Ontology mapping:
a way out of
the medical tower of Babel?
Frank van Harmelen
Vrije Universiteit Amsterdam
The Netherlands Antilles

Before we start…
 a talk on ontology mappings
is difficult talk to give:
 no concensus in the field
• on merits of the different approaches
• on classifying the different approaches
 no one can speak with authority on
the solution
 this is a personal view, with a sell-by date
 other speakers will entirely disagree
(or disapprove)

Good overviews of the topic
 Knowledge Web D2.2.3:
“State of the art on ontology alignment”
 Ontology Mapping Survey
talk by Siyamed Seyhmus SINIR
 ESWC'05 Tutorial on
Schema and Ontology Matching
by Pavel Shvaiko Jerome Euzenat
 KER 2003 paper Kalfoglou & Schorlemmer
 These are all different & incompatible…

Ontology mapping:
a way out of
the medical tower of Babel?

The Medical tower of Babel
 Mesh
• Medical Subject Headings, National Library of Medicine
• 22.000 descriptions
 EMTREE
• Commercial Elsevier, Drugs and diseases
• 45.000 terms, 190.000 synonyms
 UMLS
• Integrates 100 different vocabularies
 SNOMED
• 200.000 concepts, College of American Pathologists
 Gene Ontology
• 15.000 terms in molecular biology
 NCI Cancer Ontology:
• 17,000 classes (about 1M definitions),

Ontology mapping:
a way out of
the medical tower of Babel?

What are ontologies &
what are they used for
world
concept
language
Agree on a
no shared understanding conceptualization
Conceptual and
terminological confusion Make it explicit
in some language.
Actors: both humans and machines

Ontologies come in very
different kinds
 From lightweight to heavyweight:
• Yahoo topic hierarchy
• Open directory (400.000 general categories)
• Cyc, 300.000 axioms
 From very specific to very general
• METAR code (weather conditions at air terminals)
• SNOMED (medical concepts)
• Cyc (common sense knowledge)

What’s inside an ontology?
 terms + specialisation hierarchy
 classes + class-hierarchy
 instances
 slots/values
 inheritance (multiple? defaults?)
 restrictions on slots (type, cardinality)
 properties of slots (symm., trans., …)
 relations between classes (disjoint, covers)
 reasoning tasks: classification, subsumption
Increasing semantic “weight”

In short
(for the duration of this talk)
 Ontologies are not
definitive descriptions of
what exists in the world (= philosphy)
 Ontologies are
models of the world
constructed
to facilitate communication
 Yes, ontologies exist
(because we build them)

Ontology mapping:
a way out of
the medical tower of Babel?

 Ontology mapping is
old & inevitable
 Ontology mapping is old
• db schema integration
• federated databases
 Ontology mapping is inevitable
• ontology language is standardised,
• don't even try to standardise contents

 Ontology mapping is
important
 database integration,
heterogeneous database retrieval
(traditional)
 catalog matching (e-commerce)
 agent communication (theory only)
 web service integration (urgent)
 P2P information sharing (emerging)
 personalisation (emerging)

 Ontology mapping is
now urgent
 Ontology mapping has acquired
new urgency
• physical and syntactic integration is ± solved,
(open world, web)
• automated mappings are now required (P2P)
• shift from off-line to run-time matching
 Ontology mapping has new opportunities
• larger volumes of data
• richer schemas (relational vs. ontology)
• applications where partial mappings work

Different aspects
of ontology mapping
 how to discover a mapping
 how to represent a mapping
• subset/equal/disjoint/overlap/
is-somehow-related-to
• logical/equational/category-theoretical
 atomic/complex arguments,
 confidence measure
 how to use it
We only talk about “how to discover”

Many experimental systems:
(non-exhaustive!)
 Prompt (Stanford SMI)  Coma (ULeipzig)
 Anchor-Prompt (Stanford SMI)  Buster (UBremen)
 Chimerae (Stanford KSL)  MULTIKAT (INRIA S.A.)
 Rondo (Stanford U./ULeipzig)  ASCO (INRIA S.A.)
 MoA (ETRI)  OLA (INRIA R.A.)
 Cupid (Microsoft research)  Dogma's Methodology
 Glue (Uof Washington)  ArtGen (Stanford U.)
 FCA-merge (UKarlsruhe)  Alimo (ITI-CERTH)
 IF-Map  Bibster (UKarlruhe)
 Artemis (UMilano)  QOM (UKarlsruhe)
 T-tree (INRIA Rhone-Alpes)  KILT (INRIA LORRAINE)
 S-MATCH (UTrento)

