Hi All,
I frequently see genes, transcripts, dna and mrna and their
sequences, proteins, protein sequences, transcripts, and peptides
all confusedly identified by overlapping identifiers. I don't
see how
any identifier scheme, in itself, lsid's included, currently fixes
this problem. It is this problem that I personally want to see
progress on.
You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and despite
the confusion seem to make their way through it.
cheers,
Michael
Michael Miller
Lead Software Developer
Rosetta Biosoftware Business Unit
www.rosettabio.com
Message
From: public-semweb-lifesci-request (AT) w3 (DOT) org
[@w3.org] Behalf
Alan Ruttenberg
Sent: Sunday, July 30, 2006 7:08 PM
To: Mark Wilkinson
Cc: Alan Ruttenberg; public-semweb-lifesci (AT) w3 (DOT) org;
noah_mendelsohn (AT) us (DOT) ibm.com; Sean Martin; Henry S. Thompson;
Phillip Lord; www-tag (AT) w3 (DOT) org; Dan Connolly
Subject: Re: A precedent suggesting a compromise for the
SWHCLS IG Best Practices (ARK)
Excellent response! I 95% heartedly agree (all but the "I stand by
LSIDS part" :)
I will note however that whenever there are versions of something,
there tends to some concept of the thing that they are versions of.
So even though there are versions of the sequence, there ought to
still be some thing which represents the thing that all the versions
are of.
Back to your point, is there anyone out there who has minted LSIDs
for genes and for the sequences distinctly and related them? Do the
gene LSIDs ever get versions? Do the sequence LSIDs ever not have
versions? When there are different authorities for the genes and
sequences, what are the relations that people use to relate them?
Let's put these examples on the table.
If any one has done this in the context of NCBI databases in
particular I think it would be helpful to share the specifics of how
these ids were used and conceptualized.
My experience has been that there is routine confusion of the sort
that you describe throughout the life sciences community and that
this bleeds into the discussion of identifiers (as it just did,
though I have to admit I was baiting for exactly this discussion :)
I frequently see genes, transcripts, dna and mrna and their
sequences, proteins, protein sequences, transcripts, and peptides
all confusedly identified by overlapping identifiers. I don't
see how
any identifier scheme, in itself, lsid's included, currently fixes
this problem. It is this problem that I personally want to see
progress on.
LSID's contract seems more to do with persistence, mutability,
cacheability, and discoverability of byte sequences - not around
issues of the identifiers and their relations making
ontological sense.
While I understand that in some contexts the issues around data
management are central, they aren't in all contexts. Because I think
that optimization of the data management issues, while in some ways
elegantly handled by the LSID protocol, aren't central to the issue
of representation in the life sciences, and because I don't see LSID
addressing the representation issues, I worry that imposing the use
of the LSID protocol puts a burden on all, for the benefit of
relatively few. And for those relatively few who are going
to go out
of their way to have internal copies of data and the like, I don't
see why a custom system that is circumvents http for efficiency
reasons is too much of a burden.
How do you see things otherwise?
-Alan
(Being deliberately provocative here - my assigned role in this
debate :)
Jul 30, 2006, at 9:06 PM, Mark Wilkinson wrote:
Sun, 30 Jul 2006 16:46:21 -0700, Alan Ruttenberg
<alanruttenberg (AT) gmail (DOT) comwrote:
I may be speaking out-of-turn here, and should probably let Sean
answer this one since he may have (no doubt) thought-through it
more deeply than I have; however I think you may be mixing up
several different entities here (as so often happens in a URL
world ;-) )
In the case you cite above you are likely talking about a "gene",
not a "sequence". A "gene" will have its own LSID, and it
is (even
by the strict genetic definition) a conceptual entity defined by
complementation. A "gene" and its "sequence" are not the same
thing! So I don't see a problem. When you need to
refer to the
gene in the abstract, you can refer to the gene's LSID. When you
need to talk about a concrete sequence, you refer to *it's* LSID.
The metadata of the gene will (in a sensible world) include
triples
that describe its possible sequences, and these will have versions.
Genes have many many many properties, so we cannot munge them all
into "sequence". Certainly, this is how we are modelling our data
locally
I stand by LSID's :-)
Mark

Quoting "Miller, Michael D (Rosetta)" <Michael_Miller (AT) Rosettabio (DOT) com>:

You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and despite
the confusion seem to make their way through it.

