What you begin to confront is the fact that if the ontologies were isolated both in terms of not using a shared foundational ontology, there were likely also isolated in the sense of not sharing "best practices" for ontology construction:

                      - many definitions are imprecise, circular, or - in fact - non-existent 

                      - the same is true for relations - they are used in an imprecise, inconsistent, or incommensurate manner (a big problem initially for GO) - mixing "is_a" with the MANY KINDS of "part of" can be a semantic data integration nightmare**.

                      - loosely defined and used lexical facets associated with nodes in the ontology - e.g., acronyms, abbreviations, and misspellings may get used indiscriminately as synonyms - sometimes that's valid, and sometimes its not.

                      - plurals and pre-coordinated descriptions cutting across knowledge domains may appear in the graph - which can limit computability at fine-grained levels.

 

[VK] So, I am in agreement with the fact that best practices need to be evolved for creating biomedical ontologies in general. Foundational ontologies could be an aspect of these best practices, but it is going to be

tricky. Past efforts at creating a global ontology (Cyc) haven’t been particularly useful. I think in order to get a set of primitives that characterize the entire range of biomedical activity one has to be at a very

high level of abstraction, leading to lack of utility for interoperation for instance. If however, done right, it has great potential to be useful.

 

Can you provide a concrete example where having a foundational ontology helped create a “better” ontology and in turn enabled greater interoperation?

 

Probably, the right way to get there is to iterate over a broad set of use cases as suggested by Alan Rector.

 

Another aspect of best practices is the ontology building process, an excellent example being the flowchart you refer to in slide 19 below.

 

The problems above can be a big problem if more than one ontology is required that have overlapping domains.  These inconsistencies need to be disambiguated or reconciled in some way, and in doing so, you may invalidate mappings made to data model elements.

[VK] Can we have some concrete examples of the types of things you are referring to the above. What are the types of inconsistencies observed? How can they be reconciled? How does this result in invalidating some

of the mappings.

A concrete use case illustrating this will help us do a better job of creating ontologies and mappings for the Parkinson’s Disease use Case…

 

Don't get me wrong.  It may sound like I'm slamming GO here.  I'm not.  I've great admiration and respect for their experience and their efforts.  Most commendable of all is the fact that they've gradually learned a lot - both from the school of hard knocks and from the C.S., library science, and ontological philosophy fields that can helped them to develop better shared practices both for ontology creation, curation, and use.  To my mind, the 25 slides prepared by Suzi Lewis & Michael Ashburner (slides 11 - 37) in the following presentation summing up the hard won lessons of the GO Consortium do a better job at establishing such guidelines than anything else I've seen anywhere:

 

 

I'd specifically draw attention to the procedural flowchart on slide 19 as being an excellent foundation from which to build more specific guidelines, some of which can be culled from other portions of the presentation. 

[VK] The flowchart on slide 19 is very valuable. It will be great to understand how having a foundational ontology can help facilitate the process described in the flow chart?

 

There have also been many successes that have made it very clear, ontologies are an extremely important part of biomedical KE/KR/KD and semantically-driven data federation.

[VK] Would be great to post examples of these successes on the Wiki, Success stories will be very important to all of us to learn from.

 

Regards,

 

---Vipul


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