W3C home > Mailing lists > Public > public-semweb-lifesci@w3.org > April 2006

Re: Ontology editor + why RDF?

From: M. Scott Marshall <marshall@science.uva.nl>
Date: Wed, 19 Apr 2006 23:24:13 +0200
Message-ID: <4446AA7D.1000109@science.uva.nl>
To: "deWaard, Anita (ELS)" <A.dewaard@elsevier.com>
Cc: public-semweb-lifesci@w3.org

deWaard, Anita (ELS) wrote:
> A quick question that I was hoping this forum might have some thoughts 
> on: we are looking for a new editing tool for our life science thesaurus 
> EMTREE (proprietary, multi-facted polyhierarchical, 260 k terms (50 k 
> preferred, 210 k+ synonyms), > 10,000 nodes) and I am trying to convince 
> the thesaurus department to go to an RDF-based editor. I was wondering 
> if anyone had any thoughts on
> a- the best professional-grade ontology editor to use (serious 
> alternatives to Protege?), and
> b- the best arguments to convince my company to start using RDF, both 
> internally and externally.

I would like to address b) i.e. the WHY question.

There are several benefits to a semantic web approach:

1) Interoperability and reuse: The use of RDF should increase 
interoperability and reuse within your company. Once your data/knowledge 
is in RDF/OWL, a steadily growing number of tools are available to 
query, manipulate, browse, and visualize it. In the 'internal use' 
scenario, the use of standards that bring "interoperability" can result 
in a common vocabulary for implementers, architects, and domain experts 
within the company - this is already quite something!

2) Knowledge capture: semantic web tools are self-documenting in the 
sense that you are able to 'look up' the semantics of both data and 
queries. Semantic web can expose precisely the sort of semantics that 
are often 'locked up' in the code of a programming language. For 
example, some queries can be coded in a programming language for speed 
but readability is dramatically reduced relative to SPARQL.

[Note that 1) and 2) can make personnel changes less traumatic - exposed 
semantics simplifies reengineering and reuse.]

3) Reasoning: Reasoners can leverage the inherent semantics in a query, 
for example, by 'expanding' and 'contracting' queries for you, making 
use of background knowledge that is often too difficult to include in 
the query itself.

4) Dissemination = robustness?: If your thesaurus is made public in an 
RDF format, it will be used and referred to more frequently than if it 
remains proprietary. Suggestions for improvements can then come from 
outside as well as inside the company (as long as your company provides 
a way to channel such information).

5) Clarification from formalization: I believe that the process of 
formalization used to build an ontology can clarify murky issues and 
improve the semantic models themselves. In the case of the life 
sciences, the semantic models are often implicit in the text of a 
document, or worse, in a researcher's brain. If semantic models can be 
dislosed by choosing/defining the terms to describe a scientific 
experiment, for example, it can potentially *expose* the often implicit 
assumptions that are necessary for the experiment to succeed.


p.s. The term 'semantic web' seems to mean different things to different 
people. It reminds me of people using 'AI' to refer to (all or some of): 
rule-based systems, logic, knowledge representation, machine learning, 
theorem provers, game players, scheduling algorithms, natural language 
processing, machine intelligence, machine consciousness (!?), etc.

M. Scott Marshall
Received on Wednesday, 19 April 2006 21:24:18 UTC

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