RE: [HCLS] Bridging Ontology...An Automated Approach?

Sorry for the delay in following up. This kind of discussion is key to getting
at the real value proposition of SW specifications.

I think one key realization is that SW specifications by themselves are not the
answer. It should be accompanied

by appropriate architectures (e.g., mediation with a mapping/transformation
layer), separation of concerns (e.g., lexical as opposed to semantic), etc.

 

In performing the cross-model mapping, at some point, one needs to specify the
semantic relatedness of the attributes in the entities being inter-related.  For
example, if in one data model you are storing the descriptions about the shape
organ sub-regions and descriptions about the shape of cells in the same entity
called "biomaterial_shape" with attributes "biomaterial entity" and "shape",
while in another source data model these are stored in separate entities called
"organ_subregion_shape" and "cell_shape", these two different models need to map
in different ways into the "global" mediator schema, and the ways will mainly
differ based on the semantic means used to specify different levels of
anatomical resolution.

 

[VK] There has been tons of literature in the database field that explores the
use of some variant of SQL/relational algebra to represent these mappings. 

      The key question is how can SW specifications like RDF/OWL help? 

     So based on the example given above. We need to specify the following
mappings:

-       DB1.biomaterial_shape.biomaterial_entity <=> DB2.organ_subregion_shape

-       DB1.biomaterial_shape.shape <=>DB2.cell_shape

Please explain why the above mappings are better represented in OWL/RDF as
opposed to some variant of relational algebra?

How will semantic specifications take care of different levels of anatomical
resolution?

 

May be a further drill down on this will help us isolate the value
proposition...

	In some cases (e.g., TAMBIS), the mediating model is actually an
ontology, but this is rare.  

The mappings themselves are expressed in a language with sophisticated
transformative capabilities because of the myriad incompatibilities that exist.

[BB] Absolutely.  These languages are very powerful, but many of the
transformational grammars require mixing lexical manipulations and semantic
manipulations, as is quite common when using UMLS.  This again can introduce
inconsistencies in interpretation, as you attempt to build on the first level of
semantic correlations you derive from the mappings.

[VK] I like this separation of concerns between the "lexical" and the
"semantic". The key is to clearly define and identify these type of mappings.

 For example, aggregation is not supported by many rule or ontology languages.

[BB] Again, I completely agree, aggregation is not necessarily supported by some
ontological structures, though lexically-oriented graphs are quite good at
dealing with aggregations.  The Wordnet SynSets are a good example of this.
TopicMaps (XTM) also have some advantages to provide in this arena.  These
derive from the field of text summarization and set theoretical approaches to
knowledge organization, which can differ somewhat from the graph theory based
methods used in the XML world and often used in ontological formalisms.  That's
why it can be valuable to keep lexical and semantic formalisms distinct, though
the must definitely interoperate.

 

[VK] I am not sure what is meant by "aggregation"?

      Two simple types of aggregation are:

-       tuple based aggregation (as in fields of a class definition)

-       set based aggregation 

There are ways of expressing these constructs in both SQL and other XML, RDF,
OWL types of languages...

 

More complex notions of aggregation, as in "part-of" relationships have not been
precisely defined in the first place.

However there has been work in FOMA to specify the semantics and appropriate
languages (frame based and DLs) have been used

to precisely represent these relationships...

 

I guess the issue of aggregation is orthogonal to the notion of semantic and
lexical...

 Neither are regular expressions.

[BB]: Regex's are an extremely valuable tool in the lexical analysis toolkit,
but they have nothing to do with ontologies.  One would hope the results of a
Regex-based analysis would be linked to an ontology, but if things are
constructed in the proper manner, there is no need for Regex-capability within
the ontological framework.

[VK] I agree with this characterization completely. The goal of lexical analysis
should be to "slot" various data objects into ontological elements and
categories.

 

Cheers,

 

---Vipul

 

________________________________

From: Kashyap, Vipul [mailto:VKASHYAP1@PARTNERS.ORG] 
Sent: Tuesday, August 22, 2006 8:57 AM
To: William Bug; Mork, Peter D.S.
Cc: donald.doherty@brainstage.com; public-semweb-lifesci@w3.org
Subject: RE: [HCLS] Bridging Ontology...An Automated Approach?

 

We are using mediation technology within the BIRN project as well.  It has many
ways in which it can solve some of the problems we're discussing.

 

My fear is every new repository requires an new mapping/registration to the
mediator and/or shared ontology.  This can be a very fragile system over time,
and - as you point out, Peter, tacit assumptions in the source data models (and
their use of semantics) - which of course can also change - may greatly limit
the depth and breadth with which queries can be mapped to all the data sources.

 

Semantic web approaches appear to circumvent some of the technical frailties,
though I don't think they can necessarily overcome the requirement for shared
foundational and generic ontology layers.

 

[VK] It will be great if you could give some use cases on how Semantic web
approaches can help circumvent the fragility of the system, requiring mappings
and registrations...

 

 

On Aug 22, 2006, at 7:52 AM, Mork, Peter D.S. wrote:

 

 

		Creating explicit connections between all similar and/or
identical

	entries

		in two schemas is an arduous task that is impractical to do

manually.

	 

	[VK] Will mapping each of these schemas to an ontology and then using

the

	ontology to mediate further queries help alleviate the problem?

	 

	---Vipul

	 

	 

 

This is the approach adopted by caBIG.  Individual data models (or

schemata) are related to a common ontology.  By itself, this doesn't

allow one to rewrite queries (because of tacit assumptions present in

the respective data models?), but the hope is that development of

query-mediators will be facilitated by the existence of a loose

mapping.

 

Peter

 

 

Bill Bug

Senior Research 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)

 

 

Please Note: I now have a new email - William.Bug@DrexelMed.edu
<mailto:William.Bug@DrexelMed.edu> 

 

 

 

 

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Bill Bug

Senior Research 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)

 

 

Please Note: I now have a new email - William.Bug@DrexelMed.edu

 

 





 



This email and any accompanying attachments are confidential. 
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