> [MB] This is what the Open Ontology
> Repository project is about. (01)
[ppy] Yes, indeed ... thank you very much, Mike. (02)
or: http://ontolog.cim3.net/cgi-bin/wiki.pl?OpenOntologyRepository (03)
... this is a global community initiative (incubated at the Ontolog
collaborative work environment), and a work-in-progress, obviously. I
will take the opportunity to make a call, one more time, for members
of this community to get involved! (04)
Thanks & regards. =ppy (05)
Regards. =ppy (06)
Peter P. Yim (07)
OpenOntologyRepository (OOR) Initiative
On Thu, Nov 4, 2010 at 7:48 AM, Mike Bennett <mbennett@xxxxxxxxxxxxxxx> wrote:
> Hi Jeff,
> Nice to see you in here. This is what the Open Ontology
> Repository project is about. I'll let others describe it in
> detail since the calls are usually at a time I can't manage and
> I'm a bit out of the loop, but this will be a repository in which
> ontologies can be placed for public search, and we can start to
> address some of the questions around ontology interoperability
> which I know you're interested in.
> Mike (010)
> On 04/11/2010 14:39, Zhuk, Yefim wrote:
>> Mike and Barry,
>> What are the places populated with multiple ontologies for convenient search?
>> Thank you,
>> Jeff (Yefim) Zhuk (011)
>> -----Original Message-----
>> From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
>[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Barry Smith
>> Sent: Thursday, November 04, 2010 10:29 AM
>> To: [ontolog-forum] ; Pradeep Kumar S; [ontolog-forum]
>> Subject: Re: [ontolog-forum] Need suggestions developing cardiac
>> 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:
>> BS (012)
>> 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
>>> 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 (013)
>>> 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,
>>>> --Pradeep (014)
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