MW: Well I’m not sure there is a generally agreed definitions of what an ontology is [[Sjir2: indeed, I agree with you and I believe this forum should admit that clearly, and start work to get to a series of definitions that can be assigned to the widely varying kinds of “ontologies” mentioned in this forum.]], but we are not talking about the philosophical study of what exists. [[Sjir2: I agree.]] My definition for the purposes of this summit is:
A formal (i.e. computer processable) representation of (some of) the things that exists and (some of) the rules that govern them.
[[Sjir2: Another proposal: A complete and truely conceptual (in the sense of ISO TR9007) ontology is a formal (i.e. computer processable) representation of
a. the kinds of things considered within scope of a certain ontology,
b. the kinds of facts about instances of these kinds of kinds and
c. all the associated integrity rules about the fact populations and fact population transitions.
d. There is always a human understandable representation (in a CNL), that is extended with a set of all relevant concept definitions.]]
MW: As I said, there is no general consensus on the definition of ontology, and you immediately prove my point. Broadly I think your definition amounts to the same as mine, and is pretty much in line with what I would expect a definition of a conceptual data model to be, but there are some key differences.
· Your definition is restricted to “kinds of things”. This is fine for a data model, but other forms of ontology (e.g. OWL or CL based ontologies, or indeed master data) can include individuals such as you me and the USA. Also,
· an ontology does not just need to consist of facts, but can include negations. Again, in databases we do generally only hold things that are asserted to be true (at least you have to work very hard to hold something negative) but that is not a limitation of other forms of ontology. Finally,
· there are far more rules than integrity rules (unless you have a very different meaning for the word “integrity” than I have).
Examples can be as diverse as Cyc, a database schema, and Master Data.
1. Measure the quality of the result against the requirements that it should meet and fix the defects. [[Sjir: I suggest to take the three principles (Helsinki, 100 % and Conceptual) of ISO TR9007 into account.]]
MW: I’m sorry, I don’t follow you there. Could you elaborate please?
[[Sjir2: ISO TR9007 (TR stands for Technical Report, often a predecessor of a standard) was an effort by ISO that started in 1978 and was finished in 1987. It includes a validatable definition of a conceptual schema (The description of the possible states of affairs of the universe of discourse including the classifications, rules, laws, etc., of the universe of discourse.
MW: Yes, I remember it. It was enormously influential in the data modelling community.
(Page I-4) ) and the following three principles:
The Helsinki Principle
Any meaningful exchange of utterances depends upon the prior existence of an agreed set of semantic and syntactic rules. The recipients of the utterances must only use these rules to interpret the received utterances, if it is to mean the same as that which was meant by the utterer. ISO TC97/SC5/WG3- Helsinki 1978 (Page 0-2)
MW: As long as those rules include the definition of terms used in the exchange so the intended interpretation is clear, then that makes good sense. Of course it is still necessary if they are not, just not sufficient.
Conceptualization Principle
A conceptual schema should only include conceptually relevant aspects, both static and dynamic, of the universe of discourse, thus excluding all aspects of (external and internal) data representation, physical data representation and access as well as all aspects of a particular external user representation such as message format, data structures, etc. (page I-9)
MW: Yes. This is the sense in which data modellers use the word “conceptual”, i.e. it is a model of the things, rather than the way data about the things are structured.
100 Percent Principle
All relevant general static and dynamic aspects, i.e. all rules, laws, etc., of the universe of discourse should be described in the conceptual schema. The information system cannot be held responsible for not meeting those described elsewhere, including in particular those in application programs. (page I-8)
MW: The key question here is what makes something relevant?
2. Use a process or methodology to ensure the quality of the resultant ontology. [[Sjir: I stongly agree with this.]]
That is, Proactive versus Reactive.
The advantage of using a methodology are that you get it (or at least more of it) right first time, thus avoiding the cost of rework to fix the defects. [[Sjir: I stongly agree with this.]]
- Do such methodologies exist for ontologies? [[Sjir: that depends on what you mean by ontology. Informally yes, but that is outside the “ontology”” community.]]
MW: I believe there are also some within the “ontology” community, as well as the broader data modelling/relational database community. [[Sjir2: please let me know which ones.]]
MW: At least parts of the Medical/Biological community have been using a methodology developed by Barry Smith and his co-workers. I presume Cyc have a methodology (they must have to successfully develop something of that size). Chris Partridge has his Boro methodology. We have our ISO 15926 methodology (though that has its origins in data modelling) and I’m sure there are others. I’m hoping we will hear from them on this track.
Regards
Matthew West
Information Junction
Tel: +44 1489 880185
Mobile: +44 750 3385279
Skype: dr.matthew.west
matthew.west@xxxxxxxxxxxxxxxxxxxxxxxxx
http://www.informationjunction.co.uk/
http://www.matthew-west.org.uk/
This email originates from Information Junction Ltd. Registered in England and Wales No. 6632177.
Registered office: 2 Brookside, Meadow Way, Letchworth Garden City, Hertfordshire, SG6 3JE.