Quite right - thanks Barry. I was just looking at the basic
principles but looking at what's already defined (especially if
it's been defined by a competent industry standards body) should
come before those first steps. (01)
On 04/11/2010 14:28, Barry Smith wrote:
> Mike leaves out one important step:
> 0. Find out what already exists in the relevant domain, and use what
> has already been created as far as possible.
> In this case the first places to look will include:
> At 10:08 AM 11/4/2010, Mike Bennett wrote:
>> Hi Pradeep,
>> Here's how I tend to look at creating an ontology. Assuming the
>> ontology is intended as a conceptual model of the reality itself,
>> and is not constrained by any specific application requirement (i.e.
>> it's a model of the problem and not the solution), I would proceed as
>> 1. Have a strict taxonomic hierarchy in which something sits below
>> something else if it is a "kind" of that thing, that is it inherits
>> all the facts defined for that thing above, and its parents and so on;
>> 2. If there are multiple ways of classifying things in the problem
>> domain, consider honoring all that are relevant (for example, a
>> whale is both a Mammal and a MarineAnimal). However, you may wish to
>> consider that while reality and therefore ontology support multiple
>> inheritance, most of the applications that use this information will
>> not. Having multiple taxonomies will enable you to develop (and map
>> against) different applications which classify the diseases in
>> different ways. So you should be able to achieve a taxonomy for any
>> application that you need to deal with.
>> 3. At the top of your hierarchy have what sort of "Thing" everything
>> is, that is "DiseasePhenotype" I would imagine. If there is a
>> separate way of classifying the diseases (genotype?) then put a
>> separate top level term, with an even more general term above that
>> (e.g. Disease).
>> 4. Finally the facts (properties). For every new kind of thing
>> (every disease) ask yourself two questions:
>> - what sort of thing is it? (this determines where it goes in the
>> taxonomy e.g. is it a neurone related or muscle related condition);
>> - what facts distinguish it from other things in that part of the taxonomy?
>> This latter gives you the necessary facts that define that that
>> thing is what it is.
>> There may be other, incidental facts. In financial securities we
>> have lots of facts that always apply to a given kind of security so
>> it's not always obvious which one is a necessary, defining fact. For
>> example exchange traded options are distinguished by the fact that
>> they are traded on an exchange, and also by the fact that they have
>> specific, standardized legal terms. So don't worry too much about
>> distinguishing necessary from incidental facts since the main
>> semantics notations don't have a means to put that in anyway. Just
>> make sure you have captured the necessary facts that give a thing its
>> If no facts distinguish something from something else, you have a
>> synonym (at least at the level of detail you have considered
>> appropriate for this particular ontology). Rather than have separate
>> classes for each word or disease name that exists, you should
>> consider having one class for each meaning, and then identify
>> synonymous words in a separate "synonym" tag. Unfortunately there
>> isn't a synonym tag in OWL (people seem happy to have a class per
>> word and use equivalence relations, which is unnecessary and I would
>> suggest counter productive). So either use RDFS Label or find some
>> way of identifying particular RDFS Labels as "synonym". Here you
>> might also want to have separate synonym tags with a language
>> marker, so you have all your Latin terms.
>> Ultimately the questions of what amount of relationships / facts to
>> put in, is a judgement call based on what level of detail of terms
>> you need in any applications you will develop from this ontology, or
>> what level of detail exists in databases, data feeds and
>> applications that you wish to relate to with this ontology, for
>> example if you are using it for integration of disparate systems or
>> to create a common messaging language.
>> What I found is that once you get an initial draft in place and
>> present this to the business subject matter experts to review (I
>> hope you will be doing this, since it's their knowledge), they will
>> tend to let you know about additional facts and levels of detail
>> that are relevant to a given application or to their view of the
>> business domain. For example when we looked at legal entities, we
>> had 4 terms for kinds of relationships among entities, but the
>> business experts identified up to 4 possible shades of meaning for
>> each of these, that they considered relevant, for instance for
>> different levels of control and different kinds of control that one
>> legal entity has over another.
>> So once you have got your ontology into the hands of the business
>> domain experts they will pretty much fine tune the level of detail
>> for you. The key is making sure that it is structured, and can be
>> presented and explained, in simple set theory logic with the
>> necessary defining facts in place.
>> Hope this helps,
>> Mike Bennett
>> On 04/11/2010 13:38, Pradeep Kumar S wrote:
>>> Dear all,
>>> I hope this is the right place to request for some help I need
>>> developing a cardiac disease/phenotype ontology.
>>> I have been curating list of phenotype/disease terms from
>>> biomedical literatures for past 1year.
>>> Now the collection has grown to about ~1200 terms. I have the
>>> following attributes curated along with the terms:
>>> 1. Term name
>>> 2. Source ( web, reference of book or paper)
>>> 3. Some times definition and the context in which it was curated.
>>> I request experts here to suggest me how to proceed further from
>>> list of terms to making a good ontology?
>>> Specifically I need to know:
>>> 1. how many relationships should I consider initially. Putting
>>> terms under hierarchy like a taxonomy will be the easiest. But how
>>> to incorporate other plausible relationships. When (how many) would
>>> it get very complex?
>>> 2. Basically I want to render a ontology that could be used by
>>> data mining tools and serve biological community in their data analysis.
>>> Any suggestions from experts here will be highly appreciated. Also
>>> please point me to any websites/literature/books that would be of help.
>>> Thanking you,
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