the semantic annotation and description of WWW resources. They enable an improved search and navigational access on information objects stored in semi-structured information spaces like the WWW. However, the according standards ISO 13250 and XTM (XML Topic Maps) lack formal semantics, several questions concerning e.g. subclassing, inheritance or merging of topics are left open. The proposed TMUML meta model, directly derived from the well known UML meta model, is a meta model for Topic Maps which enables semantic constraints to be formulated in OCL (object constraint language) in order to answer such open questions and overcome possible inconsistencies in Topic Map repositories. We will examine the XTM merging conditions and show, in several examples, how the TMUML meta model enables semantic constraints for Topic Map merging to be formulated in OCL. Finally, we will show how the TM validation process, i.e., checking if a Topic Map is well formed, includes our merging conditions.
    Object
    Topic maps
  10. Yi, M.: Information organization and retrieval using a topic maps-based ontology : results of a task-based evaluation (2008) 0.06 
    0.05930172 = product of:
  0.11860344 = sum of:
    0.11860344 = product of:
      0.23720688 = sum of:
        0.23720688 = weight(_text_:maps in 2369) [ClassicSimilarity], result of:
          0.23720688 = score(doc=2369,freq=10.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.8329506 = fieldWeight in 2369, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=2369)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    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.
    Object
    Topic maps
  11. Siebers, Q.H.J.F.: Implementing inference rules in the Topic maps model (2006) 0.06 
    0.05930172 = product of:
  0.11860344 = sum of:
    0.11860344 = product of:
      0.23720688 = sum of:
        0.23720688 = weight(_text_:maps in 4730) [ClassicSimilarity], result of:
          0.23720688 = score(doc=4730,freq=10.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.8329506 = fieldWeight in 4730, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=4730)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    This paper supplies a theoretical approach on implementing inference rules in the Topic Maps model. Topic Maps is an ISO standard that allows for the modeling and representation of knowledge in an interchangeable form, that can be extended by inference rules. These rules specify conditions for inferrable facts. Any implementation requires a syntax for storage in a file, a storage model and method for processing and a system to keep track of changes in the inferred facts. The most flexible and optimisable storage model is a controlled cache, giving options for processing. Keeping track of changes is done by listeners. One of the most powerful applications of inference rules in Topic Maps is interoperability. By mapping ontologies to each other using inference rules as converter, it is possible to exchange extendable knowledge. Any implementation must choose methods and options optimized for the system it runs on, with the facilities available. Further research is required to analyze optimization problems between options.
    Object
    Topic maps
  12. Sigel, A.: Wissensorganisation, Topic Maps und Ontology Engineering : Die Verbindung bewährter Begriffsstrukturen mit aktueller XML Technologie (2004) 0.06 
    0.058472283 = product of:
  0.11694457 = sum of:
    0.11694457 = product of:
      0.23388913 = sum of:
        0.23388913 = weight(_text_:maps in 3236) [ClassicSimilarity], result of:
          0.23388913 = score(doc=3236,freq=14.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.8213004 = fieldWeight in 3236, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3236)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Wie können begriffliche Strukturen an Topic Maps angebunden werden? Allgemeiner. Wie kann die Wissensorganisation dazu beitragen, dass im Semantic Web eine begriffsbasierte Infrastruktur verfügbar ist? Dieser Frage hat sich die Wissensorganisation bislang noch nicht wirklich angenommen. Insgesamt ist die Berührung zwischen semantischen Wissenstechnologien und wissensorganisatorischen Fragestellungen noch sehr gering, obwohl Begriffsstrukturen, Ontologien und Topic Maps grundsätzlich gut zusammenpassen und ihre gemeinsame Betrachtung Erkenntnisse für zentrale wissensorganisatorische Fragestellungen wie z.B. semantische Interoperabilität und semantisches Retrieval erwarten lässt. Daher motiviert und skizziert dieser Beitrag die Grundidee, nach der es möglich sein müsste, eine Sprache zur Darstellung von Begriffsstrukturen in der Wissensorganisation geeignet mit Topic Maps zu verbinden. Eine genauere Untersuchung und Implementation stehen allerdings weiterhin aus. Speziell wird vermutet, dass sich der Concepto zugrunde liegende Formalismus CLF (Concept Language Formalism) mit Topic Maps vorteilhaft abbilden lässt 3 Damit können Begriffs- und Themennetze realisiert werden, die auf expliziten Begriffssystemen beruhen. Seitens der Wissensorganisation besteht die Notwendigkeit, sich mit aktuellen Entwicklungen auf dem Gebiet des Semantic Web und ontology engineering vertraut zu machen, aber auch die eigene Kompetenz stärker aktiv in diese Gebiete einzubringen. Damit dies geschehen kann, führt dieser Beitrag zum besseren Verständnis zunächst aus Sicht der Wissensorganisation knapp in Ontologien und Topic Maps ein und diskutiert wichtige Überschneidungsbereiche.
