Amy Guy

Raw Blog

Wednesday, November 21, 2012

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.