SemanticWeb Meetup Zürich 2010-06-10

SemanticWeb Meetup Zürich 2010-06-10

Coordinates:

  • Ort: Uni Zürich, Institut für Bankenwesen, Plattenstrasse 32, Seminarraum 4
  • Datum: 10.6.2010
  • Uhrzeit: 17:30

Handout:

Context:

  • Full title of the group: Semantic Web Technology for Business Processes
  • Objective of the group: Meet local people interested in the Semantic Web, an initiative by the W3C [http://www.w3c.org] to make the web "one giant database": The Data Web. We address technologies such as RDF, RDFS, OWL and applications that help to develop or that use ontologies, controlled vocabularies and rules systems in the enterprise and on the World Wide Web. And we adress business relevant applications that are based on semantic web technologies
  • Homepage of the group: http://www.meetup.com/Zurich-Semantic-Web-Meetup-com/

Language: The talk is given in german. Vortrag und Diskussion sind in deutscher Sprache.

Unser Thema heute

Annahme: Auf einem w3c Meetup zum Thema "Semantic Web Technology for Business Processes" treffen sich sowohl Intreressierte wie auch Experten, die ins Gespräch kommen wollen.

mögliches Problem: extreme Teilnehmer-Heterogenität. Abhilfe: Doppeldecker, d.h. reden über eine konkrete Einführung in das Thema.

Übergreifende Frage heute:

  • Wie erklären wir jemandem, was eine Ontologie ist?

Titel: Was ist eine Ontologie - und wo hilft sie?

Inhalt: Semantic Web, Linked Data, Ontologien: Diese Schlüsselkonzepte stehen für ausdrucksstarke Ansätze moderner Entwicklungen in webbasierten und corporate IT-Systemen. Der Vortrag gibt eine Einführung in die Grundlagen methodischer Ontologie-Entwicklung. Statt einzelne Technologien zu diskutieren geben wir einen Überblick über typische sozio-technische Szenarien, in denen mit Ontologien elegante und flexible Lösungen für Wissensarbeiter realisiert werden können.

Zielgruppe: Entscheider mit oberflächlichem Vorwissen; es besteht die Möglichkeit zu individuellen Diskussionen!

Title: What is an ontology, and what is it's added value?

Semantic Web, linked data, ontologies: Key concepts like these today stand for expressive architectures of web based and corporate IT systems. Our talk introduces into basic methods of ontology engineering. Instead of focusing on distinct technologies we give an overview over typical socio-technical szenarios which allow for smart and flexible solutions for knowledge workers.

audience: decision makers with shallow previous knowledge; discussions are welcome!

Talk: Inductive introduction into "What is an ontology?"

An ontology is a sort of a category system

Handout: Ontology "Category Systems"

Each term is a specialization of it's predecessor.

  • In fact we have a built a taxonomy.
  • According to this term list an ontology is a sophisticated taxonomy.

classification agenda

  • top down
  • for each subclass classify if all features are given
  • extension shrinks

Meta terms

  • terms (classes, sets) are defined by features (intension)
  • features define class tree
  • examples (extension)
  • intension vs. extension

Comparing XML and Semantics

free format

  • CSV

XML

  • data are parsed according to a uniform syntax: you don't need to write a parser any more!
  • underlying meta data model: XML tree, i.e. nested tagged content
  • XML Schema allows for validating syntax of data
  • language XSLT allows for transforming syntax, i.e. XML trees into XML trees
  • schema check = analyse syntax of data = check coherence with grammar
  • understanding of the data is open / 2b defined / undefined: bug or feature?

semantic

  • data are interpreted according to an uniform knowledge representation: you don't need to write an inferencing engine any more!
  • underlying data model: directed labeled graph
  • schema part of an ontology allows for validating semantic (model conformity) of data
  • description logic allows for
    • classifying individuals
    • checking soundness of a classification system
  • rule languages allow for model transformations, i.w. translations of data models into data models
  • semantic check = analyze understanding of data = check for coherence with a set of logical rules
  • ontology schema allows for checking logical soundness (Widerspruchsfreiheit)

Typical tasks where an ontology may help you

c.f. http://web.jbusse.de/training/whereAnOntologyMayHelpYou_%28en%29.html