ECML – Ontology Learning Tutorial

Aims of the Tutorial

  • Give an overview of Ontology Learning techniques as well as a synthesis of approaches
  • Provide a ‘start kit’ for Ontology Learning
  • Highlight interdisciplinary aspects and opportunities for a combination of techniques

Structure of the Tutorial

  • Part I Introduction – Philipp Cimiano
  • Part II Ontologies in Knowledge Management & Ontology
  • Life Cycle – Michael Sintek
  • Part III Methods in Ontology Learning from Text –
  • Paul Buitelaar & Philipp Cimiano
  • Part IV Ontology Evaluation – Marko Grobelnik
  • Part V Tools for Ontology Learning from Text – All
  • Wrap-up Paul Buitelaar

Some pre-History

  • AI: Knowledge Acquisition Since 60s/70s: Semantic Network Extraction and similar for Story Understanding. Systems: e.g. MARGIE (Schank et al., 1973), LUNAR (Woods, 1973)
  • NLP: Lexical Knowledge Extraction. 70s/80s: Extraction of Lexical Semantic Representations from Machine Readable: Dictionaries.  Systems: e.g. ACQUILEX LKB (Copestake et al.). 80s/90s: Extraction of Semantic Lexicons from Corpora for  Information Extraction. Systems: e.g. AutoSlog (Riloff, 1993), CRYSTAL (Soderland et al., 1995)
  • IR: Thesaurus Extraction. Since 60s: Extraction of Keywords, Thesauri and Controlled Vocabularies. Based on construction and use of thesauri in IR (Sparck-Jones, 1966/1986, 1971). Systems: e.g. Sextant (Grefenstette, 1992), DR-Link (Liddy, 1994)

Ontologies in Computer Science

  • Ontology refers to an engineering artifact:
  • It is constituted by a specific vocabulary used to describe a certain reality, as well as
  • a set of explicit assumptions regarding the intended meaning of
  • the vocabulary.
  • An ontology is an explicit specification of a conceptualization. ([Gruber 93])
  • An ontology is a shared understanding of some domain of interest. ([Uschold & Gruninger 96])

Why Develop an Ontology?

  • To make domain assumptions explicit
    • Easier to change domain assumptions
    • Easier to understand and update legacy data
  • To separate domain knowledge from operational knowledge
    • Re-use domain and operational knowledge separately
  • A community reference for applications
  • To share a consistent understanding of what information means

Tools for Ontology Learning from Text.

SEKTbar: User profiling
Jožef Stefan Institute
􀂄 A Web-based user profile is automatically generated while the user is browsing the Web.
􀂉 It is represented in the form of a user-interest-hierarchy (UIH)
􀂉 The root node holds the user’s general interest, while leaves hold more specific interests
􀂉 UIH is generated by using hierarchical k-means clustering algorithm
􀂉 Nodes of current interest are determined by comparing UIH node centroids to the centroid computed out of the m most recently visited pages.
􀂄 The user profile is visualized on the SEKTbar (Internet Explorer Toolbar)
􀂉 The user can select a node in the hierarchy to see its specific keywords and associated pages (documents)
Availability: open source (C++, .NET)
Link: http://www.textmining.net

http://www.sekt-project.com

© Paul Buitelaar, Philipp Cimiano, Marko Grobelnik, Michael Sintek: Ontology Learning from Text. Tutorial at ECML/PKDD, Oct. 2005, Porto, Portugal.

ECML-OntologyLearningTutorial-20050923.pdf (application/pdf Objeto).

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