Transcript neumann_SWLS

Semantic Web in
18-Oct-04
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Charter outline
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One page graphic
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Purpose: Mission & Vision
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Perimeter: Core competencies & activities
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Impact
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Engagement priority principles
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Operating principles with customer
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Appendix
 KM Definition
 (SWOT)
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TBD
 Organizational Design
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Industry productivity vs. investment
Total R&D investment ($ billions)
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$30
$25
$20
$ 897 million
including post-approval R&D costs
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to develop a new prescription drug
= 250% increase in a decade
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Inflation-adjusted
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Including failures
Rising clinical trial costs difficulty in recruiting patients
Expanding development
programs
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More chronic &
degenerated diseases
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Longer development times
NME
s
$35
400
300
200
$15
$10
Tufts Center, May 2003: $ 802 million excluding post-approval
R&D costs
100
$5
0
$0
1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Source: PhRMA & FDA 2003
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Note:’00-27 ‘01-24, ’02-17 NMEs 23% NME
obtain first approval, D. Kessler, H&Q
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R&D Challenges in Drug Discovery
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Increase productivity
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Improve submissions and approvals
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Reduce costs: Clinical and preclinical studies ~80% of total
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Segmented patient populations
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Complexity of the science and technologies
 Capturing the innovation and value
 Drug-hunting ability
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Knowledge creation and transfer
 Consortium & Alliances
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Facing a Technology Gap in
Drug Innovation
Need to utilize
Knowledge more
effectively
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Knowledge Networks within
Pharma that need to be supported
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Scientists and Researchers
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Regulatory (FDA)
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Industrial Operations
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Research Alliances
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Business Process and Management
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Competitive and Market Information
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Financial
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How Can Scientists Work Together Better?
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Biology
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Geneticists
Pathologists
Molecular Biologists
Cytologists
ADME
Toxicologists
Clinicians
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Informatics
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Genomicists
Functional Genomicists
BioStatisticians
Bioinformaticists
Cheminformaticists
Dynamics Modelers
DB admins
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Data
Integration
Chemistry
 Compound
Library Chemists
 HT Screening
 Medicinal Chemists
 Synthetic Chemists
 Molecular Modelers
 Rational Designers
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Information Interpretation
!
?
“The data clearly shows that the
compound series has hERG issues that
are exacerbated by its side groups”
Sharing
Simply
Can
“Which side groups?”
data is not sufficient for sharing insights
annotating findings with TEXT does not solve how to locate such insights.
researchers find different meaning in the same data?
Merge
Legacy data with newly generated
Capture
Context!
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A Major Unmet Challenge
- Recognizing Information Interpretation
Seeing the data the same way…
ℐi { I(x) } ~ ℐj { I(x) }
How can one guarantee that scientist i interprets data I(x)
the same way as j does?
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Social Participation
“[It]
refers not just to local events of engagement in certain activities with
certain people, but to a more encompassing process of being active
participants in the practices of social communities and constructing
identities in relation to these communities…. Such participation shapes
not only what we do, but also who we are and how we interpret what we
do.”
-
Etienne Wenger, 1999
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The Negotiation of Meaning
As
described by Friesen:
The
meaning of any set of terms, and the significance and utility of any
taxonomy, according to Wenger, can be evaluated only in the context of a
community whose members are involved in similar activities and share
similar values. Wenger calls this process the "negotiation of
meaning:" The production of meanings "that extend, redirect, dismiss,
reinterpret, modify or confirm… the histories of meanings of which they
are a part." (Wenger, 1999; p. 53)
Example: Functional Genomics and Pathologists
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Research Process and Knowledge Flow
Knowledge Networks
Expt
Design
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Data
Analysis
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Interpretation
Decision
Action
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Communities and Interoperability

Semantic interoperability is tied directly to communities of
practice:
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“Within a community or domain, relative homogeneity
reduces interoperability challenges. Heterogeneity
increases as one moves outside of a focal
community/domain, and interoperability is likely [to be] more
costly and difficult to achieve” Moen, 2001
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Meanings encoded with a (XML) schema, for use within
one community, are defined only implicitly.
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Databases can only be used by those who define them;
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Why a Semantic Web for
Life Science Applications?
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Improve Scientific Interactions and Exchanges
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Data Integration AND Interpretation
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Web-compatible strategies for information encoding and sharing
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Sharing Best Practices – Knowledge discovery rules
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Knowledge Agents –How can they accelerate science?