The problem is that semantic web is intended to make machine to
understand. And
the clarity is a prerequisite to instruct machine unambigously.

Xiaoshu

Quoting "Miller, Michael D (Rosetta)" <Michael_Miller (AT) Rosettabio (DOT) com>:

You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and despite
the confusion seem to make their way through it.

The problem is that semantic web is intended to make machine to
understand. And
the clarity is a prerequisite to instruct machine unambigously.

Xiaoshu

Drew McDermott wrote:


>
>>[wangxiao (AT) musc (DOT) edu]
>>
>>Quoting "Miller, Michael D (Rosetta)" <Michael_Miller (AT) Rosettabio (DOT) com>:
>>
>
>>
You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and despite
the confusion seem to make their way through it.


>>The problem is that semantic web is intended to make machine to
>>understand. And
>>the clarity is a prerequisite to instruct machine unambigously.
>
>>
>
>Naming genes is an interesting case where proper names shade into
>generic names. However, I think on balance genes tend to have so many
>idiosyncratic properties that their names are never going to fit into
>a systematic naming scheme very well. But remember, the key
>contribution of the semantic-web methodology is to use URIs as names
period. So long as a URI means only one gene, and everyone agrees
>what gene it means, there is no ambiguity problem. It's also a good
>idea to avoid having more than one name for a gene, but multiple names
>do not constitute ambiguity, merely inefficiency.
>

Hi Drew et al.,

I agree that gene names are interesting use cases for URI/LSID. In
addition to synonyms (different terms may be used to refer to the same
concept), we need to deal with homonyms (the same term may mean
different things). As discussed in the BioRDF call yesterday, I promised
to come up with some neuroscience examples for URI/LSID, here is one
such example for looking up the definition of "spine" in wikipedia.
Notice that this term has different meanings in different contexts
(biological vs. anatomical). It looks like we might want to think about
the possibility of providing such a context in LSID for disambiguation.

(biology)

(anatomy),

Cheers,
-Kei

Yes, indeed. Machine processing of information relies on
consistent usage of terms. You can't reuse information for
new problems when its use requires human intervention to disambiguate
it.

Tim Berners-Lee

Aug 10, 2006, at 21:54, wangxiao (AT) musc (DOT) edu wrote:

Quoting "Miller, Michael D (Rosetta)" <Michael_Miller (AT) Rosettabio (DOT) com>:
>
>You're correct here but it is the state of the art. Interestingly
>enough, I've found that in general the biology-based scientists and
>investigators are not all that bothered by this confusion and despite
>the confusion seem to make their way through it.
>
The problem is that semantic web is intended to make machine to
understand. And
the clarity is a prerequisite to instruct machine unambigously.

Xiaoshu

Yes, indeed. Machine processing of information relies on
consistent usage of terms. You can't reuse information for
new problems when its use requires human intervention to disambiguate
it.

Tim Berners-Lee

Aug 10, 2006, at 21:54, wangxiao (AT) musc (DOT) edu wrote:

Quoting "Miller, Michael D (Rosetta)" <Michael_Miller (AT) Rosettabio (DOT) com>:
>
>You're correct here but it is the state of the art. Interestingly
>enough, I've found that in general the biology-based scientists and
>investigators are not all that bothered by this confusion and despite
>the confusion seem to make their way through it.
>
The problem is that semantic web is intended to make machine to
understand. And
the clarity is a prerequisite to instruct machine unambigously.

Xiaoshu

Tim --
At 10:54 AM 8/21/2006 -0400, you wrote:

>Machine processing of information relies on
>consistent usage of terms. You can't reuse information for
>new problems when its use requires human intervention to disambiguate
>it.

