RE: wiki page for semantic web ROI

OK, so how about making up a plausible and specific story -- perhaps
adding onto this one -- and attach $'s to it somehow?
 
What the heck is a nonlinear reduction formula?

________________________________

From: helen.chen@agfa.com [mailto:helen.chen@agfa.com] 
Sent: Thursday, April 20, 2006 1:28 PM
To: RogerCutler@chevron.com
Cc: eneumann@teranode.com; public-semweb-lifesci@w3.org;
public-semweb-lifesci-request@w3.org
Subject: RE: wiki page for semantic web ROI



Hi, Roger 

Good story. 

I would like to "dream" about one more important benefit that might come
out of semantic web: the re-usable knowledgebase. 

In your calculation, "Supposing that a large company might have fifty
such issues to research per year, we find a $5.5M expense being reduced
to $0.5M, a cost savings of $5M for a single company".   

Most likely, this company's subject matters are concerned with a certain
domain, thus the domain knowledge is expressed in RDF, OWL and some
rules.  Many facts and relationship developed to analyze one issue will
be reused when looking into other issues, and best still, be published
on the web for others to use them.   

Ideally, the cost saving will be calculated with a nonlinear reduction
formula. 

Helen   
http://www.agfa.com/w3c/hchen




"Cutler, Roger (RogerCutler)" <RogerCutler@chevron.com> 
Sent by: public-semweb-lifesci-request@w3.org 

04/20/2006 01:36 PM 

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eneumann@teranode.com, public-semweb-lifesci@w3.org 
cc
Subject
RE: wiki page for semantic web ROI

	






It seems to me that Davide's case study can pretty easily be made into
an ROI story titled something like "Computer Aided Literature Searches".
I'm obviously not the one to do this for real, since I don't know enough
about the subject matter to talk about it convincingly, but let me take
a crack at some verbiage that may be all wrong in detail:

A case study from L&C (Language and Computing) illustrates the cost
savings and new capabilities that can result from combining NLP (Natural
Language Processing) with Semantic Web reasoning capabilities.  It seems
that some entirely unexpected side effects occurred during clinical
trials of an anti-diabetic drug, and this small company took on the task
of trying to track down the mechanism of the drug interaction.  They ran
8800 journal articles through their NLP process, deriving thousands of
relations like "enzyme X may activate protein Y" out of this literature
and combining the resulting ontology with information from relational
databases and other public sources.  They analysed the results with a
Semantic Web inference engine using queries like "what paths connect
'PKC Beta inhibitor' and 'Diabetes'", resulting in a plausible
explanation as to the previously unknown mechanism causing the effect.
Taking the specifics of this case study as representing a typical
example of what might be expected from computer aided literature
searches, the above results were obtained in a "couple of days".  Let's
assume that a few people were working on it and that the cost was about
10 man-days.  The alternative would be to examine all the journal
articles by hand and make database queries as appropriate to supplement
that information.  Taking this at face value, perhaps an expert person
could "process" ten journal articles per hour, which for 8800 journal
articles translates to 110 man-days.  On the face of it, the computer
aided process has ten times less cost.  There is also the possibility
that the computerized process could bring to light relationships that a
person would miss, and if the number of journal articles were much
greater perhaps this "possibility" would become highly probable -- but
it is very difficult to quantify the value of this potential benefit so
we will pass over it in this analysis.  Guessing that the total cost for
the services of an expert in a major industry is about $1000/day, we
have a cost comparison for this operation of $10K vs $110K.  Supposing
that a large company might have fifty such issues to research per year,
we find a $5.5M expense being reduced to $0.5M, a cost savings of $5M
for a single company.  Replicating this to, say, ten companies involved
in this industry yields a cost savings from this one type of application
of $50 million.

Note that in this example I have tried to include the following
elements:

1 - A specific example in which Semantic Web technology is used to solve
a specific problem, and a plausible technical indication of how that
specific example works.

2 - A cost analysis for this example of the SW technique and ALSO a cost
analysis of the likely alternative if SW is not available.

3 - An analysis of how this might be replicated and scaled out to
provide total cost savings.

Displaying the pieces of this analysis in detail allows a skeptical
reader to substitute his or her own guesses for each atomic part of the
analysis and also perhaps to make some sort of quantitive estimate of
the reliability of the guesswork.

-----Original Message-----
From: public-semweb-lifesci-request@w3.org
[mailto:public-semweb-lifesci-request@w3.org] On Behalf Of Eric Neumann
Sent: Wednesday, April 19, 2006 10:54 PM
To: public-semweb-lifesci@w3.org
Subject: wiki page for semantic web ROI



I have added a wiki page to begin the discussion (as well as collect
different anecdotes and examples) into the various kinds of return of
investment (ROI) that could be realized by applying semantic web
technologies:

      http://esw.w3.org/topic/HCLSIG/ROI

Any additional examples people would like to include and discuss should
be added to the list of examples on this page.

Although areas of potential benefits are identified and presented in
economic terms, estimates of how much the semantic web could improve
these situations do not yet exist. It is hoped that one goal of this
discussion will help put into context the potential value of some of the
current HCLS tasks. Working demonstrations will allow us to begin
estimating how much of an impact (e.g., reducing development and data
design time and costs) these approaches will have in the various HCLS
areas.

Eric