Re: URIs

Another fantastic citation worth it's weight in gold and definitely  
relevant to the long-term goal here of creating an algorithmic means  
to express - and then operate on - biomedical knowledge!  Many  
thanks, Bob.  I've already passed on your "hedging" reference to  
several other colleagues.

Having said that, I do *strongly* feel its very important to make a  
distinction between linguistic analysis and ontology development.   
The two are very different animals - albeit with intricately  
overlapping interests in the nature of how one expresses semantic  
information for both humans and machines.  To be to hasty to cast  
these resources all as woefully imperfect, is to throw out the baby  
with the bath water - when it comes to producing accepted practices  
and tools to support machine processing of semantic content.

The problem as I see it if you convolve the two is they come with  
significantly different a priori assumptions and expectations for  
their use.  When one looks at an analysis of the lexicon used in a  
specific research article to describe a specific statement - the  
equivalent of which can be expressed as an RDF triplet - one needs to  
know the implied, allowable PROCESSING (as Xiaoshu has been  
stressing) for such a triplet is very different than when one has a  
triplet expression derived from a "universal" relationship  
represented in an ontology.

This is the critical point I've been trying to make throughout this  
thread - and other related threads on the BIOONT task force.

This distinction may be less evident and/or relevant when you are  
just trying to communicate this information to another human being,  
but when the goal is a provide it in a useful way to a machine  
algorithm that is expected to compute on it in some useful way, the  
distinction is critical.

When folks are first coming to understand the nature of the various  
efforts that have been underway for at least 60 years to come up with  
more formal means to represent the SHARED concepts biomedical  
scientists intend to evoke when they are communicating their findings  
- and theories - with others - it can be a helpful aid to cast these  
lexical resources and complex ontologies as lying along a continuum  
of semantically-oriented resources.  As Bob is implying in this post,  
they lie much closer together than most would like to think and as a  
collective don't do a particularly good job at deterministically  
expressing the subtle, fine-granularity of meaning either embedded in  
or implied by specific scientific communications.  They also both  
bring with them many problematic tasks, if the goal is to keep the  
terminology or ontology consistent with the "bleeding edge" of  
knowledge in every micro-domain of a scientific field.  These facts I  
most definitely agree with.

I would say there is quite a bit of semantically-oriented data  
management & knowledge mining "low hanging fruit" available, IF one  
keeps the distinction between these two types of knowledge resource  
separate.

Again - lexical/linguistic resources are constructed in very  
different ways and bring with them a very different set of a priori  
assumptions and ultimate goals than the development of formally, well  
founded ontological frameworks cast in the Leibnitzian realm of  
providing a means to compute on "meaning".  I would also stress that  
compute on meaning does not only mean to interpret and re-combine  
complex logical assertions (1st or 2nd order).  These tools can also  
be used to great effect to look for gaps and inconsistencies in the  
semantic graph you are constructing - the bread & butter maintenance  
tasks groups like the Gene Ontology Consortium need to automate as  
much as possible.  This is certainly true for the work we are doing  
in on the BIRN Ontology Task Force.

I would also add that it is usually the case when mapping out new  
domains to include in an ontology, you typicall start with an  
analysis of the lexicon in that domain - by collecting and analyzing  
the terms used by scientists in that field - down to deep levels of  
granularity.  You then go about the task of organizing the lexical  
relations, as a means to come up with a more complete and consistent  
representation of this lexicon - and all of the lexical variants and  
inter-relations (e.g., synonyms, homographic homonyms [a particularly  
nasty beast for algorithms to disambiguate], meronym-holonym pairs,  
hyponym-hypernym pairs, etc.).  if one goes about this task informed  
by the extensive work from the fields of linguistics and psychology  
of language (think - WordNet - as just one example), then the task of  
using this lexical framework as an outline for an ontology is made  
ever more accessible.  In this context, analyzing the biomedical  
lexicon and moving toward ontological frameworks - I would say way  
too little attention has been paid to the issues Bob has been  
referring us to.  I think a great deal could be gained from  
incorporating an understanding of these complex issues - hedging and  
the limits of knowledge representation - into the process.

I do think, however, that should following harvesting the "low  
hanging fruit".

Just my $0.02.

Cheers,
Bill



On Jun 19, 2006, at 5:11 PM, Bob Futrelle wrote:

> I would suggest that both natural language *and* ontologies are views
> of (possibly shallow) underlying knowledge.  This knowledge is
> difficult to characterize.  It is also difficult to achieve agreement
> on it within or across communities.
>
> I find the following study sobering.  Don't be misled by the term
> "folk".  Today's science is tomorrow's folk science.
>
> - Bob Futrelle
> ---------------------------------------------------------------------- 
> -
>
> Abstract
> Cognitive Science: A Multidisciplinary Journal
> 2002, Vol. 26, No. 5, Pages 521-562
> (doi:10.1207/s15516709cog2605_1)
>
> The misunderstood limits of folk science: an illusion of  
> explanatory depth
>
> Leonid Rozenblit​‌ - Department of Psychology, Yale University
> Frank Keil​‌ - Department of Psychology, Yale University
>
> People feel they understand complex phenomena with far greater
> precision, coherence, and depth than they really do; they are subject
> to an illusion—an illusion of explanatory depth. The illusion is far
> stronger for explanatory knowledge than many other kinds of knowledge,
> such as that for facts, procedures or narratives. The illusion for
> explanatory knowledge is most robust where the environment supports
> real-time explanations with visible mechanisms. We demonstrate the
> illusion of depth with explanatory knowledge in Studies 1–6. Then we
> show differences in overconfidence about knowledge across different
> knowledge domains in Studies 7–10. Finally, we explore the  
> mechanisms
> behind the initial confidence and behind overconfidence in Studies 11
> and 12, and discuss the implications of our findings for the roles of
> intuitive theories in concepts and cognition. (c) 2002 Leonid
> Rozenblit. Published by Cognitive Science Society, Inc. All rights
> reserved.

Bill Bug
Senior Analyst/Ontological Engineer

Laboratory for Bioimaging  & Anatomical Informatics
www.neuroterrain.org
Department of Neurobiology & Anatomy
Drexel University College of Medicine
2900 Queen Lane
Philadelphia, PA    19129
215 991 8430 (ph)
610 457 0443 (mobile)
215 843 9367 (fax)


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Received on Wednesday, 21 June 2006 16:43:48 UTC