Transcript Taal, terminologie en ontologie in een medische context

Language, terminology and
ontology in a medical context:
theory en reality in
industrial applications
Werner CEUSTERS
CTO
Language & Computing nv
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Presentation overview
1. L&C’s current key application
2. Some problems related to existing medical
terminology systems
3. Major reason: a too simplistic interpretation of
the semantic/semiotic triangle
4. L&C’s answer: a cognitive approach to
terminology connecting Aristotelian Realism
with Linguistic Functionalism
5. How this is implemented and used
6. Conclusions and issues for debate
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Mission of L&C nv
We hereby
declare
...
To provide
users and developers
of systems for
knowledge management
with tools and services
for efficient and accurate
data-entry and retrieval by
exploiting the full power of
automated (medical) natural
language understanding
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Employees
Share value (1000 Euro)
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R/D ratio
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Anthem Multi-Tale
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Dome
GIU Select C-Care Liquid
Mobidev
Homey
Poirot
Inface
SCOP
Most Successful Application
• Automatic semantic annotation of documents
– semantic indexing and retrieval
– relevance ranking of in-document semantics
– coding towards medical terminologies
• Often requires formalisation of clients’
proprietary terminologies
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TeSSI Indexing Service
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TEX Indexing component
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Result 1 : Annotated document
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Result 2 : relevance ranking
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View on MeSH coding
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Behind the scene (1):
Semantics based shallow processing
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Behind the scene (2): disambiguation
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But: this system is NOT perfect !
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no code
found
in recognised
MeSH scope of “infections”
missed
theword
largenot
syntactic
Some problems
Major problem:
most medical terminologies
are build following unsound principles
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Problem
areas:
123456
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ICD-10 “class-of” hierarchy
Problems of mapping
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Problems with naïve merging
UMLS
semantic
drift
ISA
circles
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UMLS
superficial
term
analysis
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Is a
“formal”
approach
better ?
SNOMED-RT
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ISA ?
Are these the only
problems ?
Don’t dream !
The semiotic/semantic triangle
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Concept – Model – Mind
“prototype” view
Language
Term
Symbol
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“characteristics” view
Reality
Object
Where it went wrong ...
• Normative interpretation (Wüster,
ISO/TC37, prefered terms, ...)
• Too much focus on the concepts
• Assuming independence amongst
the vertices of the triangle
• Does not survive an intentional
framework
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Too much focus on the concepts
• “conceptualism” versus “realism”
• “ontology” versus “Ontology”
• oversimplification of reality
– discussions on 3D or 4D, instead of 3D and 4D
– limitations of set theory
• “independence of language” paradigm that does
not accept language as a medium of
communication
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Triangular dependencies
concept
The structures of language are
partially determined by our
conceptualisation of the
world.
Human observation is
determined by the
possibilities and
restrictions of the
human body.
Halliday
Lakoff
term
object
Baboons and humans have different cut-off points for discerning "same" objects because
our verbal expression for "same" makes the idea of "same" more restrictive.”
Fagot and Wasserman (Centre for Research in Cognitive Neuroscience in Marseille)
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Intentional framework problem
“Brussels” “capital of Belgium”
“I want to
go to ...”
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The basis of L&C’s approach
the description of
Halliday’s systemic
functional
grammar
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Aristotelian
realism
LinkBase
Language A
Proprietary Terminologies
Language
LexiconB
Lexicon
Others ...
Grammar
ICPC
Grammar
SNOMED
Formal Domain
Ontology
ICD
Cassandra Linguistic
Ontology
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MEDDRA
Example: joint anatomy
• joint HAS-HOLE joint space
• joint capsule IS-OUTER-LAYER-OF joint
• meniscus
– IS-INCOMPLETE-FILLER-OF joint space
– IS-TOPO-INSIDE joint capsule
– IS-NON-TANGENTIAL-MATERIAL-PART-OF
joint
• joint
– IS-CONNECTOR-OF bone X
– IS-CONNECTOR-OF bone Y
• synovia
– IS-INCOMPLETE-FILLER-OF joint space
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• synovial membrane IS-BONAFIDEBOUNDARY-OF joint space
Linguistic and domain ontologies
Generalised Possession
Haspossessor
Human
1
2
IS-A
IS-A
1
Healthcare phenomenon
Haspossessed
1
Having a healthcare phenomenon
2
Is-possessor-of
Patient
3
IS-A
IS-A
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4
Has-Healthcare3 phenomenon
Patient at risk
Patient at risk
for osteoporosis
Is-RiskFactor-Of
IS-A
4
Risk Factor
IS-A
Has-Healthcarephenomenon
Risk factor for
osteoporosis
IS-A
Is-RiskFactor-Of
Osteoporosis
The industrial challenge
• How to set up a profitable business taking into
account
– the problems intrinsic to medical terminologies
• the obligation for many clients to use them
• the existence of many different, incompatible systems
• often based on a design that is not suited for automatic
processing
– the problems intrinsic to natural language processing
– the business environment in which our applications
have to run
– the high costs for “doing right what others do wrong”
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Integration of Research, maintenance and production
WWW
candidate
knowledge
“WebAgent”
gap
“GapFinder”
Medico-Linguistic
Ontology
text to analyse
TeSSI, ...
client
document set
relevance
ranking
index
various beans
candidate
classified
terms
marked up text
various beans
“Model-H1”
corrected
document
production
new-terms
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uncorrected
document
gold standard corpus
research
maintenance
A terminological workbench for
maintenance AND production!
The
solution
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LinkFactory ToolSet
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Managing different views
Internal
ontology
External
ontology
Criteria
Mappings
Definitions
Terms
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Assisted term classification
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1
GapFinder
and
1.
2.
3.
4.
2
5.
concept queried for
information used for document
relevance assessment
new words retrieved
context-information for new
word
relevant pages found
3
4
WebAgent
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5
Research:automatic ontology extraction
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Some details
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Conclusions (and issues for discussion)
1. Medical natural language understanding (MNLU) is a
very complex endeavour;
2. Terminology is a necessary component, though not the
most important one;
3. Traditional terminology approaches have less value
for MNLU than those that accept a close relationship
with language and reality (e.g. the “sociocognitive” approach);
4. Some traditional, authoritative sources in medical
terminology follow a wrong approach;
5. Get away with Conceptualism and adopt Realism.
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Successful industrial application of
MNLU requires (amongst other things):
1. Acceptation of the previous points without
discussion;
2. 200% awareness of the state of the art;
3. the capacity to make valid judgments about the
expected outcomes of a (new) theory without
requiring full proofs;
4. an integrated service/production and
maintenance/ development environment to
transform costs into revenue.
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