But, perhaps ontologies can help in mapping various human usages to one
another in such a way that the machines can then work uninterrupted?

A little example in this direction that you can run in a browser is

Does anyone have a pointer please to a similar task in biology / health
sciences?

Thanks, -- Adrian

Internet Business Logic (R)
Executable open vocabulary English
at www.reengineeringllc.com
Shared use is free

Adrian Walker
Reengineering
P Box 1412
Bristol
CT 06011-1412 USA

Phone: USA 860 583 9677
Cell: USA 860 830 2085
Fax: USA 860 314 1029

I agree, consistent use of terms makes life easier for machines and for
humans too when the terms have been agreed on, learned, and understood.
Unfortunately, this takes a lot of effort and dedication from the
humans. Learning a whole ontology before anything can be done is a bit
like reading the whole manual of a DVD player before one can use that.
And we all know that while there are people who actually read the whole
manual, they are a minority.

As a usability person I always like to see the machines support the
humans as much as possible and not vice versa.
In my view, new inventions often start from not so great terms and
evolve stepwise as learning happens. terms are first shared and
polished in small groups and later links are made between groups that
may use different terminologies for similar things. If we want to
support humans doing inventions I think we should support the use of
different terms, their evolution, and making connections between similar
terms when they are discovered as much as possible. And I think Semantic
Web is great for that.

Marja

Tim Berners-Lee wrote:

Yes, indeed. Machine processing of information relies on
consistent usage of terms. You can't reuse information for
new problems when its use requires human intervention to disambiguate
it.

Tim Berners-Lee

Aug 10, 2006, at 21:54, wangxiao (AT) musc (DOT) edu wrote:
>
>>
>Quoting "Miller, Michael D (Rosetta)" <Michael_Miller (AT) Rosettabio (DOT) com>:
>>
You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and despite
the confusion seem to make their way through it.
>>
>>
>The problem is that semantic web is intended to make machine to
>understand. And
>the clarity is a prerequisite to instruct machine unambigously.
>>
>Xiaoshu
>>
>
>

I agree, consistent use of terms makes life easier for machines and for
humans too when the terms have been agreed on, learned, and understood.
Unfortunately, this takes a lot of effort and dedication from the
humans. Learning a whole ontology before anything can be done is a bit
like reading the whole manual of a DVD player before one can use that.
And we all know that while there are people who actually read the whole
manual, they are a minority.

As a usability person I always like to see the machines support the
humans as much as possible and not vice versa.
In my view, new inventions often start from not so great terms and
evolve stepwise as learning happens. terms are first shared and
polished in small groups and later links are made between groups that
may use different terminologies for similar things. If we want to
support humans doing inventions I think we should support the use of
different terms, their evolution, and making connections between similar
terms when they are discovered as much as possible. And I think Semantic
Web is great for that.

Marja

Tim Berners-Lee wrote:

Yes, indeed. Machine processing of information relies on
consistent usage of terms. You can't reuse information for
new problems when its use requires human intervention to disambiguate
it.

Tim Berners-Lee

Aug 10, 2006, at 21:54, wangxiao (AT) musc (DOT) edu wrote:
>
>>
>Quoting "Miller, Michael D (Rosetta)" <Michael_Miller (AT) Rosettabio (DOT) com>:
>>
You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and despite
the confusion seem to make their way through it.
>>
>>
>The problem is that semantic web is intended to make machine to
>understand. And
>the clarity is a prerequisite to instruct machine unambigously.
>>
>Xiaoshu
>>
>
>

Hi All,

There are examples of systems that strive to separate the lexicon
from the ontology, so as to ensure one particular lexical view of the
underlying semantics doesn't "lock out" either humans or machines who
do not "understand" that lexicon. Few are perfect, but many have
effectively handled the issue of semantic interoperability, though
often not at the level of semantic granularity required by experts at
the bleeding edge of a specific scientific field.