    Object
    Topic maps
  13. Moustafid, Y. El: Semantic Web Techniken für E-Learning (2003) 0.06 
    0.056258548 = product of:
  0.112517096 = sum of:
    0.112517096 = product of:
      0.22503419 = sum of:
        0.22503419 = weight(_text_:maps in 585) [ClassicSimilarity], result of:
          0.22503419 = score(doc=585,freq=36.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.7902063 = fieldWeight in 585, product of:
              6.0 = tf(freq=36.0), with freq of:
                36.0 = termFreq=36.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0234375 = fieldNorm(doc=585)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Die vorliegende Arbeit versucht, das Thema "Topic Maps" von verschiedenen Perspektiven zu betrachten. "Topic Maps" sind geordnete Wissensnetze. Sie stellen ein Hilfsmittel dar, um sich in der immer größer werdenden Informationsvielfalt zurechtzufinden und beim Navigieren trotz einer möglichen Informationsüberflutung die Übersicht zu behalten. Wie ein Stichwortverzeichnis in einem guten Fachbuch, helfen sie, die genau gesuchte Information zu finden. Die Tatsache, dass elektronische Informationen in größerem Umfang als die Seiten eines Buches vorliegen und auf heterogenen Plattformen gespeichert sind, zieht die Konsequenz mit sich, dass Topic Maps nicht nur aus einer Liste alphabetisch sortierter Stichworte bestehen. Vielmehr werden mit Hilfe von Topic Maps logische Konzepte entworfen, die Wissensnetze semantisch modellieren. In Zusammenhang mit Topic Maps spricht Tim Berner-Lee von der dritten Revolution des Internets. Die XTM-Arbeitsgruppe wirbt sogar mit dem Slogan "Das GPS des Web". So wie eine Landkarte eine schematische Sicht auf eine reale Landschaft ermöglicht und bestimmte Merkmale der Landschaft (z.B. Städte, Straßen, Flüsse) markiert, sind Topic Map in der Lage wichtige Merkmale eines Informationsbestandes festzuhalten und in Bezug zueinander zu setzen. So wie ein GPS-Empfänger die eigene Position auf der Karte feststellt, kann eine Topic Map die Orientierung in einer virtuellen Welt vernetzter Dokumente herstellen. Das klingt etwas exotisch, hat jedoch durchaus praktische und sehr weit gefächerte Anwendungen.
    In dieser Arbeit wurde zuerst der Übergang von Suchmaschinen zu einem semantischen Web beschrieben. Im zweiten Kapitel wurden die Topic Maps ausführlicher behandelt. Angefangen bei der Geschichte von Topic Maps, über die Entwurfsziele bis hin zu einem XTM-Tutorial . In diesem Tutorial wurden verschiedene Beispiele durchgeführt und die Lineare Topic Map von Ontopia vorgestellt. Abschließend wurde anhand eines Beispiels eine mögliche Realisierung von Topic Maps mit HTML. Das dritte Kapitel wurde den TopicMaps-Tools und Anfragesprachen gewidmet. Es wurden kommerzielle sowie freiverfügbare Tools vorgestellt und miteinander verglichen. Danach wurden die beiden Anfragesprachen Tolog und TMQL eingeführt. Im vierten Kapitel wurden die beiden Einsatzgebiete von Topic Maps behandelt. Das sind zum einen die Webkataloge und die Suchmaschinen. Zum anderen ist es möglich, auch im Rahmen vom E-Learning von dem Konzept der Topic Maps zu profitieren. In diesem Zusammenhang wurde erst der Omnigator von Ontopia vorgestellt. Dann wurde das im Laufe dieser Arbeit entwickelte Topic Maps Tool E-Learning -Tracker ausgeführt und erklärt.
