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Notes about ontology creation methodologies (2 papers)

Yesterday I unexpectedly read two whole papers about ontology development methodologies. They were open in tabs I don't remember opening, but presumably did so during our weekly Ontologies With A View meeting last Friday. There are still a bunch more tabs open with papers or articles about the same thing, so maybe I'll read those later..

The notes are here more or less as I scribbled them down whilst reading, and I haven't expanded with any analysis or discussion as of yet.

Notes in purple are things I intend/need to investigate further; colour-coding is just for me, really.

Jean Vincent Fonou-Dombeu & Magda Husiman (2011) Combining Ontology Development Methodologies **and Semantic Web Platforms for E-government **Domain Ontology Development. International Journal of Web & Semantic Technology (IJWesT) Vol.2, No.2, April 2011

  • Start by describing ontology in a human-readable way, then turn to RDF (etc) to be machine readable.
  • Says there's not sufficient practical research around existing technologies or ontology development guidelines that would allow non-experts in e-government domain to make ontologies.

  • Uses framework from Uschold & King (see later in this post) to describe ontology - technique used here should be platform independent.
  • Then uses UML to semi-formally represent ontology
  • Uses Protege and Jena to convert to OWL and RDF

  • Paper's goal is to produce guidelines for e-government developers to create semantic content AND strengthen adoption of Semantic Web technologies in governments (particularly developing countries).

  • Outlines RDF, OWL, Protege and Jena (described as leading platforms; mentions other platforms: WebODE, OntoEdit, KAON1, Sesame).

  • Very critical of other literature; either ontologies have been produced but no practical information given; they've been developed with proprietary platforms; or they're only conceptual and don't say how they could actually be constructed with existing technologies. other studies have not focused on a methodological approach, which means nothing is easily repeatable.

  • Detailed comparative studies of methodologies in:

      • M. Fernandez-Lopez, “Overview of Methodologies for Building Ontologies, ⁂ In Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5), Stockholm, Sweden, 2 August, 1999.
    • H. Beck and H.S Pinto, “Overview of Approach, Methodologies, Standards, and Tools for Ontologies,⁂ Agricultural Ontology Service (UNFAO), 2003.
    • C. Calero, F. Ruiz and M. Piattini, “Ontologies for Software Engineering and Software Technology, ⁂ Calero.Ruiz.Piattini (Eds.), Springer-Verlag Berlin Heidelberg, 2006.
  • Case study: Ontology for monitoring development projects in developing countries (OntoDPM)

    1. Create with Protege:

      • class heirarchies
      • slots
      • domain and range of slots
      • Based on the UML
      • Saved as OWL
    2. Then put content in RDF with Jena

*Mike Uschold and Martin King (1995) **Towards a Methodology for Building Ontologies. Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95.

  • Steps:

    1. identify purpose
    2. build ontology

      • capture
      • coding
      • integrating existing ontologies
    3. evaluation
    4. documentation
  • Purpose
    • Many ontologies are intended for reuse
    • Should survey purposes to clarify options for future projects
  • Building
    • Capture
      • Identify key concepts and relationships in domain of interest
      • Produce unambiguous text definitions for these
      • Identify terms for these
      • Agree on all of the above
    • Coding
      • Explicit representation of conceptualisation in a formal language (choose a language)
    • When can capture and coding stages be merged?
    • Differences between building ontology and creating a general knowledge base (thinking about methodology will help with this)
    • Integrating (during either or both of above)
      • Work must be done in agreement between communities
      • Make explicit all assumptions underlying an ontology
  • Evaluation
    • Judge against requirements specification (and/or)
    • Judge against competency questions (and/or)
    • Judge against real life
    • This paper looks at knowledge base systems, and adapts for ontologies.
  • Documentation

    • Desirable to have established guidelines for documenting
    • Main barrier to effective knowledge sharing is inadequate documentation
    • ALL important assumptions should be documented
  • Case Study

    • Main emphasis is on capture phase
    • Initially:
      • define ontology (Gruber)
      • identify users and usage (initially abstract, then clarify with real life)
      • choose language (Ontolingua was chosen)
      • choose method for capture - BDSM (IBM) supported by others:
      • KADS
      • IDEF5
      • OO Analysis and Design techinques
      • Gruber's principles for ontology design
    • Categorisation is fundamental to the human condition (Lakoff)

      • Not heirarchical, but:
      • GENERAL
        BASIC -> primary with respect to knowledge organisation

      • eg.
        SUPER: Animal / Furniture
        BASIC: Dog / Chair
        SUB: Retriever / Rocker
      • Certain concepts used subconsciously, rather than understood intellectually.
        • These have a more important psychological status.
      • Therefore paper uses middle-out approach to capture terms
        • (bottom-up = too much detail unnecessarily,
        • top-down = risks imprecision)
      • BASIC concepts first because:
        • most important
        • used to define non-BASIC terms
        • increase clarity, especially for non-technical use
        • backed by BSDM experience of paper author
  • Scoping
    • Brainstorming
    • Consult corpora if there aren't enough domain experts to brainstorm
    • Grouping
      • structure terms into naturally arising sub-groups
      • collate synonyms
      • consider things that might refer to each other
  • Meta-ontology
    • Don't commit too early, can restrict thinking. Let concepts and relationships themselves determine requirements.
    • Be consistent.
    • Use technologically neutral language ('thing' vs 'entity').
    • Start with areas where there's most overlap.
    • Work from basic terms to more abstract ones within an area.
  • Producing definitions
    • Agreeing on definitions (varying degrees of problems)
    • Handling ambiguous terms
      • clarify ideas without technical terms
      • use a dictionary!
      • label definitions, eg. x1, x2
      • determine most important concept
      • choose a term, avoiding original ambigious one
    • Avoid new terms
    • Terms get in the way (peoples' preconceived ideas) - concentrate on underlying meaning and concepts.

🏷 methodologies ontologies ontologieswithaview ontology building ontology phd rdf semantic web

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