An ontology is of little use to anyone - person or machine - without
instantiating it via a lexicon. Where very significant problems
arise is when the lexicon is confused with the universals the
ontology is intended to formally represent. I realize this boundary
may appear artificial to some, but those who've worked on such issues
for decades in the library & info sciences and in computational
linguistics - despite some disagreement at the edges - will generally
see this boundary as useful - even if they agree to disagree on
whether it is in fact an artifact of human linguistic expression or a
more fundamental expression of a sort of Heisenberg Uncertainty
principle of KE/KR/KD. What I mean is the moment an algorithm tries
to compute on an ontological expression in the context of specific
data instances - whether the algorithm resides in silico or in a
human brain - it "breaks" the universal nature of the principles and
grounds it in a lexicon used to address the specific existential
instances being manipulated within the domain of a specific
application. I believe this issue is at the heart of some
significant confusion regarding what an ontology is and the tasks it
can help to implement.

An effective and practical knowledge resource needs to include both
ontological graphs and a complex lexical repository.

I think where "ontology" construction often goes wrong is when it is
not EXPLICIT and - of equal importance- quite SYSTEMATIC regarding
the lexical extensions it includes - e.g., abbreviations,
misspellings, various types of synonyms, homographic homonyms (the
bane of NLP efforts everywhere), etc

I was just listening to Michio Kaku discussing the recent controversy
regarding the redefinition of "planet" status. As he and the
astronomer Ken Croswell were discussing the issue, Dr. Kaku brought
up the story from Richard Feinmann's biography regarding the
difference between "naming" an entity and studying the fundamental
properties and rules relating the continuum of entities in the
physical world. Both the naming and the formalisms for
characterizing the fundamentals are human artifacts - BUT what
separates the naming from the expression of universals is the latter
is guided by our increasing level of insight and understanding of
real-world entities and the ways in which they relate to one
another. No such criterion exists for the naming process, and this
is why it is extremely helpful to keep the lexicon characterizing
these names distinct from the expression of fundamentals (the
ontologies). This is also an issue addressed by Gottfried W. von
Leibniz in his philosophical works which all derived from the insight
he had as a child that it MIGHT be possible to create a computable
formalism for ontological entities analogous to the system created by
mathematicians for performing axiomatic proofs in geometry. In MANY
ways, our efforts here date back to this work by Leibniz via several,
related historical threads in mathematics, philosophy, and various
computationally-oriented scientific fields.

other general point - obviously the strategies and "best
practices" for addressing these issues in the context of existing
(and historical) data records including the literature are somewhat
different, as opposed to what we hope to see researchers doing going
forward. In an ideal world - say 10 years form now - we can hope to
see publication mechanisms in place both for primary data, supporting
, and the larger world of the
scientific literature - systems such as SWAN and some of the more
advanced systems in development at BioMed Central and PLoS - to help
reduce the complexity of the lexical Babel-esque landscape we must
currently contend with. This needs to be done in a manner that
doesn't in any way restrict the expressiveness of lexicon or the
onotological foundations, while also being implemented in a highly
intuitive manner not requiring the researcher learn a complex formal
means to express themselves beyond the existing complexity typically
used amongst domain experts. This is why I'd still place this 10
years out. I don't think that's too optimistic a duration, however,
given some of the revolutionary changes being introduced both by the
SWTech C.S. community, as well as by the community of researchers
embedded in the increasingly less messy process of biomedical
ontology development and use. Some of these more modern scientific
publication systems will come on line much sooner than this, but
probably only in restricted contexts where there is a centralized
authority that can both provide technical resources to develop,
support, and evolve the systems, as well as enforce a certain level
of compliance amongst its users - e.g., caBIG, the eScience myGRID
project, REWERSE, The MIND Center at MGH, the BIRN project, etc
For better or worse, as great a profile as these organizations
represent, the landscape of working neuroscientists extends way
beyond this privileged environment, and we all hope to see our
efforts be of use and relevant to all neuroscientists (given the
current scope of the HCLSIG hosted efforts is focussed on the
neurosciences) and the value it can help neuroscientists realize for
society at-large.