    Im fünften Kapitel wurden die neuen Suchmaschinen, die ausschließlich auf dem Konzept der Topic Maps basieren und diese Technik auch tatsächlich verwenden, angesprochen und mit Beispielanfragen erläutert. In dieser Diplomarbeit wurden wegen dem großen Einsatzpotential von Topic Maps, viele Gebiete angesprochen, angefangen bei den Webkatalogen über Suchmaschinen bis hin zum E-Learning. Mit XML Topic Maps gibt man den Beziehungen zwischen den verschiedenen Topics die Chance sich auszuzeichnen. Damit erreicht die Suche eine neue, bis dahin unmögliche Qualität. Mit einer Topic Map lassen sich beispielsweise die klassischen Navigationselemente technischer Dokumentation (Inhalt, Index, Glossar etc.) in einheitlicher Weise beschreiben; eine andere Topic Map könnte die inhaltliche Vernetzung von Artikeln in einem Lexikon ausdrücken (z.B. Person A wurde geboren in Stadt B, B liegt in Land C, Oper D wurde komponiert von A, Person E war Zeitgenosse von A) und für "siehe auch"-Verweise sorgen (andere Werke dieses Komponisten, andere Städte in diesem Land etc.). Es klingt wie die Lösung aller Suchprobleme. Allerdings nur in der Theorie. Denn Tools, die in der Lage sind, das Wissen oder die Riesendaten in Topicmaps automatisch zu generieren, sind noch Mangelware, was die Ausbreitung von Topic Maps hemmt. Der Aufbau solcher Netze erfordert sehr viel Zeit und sehr viel "Handarbeit" - und damit auch viel Geld, was viele Firmen davon abhält Topic Maps zu benutzen.
    Trotz alledem bleibt diese Technik keine graue Theorie. Denn obwohl es spürbare Schwierigkeiten auf dem Weg zur Popularität gibt, wird sie eines Tages das Web beherrschen. Microsoft hat sogar versucht, einige Leute und Entwickler von Topic Maps abzuwerben, was ihr missglückt ist. Dies ist als ein Hinweis zu verstehen, dass diese Technik Interesse bei einigen Herrschern in der Informatikindustrie.
    Object
    Topic maps
  14. Widhalm, R.; Mück, T.: Topic maps : Semantische Suche im Internet (2002) 0.06 
    0.055910196 = product of:
  0.11182039 = sum of:
    0.11182039 = product of:
      0.22364078 = sum of:
        0.22364078 = weight(_text_:maps in 4731) [ClassicSimilarity], result of:
          0.22364078 = score(doc=4731,freq=20.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.78531337 = fieldWeight in 4731, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.03125 = fieldNorm(doc=4731)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Das Werk behandelt die aktuellen Entwicklungen zur inhaltlichen Erschließung von Informationsquellen im Internet. Topic Maps, semantische Modelle vernetzter Informationsressourcen unter Verwendung von XML bzw. HyTime, bieten alle notwendigen Modellierungskonstrukte, um Dokumente im Internet zu klassifizieren und ein assoziatives, semantisches Netzwerk über diese zu legen. Neben Einführungen in XML, XLink, XPointer sowie HyTime wird anhand von Einsatzszenarien gezeigt, wie diese neuartige Technologie für Content Management und Information Retrieval im Internet funktioniert. Der Entwurf einer Abfragesprache wird ebenso skizziert wie der Prototyp einer intelligenten Suchmaschine. Das Buch zeigt, wie Topic Maps den Weg zu semantisch gesteuerten Suchprozessen im Internet weisen.
    Content
    Topic Maps - Einführung in den ISO Standard (Topics, Associations, Scopes, Facets, Topic Maps).- Grundlagen von XML (Aufbau, Bestandteile, Element- und Attributdefinitionen, DTD, XLink, XPointer).- Wie entsteht ein Heringsschmaus? Konkretes Beispiel einer Topic Map.Topic Maps - Meta DTD. Die formale Beschreibung des Standards.- HyTime als zugrunde liegender Formalismus (Bounded Object Sets, Location Addressing, Hyperlinks in HyTime).- Prototyp eines Topic Map Repositories (Entwicklungsprozess für Topic Maps, Prototyp Spezifikation, technische Realisierung des Prototyps).- Semantisches Datenmodell zur Speicherung von Topic Maps.- Prototypische Abfragesprache für Topic Maps.- Erweiterungsvorschläge für den ISO Standard.