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Framework for Next Generation of the Web
Knowledge Exchange within a Semantic Web
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OWL (Ontology Web Language)
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W3C Ontology Specification
Goes beyond 1st order Logic (Frames & Descriptive Logic)
Extensible by members of any community
Structurally based on RDF
RDF (Resource Description Framework)
 Basic XML Semantic Format that OWL is based upon
 Allows users to merge and aggregate any set of related data and
relational components
 Refers to Ontologies specified in OWL
Defines
OWL
RDF
Structured
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Smarter, Searchable Annotations (Chemistry)
Free-Text
“The side chain on this compound
improves GI transport significantly”
Text found only if compound already
selected
Free-Text with Link
“As evidenced (PKID:392384), the side chain on this
compound improves GI transport significantly”
Link can be used to find all compounds
referencing it– but reason for link is unclear
RDF Statement
<side chain “#element=2”>
<improves><GI transport>
Search feasible for any side chain improving “GI
transport”, or semantically related impact
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Smarter, Searchable Annotations (Proteins)
Free-Text
“The domain on this protein regulates
catalytic activity significantly”
Text found only if compound already
selected
Free-Text with Link
“As evidenced (PKID:8832), our compound
series interact with the catalytic site”
Link can be used to find all proteins
referencing this link– but reason for link is
unclear
RDF Statement
<domain “#element=2”>
<interacts><Cmp Series XV >
Search feasible for any protein domain interacting with
“Compound Series XV”, or semantically related
binding
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Aggregation through Semantics (OWL)
PROTEIN
GENE
mRNA
CASCADE PATHWAY
MICROARRAY
EXPERIMENT
LOCALIZATION
BIO-PROCESS
INTERVENTION POINT
Data DISEASE
Sources
TARGET MODEL
DRUG
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TREATMENT
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Courtesy of
BeyondGenomics 19
New Data Paradigm for Research
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More than a collection of tables for Set-selection
Query, Upload
Results
Search
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Data can evolve with additions of attributes and properties as
well as through new inferences
Aggregate
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New Sharing Paradigm for Research
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Sharing discoveries in a Context
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Semantic Communities
Vision
Local
Ontology
Ontology
Projects
Platform
Space
Disease
Area
FGx
rDB
Annotated
Literature
Extended
Ontology
Central
Referenced
DB
Chem
Local DB’s
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Thank You
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Science
January 24, 2003
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Information vs. Knowledge
 “Information
 Converting
 And
is data that is endowed with relevance or purpose.
data into information thus requires knowledge.
knowledge, by definition, is specialized. (In fact, truly knowledgeable
people tend toward overspecialization, whatever their field, precisely because
there is always so much to know.)” – Peter Drucker, 1988
 The
conversion of data to information or knowledge is an interpretive process
that implies a sociological context:
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“ItCharter
entails
KM
personal
involvement in and commitment to specific practices, and
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Communities…
Encoding is not an isolated activity, defined by mechanical conciseness:
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“All of this seems to suggest that the significance of words
and descriptions in metadata may not be so much a matter
of clear and unambiguous definition ... Instead, it is more a
matter of doing, acting, and belonging.”
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- Norm Friesen, 2002
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…and
Meaning
Practice both defines and requires meaning:
“This
focus on meaningfulness is… not primarily on the technicalities of
‘meaning.’ It is not on meaning as it sits locked up in dictionaries. It is not
just on meaning as a relation between a sign and a reference…. Practice
is about meaning as an experience of everyday life.
-
Etienne Wenger, 1999
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
Explicit
vs. helps
Tacit
Negotiating
Meaning
takeKnowledge
what is implicit and make it explicit,
tangible, and codifiable.
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Context is essential for framing implicit knowledge
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No Knowledge is formally either tacit or explicit – when meaning is
negotiated so that interpretations and insights can be effectively shared
within a context, then what was tacit is now reified.
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Communities, if defined appropriately through common semantics, can
capture any knowledge that is viewed as relevant and timely, thereby
making it functional
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Semantic Web for Life Sciences
 What
SWLS is-
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W3C Discussion Forum for Scientists and Informaticists
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Identifying critical needs and defining them as use cases
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Help define the relation between information and (codified) knowledge
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Effective formation and interaction of research communities
 What
SWLS isn’t-
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Standards Group
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SIG for Vendors
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Closed
for Industry
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Semantic Web Life Science
 W3C Workshop Oct 27, 28  Formation of Work Groups
Activities
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Mailing List: [email protected]
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ISMB 2005 (Detroit) – Semantic Web Track
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Coordination with BioPAX, GeneOntology, UniProt, NCI, etc
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SWLS Resources
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Semantic Web: http://www.w3.org/2001/sw/
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RDF: http://www.w3.org/rdf
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SWLS:
http://esw.w3.org/topic/SemanticWebForLifeSciences
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DB wrapper: http://www.w3.org/2004/04/30-RDF-RDB-
access/
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