As an example of where things can go wrong when convolving the
lexicon with the ontology, take an artifact as relatively simple and
seemingly "self-evident" as the "preferred label" or "preferred term"
for a node in an ontological graph. In making the assertion
"preferred", there is the implication some person or agency has
passed judgement on the term. Reconciling two ontologies with
overlapping knowledge domains can be made unnecessarily difficult
when this implied contract is not made explicit. In other words, if
you focus on reconciling the terms rather than reconciling the
underlying semantic graphs, you can run into many unnecessary
problems. I believe this issue is related to many of the discussions
we've had on this list over the past 3 months both regarding ontology
construction and use, as well as URI uniqueness and versioning
contract. Formalisms such as SKS can be extremely helpful in this
regard, as we need to compute on the lexicon, as well as the
ontological graph.

To offer a relatively simple and ubiquitous example from neuroscience
- on one side of the pond they prefer "neurone", while on the other
"neuron" is standard term. Is one more true? Do they refer to
different, underlying fundamental entities? Can we even call the
underlying entities "fundamental" when any neuroscientist would admit
there is no neuron/neurone which has been explicitly qualified down
to the level of all it's constituent molecules**, along with their
explicit disposition in space and time?

I won't hold you at bay. I'll give you my sense of the "practical"
answers to these questions.

Is one more true?
not, since they are just lexical habits, as opposed to
fundamental differences in the view of the world.

Do they refer to different, underlying fundamental entities?
This is a harder call - and very context dependent, obviously. It
will be acutely sensitive to the level of granularity of the
information provided on the neuron/neurone. If you presented two
neuroscientists with coarse-grained data on a neuron/neurone, it is
likely they could come to agreement they both were referring to the
same fundamental entity when they named the source of that data as a
neuron/neurone.

Can we call the underlying entities "fundamental" when any
neuroscientist would admit there is no neuron/neurone which has been
explicitly qualified down to the level of all it's constituent
molecules, along with their explicit disposition through time?
What happens when you provide more detailed information regarding the
purported neuron/neurone - say sufficient detail so that the two
neuroscientists find aspects of data interpretation that are
incommensurable in the Kuhnian sense (http://plato.stanford.edu/
entries/thomas-kuhn/). Then, even if the two referred to the
biological material entity that was the source of the data as a
"neuron", they would likely not agree they were referring to the
same, underlying fundamental entity. This is not unlike the
situation described several posts below in this thread regarding a
"gene". There could be a gene X identified by gene finding algorithm
1, an "identical" gene X (in terms of the coding sequences it
contains) derived from gene finding algorithm 2, the same gene X
defined via a chromosomal walk, and finally a gene X defined via
conventional genetic complementarity or hybrid mapping. They could
all contain the same coding sequence - or the same as yet
functionally unidentified ESTs. What it comes down to here, as Mark
Wilkinson stated deep in the thread is there is much confusion
regarding what actual material entity is being referenced - or
whether a material entity is being referenced at all.

In the end, I hope what SWTech can help us do is provide a robust,
shared means to express the semantic facts about the data collected,
as well as providing a dynamic and semi-automatic means to improve
our characterization of the fundamentals - semi-automatic in the
sense of "augmentation" of human intellectual abilities along the
lines pursued by Doug Engelbart and Vanevar Bush before him. If we
can devise a technical infrastructure allowing the formal, shared,
semantic description of data to evolve toward an ever converging
sense of what the true underlying entities are, then many of the
misgivings folks have regarding the use of ontological frameworks to
formally express semantic information will very likely fade.

Cheers,
Bill

**Biophysicists who study ion-channel kinetics, protein folding
dynamics, rhodopsin-based photon detection, mitochondrial energy
transfer, etc. would probably also include quantum level formalisms
to represent the states and dynamics of atoms, electrons, and sub-
atomic particles.