    Object
    Topic maps
  15. Kim, J.-M.; Shin, H.; Kim, H.-J.: Schema and constraints-based matching and merging of Topic Maps (2007) 0.05 
    0.054134816 = product of:
  0.10826963 = sum of:
    0.10826963 = product of:
      0.21653926 = sum of:
        0.21653926 = weight(_text_:maps in 922) [ClassicSimilarity], result of:
          0.21653926 = score(doc=922,freq=12.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.7603764 = fieldWeight in 922, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=922)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    In this paper, we propose a multi-strategic matching and merging approach to find correspondences between ontologies based on the syntactic or semantic characteristics and constraints of the Topic Maps. Our multi-strategic matching approach consists of a linguistic module and a Topic Map constraints-based module. A linguistic module computes similarities between concepts using morphological analysis, string normalization and tokenization and language-dependent heuristics. A Topic Map constraints-based module takes advantage of several Topic Maps-dependent techniques such as a topic property-based matching, a hierarchy-based matching, and an association-based matching. This is a composite matching procedure and need not generate a cross-pair of all topics from the ontologies because unmatched pairs of topics can be removed by characteristics and constraints of the Topic Maps. Merging between Topic Maps follows the matching operations. We set up the MERGE function to integrate two Topic Maps into a new Topic Map, which satisfies such merge requirements as entity preservation, property preservation, relation preservation, and conflict resolution. For our experiments, we used oriental philosophy ontologies, western philosophy ontologies, Yahoo western philosophy dictionary, and Wikipedia philosophy ontology as input ontologies. Our experiments show that the automatically generated matching results conform to the outputs generated manually by domain experts and can be of great benefit to the following merging operations.
  16. Saarikoski, J.; Laurikkala, J.; Järvelin, K.; Juhola, M.: ¬A study of the use of self-organising maps in information retrieval (2009) 0.05 
    0.054134816 = product of:
  0.10826963 = sum of:
    0.10826963 = product of:
      0.21653926 = sum of:
        0.21653926 = weight(_text_:maps in 2836) [ClassicSimilarity], result of:
          0.21653926 = score(doc=2836,freq=12.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.7603764 = fieldWeight in 2836, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2836)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Purpose - The aim of this paper is to explore the possibility of retrieving information with Kohonen self-organising maps, which are known to be effective to group objects according to their similarity or dissimilarity. Design/methodology/approach - After conventional preprocessing, such as transforming into vector space, documents from a German document collection were trained for a neural network of Kohonen self-organising map type. Such an unsupervised network forms a document map from which relevant objects can be found according to queries. Findings - Self-organising maps ordered documents to groups from which it was possible to find relevant targets. Research limitations/implications - The number of documents used was moderate due to the limited number of documents associated to test topics. The training of self-organising maps entails rather long running times, which is their practical limitation. In future, the aim will be to build larger networks by compressing document matrices, and to develop document searching in them. Practical implications - With self-organising maps the distribution of documents can be visualised and relevant documents found in document collections of limited size. Originality/value - The paper reports on an approach that can be especially used to group documents and also for information search. So far self-organising maps have rarely been studied for information retrieval. Instead, they have been applied to document grouping tasks.
