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.
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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
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Uses Protege and Jena to convert to OWL and RDF
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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).
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Outlines RDF, OWL, Protege and Jena (described as leading platforms; mentions other platforms: WebODE, OntoEdit, KAON1, Sesame).
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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.
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Detailed comparative studies of methodologies in:
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- 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.
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Case study: Ontology for monitoring development projects in developing countries (OntoDPM)
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Create with Protege:
- class heirarchies
- slots
- domain and range of slots
- Based on the UML
- Saved as OWL
- Then put content in RDF with Jena
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*Mike Uschold and Martin King (1995) **Towards a Methodology for Building Ontologies. Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95.
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Steps:
- identify purpose
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build ontology
- capture
- coding
- integrating existing ontologies
- evaluation
- 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
- Capture
- 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.
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Documentation
- Desirable to have established guidelines for documenting
- Main barrier to effective knowledge sharing is inadequate documentation
- ALL important assumptions should be documented
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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
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Categorisation is fundamental to the human condition (Lakoff)
- Not heirarchical, but:
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GENERAL
^
BASIC -> primary with respect to knowledge organisation
v
SPECIFIC - 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.
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