Aug 22, 2006, at 3:57 PM, Marja Koivunen wrote:

I agree, consistent use of terms makes life easier for machines and
for humans too when the terms have been agreed on, learned, and
understood. Unfortunately, this takes a lot of effort and
dedication from the humans. Learning a whole ontology before
anything can be done is a bit like reading the whole manual of a
DVD player before one can use that. And we all know that while
there are people who actually read the whole manual, they are a
minority.

As a usability person I always like to see the machines support the
humans as much as possible and not vice versa.
In my view, new inventions often start from not so great terms and
evolve stepwise as learning happens. terms are first shared
and polished in small groups and later links are made between
groups that may use different terminologies for similar things. If
we want to support humans doing inventions I think we should
support the use of different terms, their evolution, and making
connections between similar terms when they are discovered as much
as possible. And I think Semantic Web is great for that.

Marja

Tim Berners-Lee wrote:
>
>>
>Yes, indeed. Machine processing of information relies on
>consistent usage of terms. You can't reuse information for
>new problems when its use requires human intervention to
>disambiguate it.
>>
>Tim Berners-Lee
>>
>Aug 10, 2006, at 21:54, wangxiao (AT) musc (DOT) edu wrote:
>>

Quoting "Miller, Michael D (Rosetta)"
<Michael_Miller (AT) Rosettabio (DOT) com>:

You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and
despite
the confusion seem to make their way through it.

The problem is that semantic web is intended to make machine to
understand. And
the clarity is a prerequisite to instruct machine unambigously.

Xiaoshu

>>
>>
>
>

Bill Bug
Senior Research Analyst/ 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)

Please Note: I now have a new email - William.Bug (AT) DrexelMed (DOT) edu

This email and any accompanying attachments are confidential.
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immediately by returning this message to the sender and delete
all copies. Thank you for your cooperation.

Hi All,

There are examples of systems that strive to separate the lexicon
from the ontology, so as to ensure one particular lexical view of the
underlying semantics doesn't "lock out" either humans or machines who
do not "understand" that lexicon. Few are perfect, but many have
effectively handled the issue of semantic interoperability, though
often not at the level of semantic granularity required by experts at
the bleeding edge of a specific scientific field.

An ontology is of little use to anyone - person or machine - without
instantiating it via a lexicon. Where very significant problems
arise is when the lexicon is confused with the universals the
ontology is intended to formally represent. I realize this boundary
may appear artificial to some, but those who've worked on such issues
for decades in the library & info sciences and in computational
linguistics - despite some disagreement at the edges - will generally
see this boundary as useful - even if they agree to disagree on
whether it is in fact an artifact of human linguistic expression or a
more fundamental expression of a sort of Heisenberg Uncertainty
principle of KE/KR/KD. What I mean is the moment an algorithm tries
to compute on an ontological expression in the context of specific
data instances - whether the algorithm resides in silico or in a
human brain - it "breaks" the universal nature of the principles and
grounds it in a lexicon used to address the specific existential
instances being manipulated within the domain of a specific
application. I believe this issue is at the heart of some
significant confusion regarding what an ontology is and the tasks it
can help to implement.

An effective and practical knowledge resource needs to include both
ontological graphs and a complex lexical repository.

I think where "ontology" construction often goes wrong is when it is
not EXPLICIT and - of equal importance- quite SYSTEMATIC regarding
the lexical extensions it includes - e.g., abbreviations,
misspellings, various types of synonyms, homographic homonyms (the
bane of NLP efforts everywhere), etc

