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  • × author_ss:"Hitzler, P."
  1. Hitzler, P.; Krötzsch, M.; Rudolph, S.; Sure, Y.: Semantic Web : Grundlagen (2008) 0.01
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    Abstract
    Das Buch Semantic Web Grundlagen vermittelt als erstes deutschsprachiges Lehrbuch die Grundlagen des Semantic Web in verständlicher Weise. Es ermöglicht einen einfachen und zügigen Einstieg in Methoden und Technologien des Semantic Web und kann z.B. als solide Grundlage für die Vorbereitung und Durchführung von Vorlesungen genutzt werden. Die Autoren trennen dabei sauber zwischen einer intuitiven Hinführung zur Verwendung semantischer Technologien in der Praxis einerseits, und der Erklärung formaler und theoretischer Hintergründe andererseits. Nur für letzteres werden Grundkenntnisse in Logik vorausgesetzt, die sich bei Bedarf jedoch durch zusätzliche Lektüre und mit Hilfe eines entsprechenden Kapitels im Anhang aneignen lassen.Das Lehrbuch richtet sich primär an Studenten mit Grundkenntnissen in Informatik sowie an interessierte Praktiker welche sich im Bereich Semantic Web fortbilden möchten.
    Classification
    ST 252 Informatik / Monographien / Software und -entwicklung / Web-Programmierung, allgemein
    Content
    Inhalt: Die Idee des Semantic Web - Stuktur mit XML - Einfache Ontologien in RDF und RDF Schema - Formale Semantik von RDF(S) - Ontologien in OWL - Ontologien in OWL - Formale Semantik von OWL - Anfragesprachen - Pradikatenlogik - kurzgefasst - Naive Mengenlehre - kurzgefasst
    RVK
    ST 252 Informatik / Monographien / Software und -entwicklung / Web-Programmierung, allgemein
  2. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.00
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    Date
    16.11.2018 14:22:01
  3. Krötzsch, M.; Hitzler, P.; Ehrig, M.; Sure, Y.: Category theory in ontology research : concrete gain from an abstract approach (2004 (?)) 0.00
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    Abstract
    The focus of research on representing and reasoning with knowledge traditionally has been on single specifications and appropriate inference paradigms to draw conclusions from such data. Accordingly, this is also an essential aspect of ontology research which has received much attention in recent years. But ontologies introduce another new challenge based on the distributed nature of most of their applications, which requires to relate heterogeneous ontological specifications and to integrate information from multiple sources. These problems have of course been recognized, but many current approaches still lack the deep formal backgrounds on which todays reasoning paradigms are already founded. Here we propose category theory as a well-explored and very extensive mathematical foundation for modelling distributed knowledge. A particular prospect is to derive conclusions from the structure of those distributed knowledge bases, as it is for example needed when merging ontologies