  17. Cregan, A.: ¬An OWL DL construction for the ISO Topic Map Data Model (2005) 0.05 
    0.054134816 = product of:
  0.10826963 = sum of:
    0.10826963 = product of:
      0.21653926 = sum of:
        0.21653926 = weight(_text_:maps in 4718) [ClassicSimilarity], result of:
          0.21653926 = score(doc=4718,freq=12.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.7603764 = fieldWeight in 4718, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4718)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Both Topic Maps and the W3C Semantic Web technologies are meta-level semantic maps describing relationships between information resources. Previous attempts at interoperability between XTM Topic Maps and RDF have proved problematic. The ISO's drafting of an explicit Topic Map Data Model [TMDM 05] combined with the advent of the W3C's XML and RDFbased Description Logic-equivalent Web Ontology Language [OWLDL 04] now provides the means for the construction of an unambiguous semantic model to represent Topic Maps, in a form that is equivalent to a Description Logic representation. This paper describes the construction of the proposed TMDM ISO Topic Map Standard in OWL DL (Description Logic equivalent) form. The construction is claimed to exactly match the features of the proposed TMDM. The intention is that the topic map constructs described herein, once officially published on the world-wide web, may be used by Topic Map authors to construct their Topic Maps in OWL DL. The advantage of OWL DL Topic Map construction over XTM, the existing XML-based DTD standard, is that OWL DL allows many constraints to be explicitly stated. OWL DL's suite of tools, although currently still somewhat immature, will provide the means for both querying and enforcing constraints. This goes a long way towards fulfilling the requirements for a Topic Map Query Language (TMQL) and Constraint Language (TMCL), which the Topic Map Community may choose to expend effort on extending. Additionally, OWL DL has a clearly defined formal semantics (Description Logic ref)
    Object
    Topic maps
  18. Pepper, S.; Groenmo, G.O.: Towards a general theory of scope (2002) 0.05 
    0.054134816 = product of:
  0.10826963 = sum of:
    0.10826963 = product of:
      0.21653926 = sum of:
        0.21653926 = weight(_text_:maps in 539) [ClassicSimilarity], result of:
          0.21653926 = score(doc=539,freq=12.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.7603764 = fieldWeight in 539, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=539)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    This paper is concerned with the issue of scope in topic maps. Topic maps are a form of knowledge representation suitable for solving a number of complex problems in the area of information management, ranging from findability (navigation and querying) to knowledge management and enterprise application integration (EAI). The topic map paradigm has its roots in efforts to understand the essential semantics of back-of-book indexes in order that they might be captured in a form suitable for computer processing. Once understood, the model of a back-of-book index was generalised in order to cover the needs of digital information, and extended to encompass glossaries and thesauri, as well as indexes. The resulting core model, of typed topics, associations, and occurrences, has many similarities with the semantic networks developed by the artificial intelligence community for representing knowledge structures. One key requirement of topic maps from the earliest days was to be able to merge indexes from disparate origins. This requirement accounts for two further concepts that greatly enhance the power of topic maps: subject identity and scope. This paper concentrates on scope, but also includes a brief discussion of the feature known as the topic naming constraint, with which it is closely related. It is based on the authors' experience in creating topic maps (in particular, the Italian Opera Topic Map, and in implementing processing systems for topic maps (in particular, the Ontopia Topic Map Engine and Navigator.
  19. Schrodt, R.: Tiefen und Untiefen im wissenschaftlichen Sprachgebrauch (2008) 0.05 
    0.053661376 = product of:
  0.10732275 = sum of:
    0.10732275 = product of:
      0.32196826 = sum of:
        0.32196826 = weight(_text_:3a in 140) [ClassicSimilarity], result of:
          0.32196826 = score(doc=140,freq=2.0), product of:
            0.42965913 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050679237 = queryNorm
            0.7493574 = fieldWeight in 140, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0625 = fieldNorm(doc=140)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)
    Content
    Vgl. auch: https://studylibde.com/doc/13053640/richard-schrodt. Vgl. auch: http%3A%2F%2Fwww.univie.ac.at%2FGermanistik%2Fschrodt%2Fvorlesung%2Fwissenschaftssprache.doc&usg=AOvVaw1lDLDR6NFf1W0-oC9mEUJf.
  20. Garshol, L.M.: Living with topic maps and RDF : Topic maps, RDF, DAML, OIL, OWL, TMCL (2003) 0.05 
    0.053590734 = product of:
  0.10718147 = sum of:
    0.10718147 = product of:
      0.21436293 = sum of:
        0.21436293 = weight(_text_:maps in 3886) [ClassicSimilarity], result of:
          0.21436293 = score(doc=3886,freq=6.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.75273424 = fieldWeight in 3886, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3886)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Object
    Topic maps

Authors

Languages

Types

  • a 1199
  • m 162
  • el 81
  • s 58
  • b 27
  • x 20
  • i 9
  • n 3
  • r 1
  • More… Less…

Themes

Subjects

Classifications