I was just listening to Michio Kaku discussing the recent controversy
regarding the redefinition of "planet" status. As he and the
astronomer Ken Croswell were discussing the issue, Dr. Kaku brought
up the story from Richard Feinmann's biography regarding the
difference between "naming" an entity and studying the fundamental
properties and rules relating the continuum of entities in the
physical world. Both the naming and the formalisms for
characterizing the fundamentals are human artifacts - BUT what
separates the naming from the expression of universals is the latter
is guided by our increasing level of insight and understanding of
real-world entities and the ways in which they relate to one
another. No such criterion exists for the naming process, and this
is why it is extremely helpful to keep the lexicon characterizing
these names distinct from the expression of fundamentals (the
ontologies). This is also an issue addressed by Gottfried W. von
Leibniz in his philosophical works which all derived from the insight
he had as a child that it MIGHT be possible to create a computable
formalism for ontological entities analogous to the system created by
mathematicians for performing axiomatic proofs in geometry. In MANY
ways, our efforts here date back to this work by Leibniz via several,
related historical threads in mathematics, philosophy, and various
computationally-oriented scientific fields.

other general point - obviously the strategies and "best
practices" for addressing these issues in the context of existing
(and historical) data records including the literature are somewhat
different, as opposed to what we hope to see researchers doing going
forward. In an ideal world - say 10 years form now - we can hope to
see publication mechanisms in place both for primary data, supporting
, and the larger world of the
scientific literature - systems such as SWAN and some of the more
advanced systems in development at BioMed Central and PLoS - to help
reduce the complexity of the lexical Babel-esque landscape we must
currently contend with. This needs to be done in a manner that
doesn't in any way restrict the expressiveness of lexicon or the
onotological foundations, while also being implemented in a highly
intuitive manner not requiring the researcher learn a complex formal
means to express themselves beyond the existing complexity typically
used amongst domain experts. This is why I'd still place this 10
years out. I don't think that's too optimistic a duration, however,
given some of the revolutionary changes being introduced both by the
SWTech C.S. community, as well as by the community of researchers
embedded in the increasingly less messy process of biomedical
ontology development and use. Some of these more modern scientific
publication systems will come on line much sooner than this, but
probably only in restricted contexts where there is a centralized
authority that can both provide technical resources to develop,
support, and evolve the systems, as well as enforce a certain level
of compliance amongst its users - e.g., caBIG, the eScience myGRID
project, REWERSE, The MIND Center at MGH, the BIRN project, etc
For better or worse, as great a profile as these organizations
represent, the landscape of working neuroscientists extends way
beyond this privileged environment, and we all hope to see our
efforts be of use and relevant to all neuroscientists (given the
current scope of the HCLSIG hosted efforts is focussed on the
neurosciences) and the value it can help neuroscientists realize for
society at-large.

As an example of where things can go wrong when convolving the
lexicon with the ontology, take an artifact as relatively simple and
seemingly "self-evident" as the "preferred label" or "preferred term"
for a node in an ontological graph. In making the assertion
"preferred", there is the implication some person or agency has
passed judgement on the term. Reconciling two ontologies with
overlapping knowledge domains can be made unnecessarily difficult
when this implied contract is not made explicit. In other words, if
you focus on reconciling the terms rather than reconciling the
underlying semantic graphs, you can run into many unnecessary
problems. I believe this issue is related to many of the discussions
we've had on this list over the past 3 months both regarding ontology
construction and use, as well as URI uniqueness and versioning
contract. Formalisms such as SKS can be extremely helpful in this
regard, as we need to compute on the lexicon, as well as the
ontological graph.

To offer a relatively simple and ubiquitous example from neuroscience
- on one side of the pond they prefer "neurone", while on the other
"neuron" is standard term. Is one more true? Do they refer to
different, underlying fundamental entities? Can we even call the
underlying entities "fundamental" when any neuroscientist would admit
there is no neuron/neurone which has been explicitly qualified down
to the level of all it's constituent molecules**, along with their
explicit disposition in space and time?

I won't hold you at bay. I'll give you my sense of the "practical"
answers to these questions.

Is one more true?
not, since they are just lexical habits, as opposed to
fundamental differences in the view of the world.

Do they refer to different, underlying fundamental entities?
This is a harder call - and very context dependent, obviously. It
will be acutely sensitive to the level of granularity of the
information provided on the neuron/neurone. If you presented two
neuroscientists with coarse-grained data on a neuron/neurone, it is
likely they could come to agreement they both were referring to the
same fundamental entity when they named the source of that data as a
neuron/neurone.

Can we call the underlying entities "fundamental" when any
neuroscientist would admit there is no neuron/neurone which has been
explicitly qualified down to the level of all it's constituent
molecules, along with their explicit disposition through time?
What happens when you provide more detailed information regarding the
purported neuron/neurone - say sufficient detail so that the two
neuroscientists find aspects of data interpretation that are
incommensurable in the Kuhnian sense (http://plato.stanford.edu/
entries/thomas-kuhn/). Then, even if the two referred to the
biological material entity that was the source of the data as a
"neuron", they would likely not agree they were referring to the
same, underlying fundamental entity. This is not unlike the
situation described several posts below in this thread regarding a
"gene". There could be a gene X identified by gene finding algorithm
1, an "identical" gene X (in terms of the coding sequences it
contains) derived from gene finding algorithm 2, the same gene X
defined via a chromosomal walk, and finally a gene X defined via
conventional genetic complementarity or hybrid mapping. They could
all contain the same coding sequence - or the same as yet
functionally unidentified ESTs. What it comes down to here, as Mark
Wilkinson stated deep in the thread is there is much confusion
regarding what actual material entity is being referenced - or
whether a material entity is being referenced at all.

In the end, I hope what SWTech can help us do is provide a robust,
shared means to express the semantic facts about the data collected,
as well as providing a dynamic and semi-automatic means to improve
our characterization of the fundamentals - semi-automatic in the
sense of "augmentation" of human intellectual abilities along the
lines pursued by Doug Engelbart and Vanevar Bush before him. If we
can devise a technical infrastructure allowing the formal, shared,
semantic description of data to evolve toward an ever converging
sense of what the true underlying entities are, then many of the
misgivings folks have regarding the use of ontological frameworks to
formally express semantic information will very likely fade.

Cheers,
Bill

**Biophysicists who study ion-channel kinetics, protein folding
dynamics, rhodopsin-based photon detection, mitochondrial energy
transfer, etc. would probably also include quantum level formalisms
to represent the states and dynamics of atoms, electrons, and sub-
atomic particles.

Aug 22, 2006, at 3:57 PM, Marja Koivunen wrote:

I agree, consistent use of terms makes life easier for machines and
for humans too when the terms have been agreed on, learned, and
understood. Unfortunately, this takes a lot of effort and
dedication from the humans. Learning a whole ontology before
anything can be done is a bit like reading the whole manual of a
DVD player before one can use that. And we all know that while
there are people who actually read the whole manual, they are a
minority.

As a usability person I always like to see the machines support the
humans as much as possible and not vice versa.
In my view, new inventions often start from not so great terms and
evolve stepwise as learning happens. terms are first shared
and polished in small groups and later links are made between
groups that may use different terminologies for similar things. If
we want to support humans doing inventions I think we should
support the use of different terms, their evolution, and making
connections between similar terms when they are discovered as much
as possible. And I think Semantic Web is great for that.

Marja

Tim Berners-Lee wrote:
>
>>
>Yes, indeed. Machine processing of information relies on
>consistent usage of terms. You can't reuse information for
>new problems when its use requires human intervention to
>disambiguate it.
>>
>Tim Berners-Lee
>>
>Aug 10, 2006, at 21:54, wangxiao (AT) musc (DOT) edu wrote:
>>

Quoting "Miller, Michael D (Rosetta)"
<Michael_Miller (AT) Rosettabio (DOT) com>:

You're correct here but it is the state of the art. Interestingly
enough, I've found that in general the biology-based scientists and
investigators are not all that bothered by this confusion and
despite
the confusion seem to make their way through it.

The problem is that semantic web is intended to make machine to
understand. And
the clarity is a prerequisite to instruct machine unambigously.

Xiaoshu

>>
>>
>
>

Bill Bug
Senior Research Analyst/ 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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