Different approaches to
ontology matching
 Linguistics & structure
 Shared vocabulary
 Instance-based matching
 Shared background knowledge

Linguistic &
structural mappings
 normalisation
(case,blanks,digits,diacritics)
 lemmatization, N-grams,
edit-distance, Hamming distance,
 distance = fraction of common parents
 elements are similar if
their parents/children/siblings are similar
decreasing order of boredom

Different approaches to
ontology matching
 Linguistics & structure
 Shared vocabulary
 Instance-based matching
 Shared background knowledge

Matching through
shared vocabulary
Q
Low(Q) Q Up(Q)
 Low(Q) µ Q µ  Up(Q)

Matching through
shared vocabulary
 Used in mapping geospatial databases
from German land-registration authorities
(small)
 Used in mapping bio-medical and
genetic thesauri
(large)

Different approaches to
ontology matching
 Linguistics & structure
 Shared vocabulary
 Instance-based matching
 Shared background knowledge

Matching through
shared instances

Matching through
shared instances
 Used by Ichise et al (IJCAI’03) to
succesfully map parts of Yahoo to
parts of Google
 Yahoo = 8402 classes, 45.000 instances
 Google = 8343 classes, 82.000 instances
 Only 6000 shared instances
 70% - 80% accuracy obtained (!)
 Conclusions from authors:
• semantics is needed to improve on this ceiling

Different approaches to
ontology matching
 Linguistics & structure
 Shared vocabulary
 Instance-based matching
 Shared background knowledge

Matching using shared
background knowledge
shared
background
knowledge
ontology 1 ontology 2

Ontology mapping
using background knowledge
Case study 1
PHILIPS Work with Zharko Aleksovski @ Philips
• Michel Klein @ VU
KIK @ AMC

Overview of test data
Two terminologies from
intensive care domain
 OLVG list
• List of reasons for ICU admission
 AMC list
• List of reasons for ICU admission
 DICE hierarchy
• Additional hierarchical knowledge describing
the reasons for ICU admission

OLVG list
 developed by clinician
 3000 reasons for ICU admission
 1390 used in first 24 hours of stay
• 3600 patients since 2000
 based on ICD9 + additional material
 List of problems for patient admission
 Each reason for admission is described with
one label
• Labels consist of 1.8 words on average
• redundancy because of spelling mistakes
• implicit hierarchy (e.g. many fractures)

AMC list
 List of 1460 problems for ICU admission
 Each problem is described using
5 aspects from the DICE terminology:
 2500 concepts (5000 terms), 4500 links
• Abnormality (size: 85)
• Action taken (size: 55)
• Body system (size: 13)
• Location (size: 1512)
• Cause (size: 255)
 expressed in OWL
 allows for subsumption & part-of reasoning

Why mapping
AMC list $ OLVG list?
 allow easy entering of OLVG data
 re-use of data in
• epidemiology
• quality of care assessment
• data-mining (patient prognosis)

Linguistic mapping:
 Compare each pair of concepts
 Use labels and synonyms of concepts
 Heuristic method to discover
equivalence and subclass relations
Long brain tumor More specific Long tumor
than
 First round
• compare with complete DICE
• 313 suggested matches, around 70 % correct
 Second round:
• only compare with “reasons for admission” subtree
• 209 suggested matches, around 90 % correct
 High precision, low recall (“the easy cases”)

Using background knowledge
 Use properties of concepts
 Use other ontologies to discover
relation between properties
?
…. ….
…. ….
…. ….

Semantic match
DICE aspect
Lexical match taxonomies Given
? Abnormality taxonomy
? Action taxonomy
? Body system taxonomy
? Location taxonomy
? Cause taxonomy
Implicit
OLVG matching: DICE
problem list property problem list
match

Semantic match
Taxonomy of body parts
Blood vessel
is more general is more general
Vein
Artery
is more general
Aorta
Lexical match: Lexical match:
has location Reasoning: has location
implies
Aorta thoracalis dissection Dissection of artery
Location match:
has more
general location

Example: “Heroin intoxication”
– “drugs overdose”
Cause taxonomy
Drugs
is more general
Heroine
Lexical
match: Lexical
Cause match: match:
cause
has more specific cause
cause
Heroin intoxication
Drugs overdosis
Abnormality match:
has more general
Lexical abnormality Lexical match:
match: abnormality
abnormality Abnormality taxonomy
Intoxicatie
is more general
Overdosis

Example results
• OLVG: Acute respiratory failure abnormality
DICE: Asthma cardiale
• OLVG: Aspergillus fumigatus cause
DICE: Aspergilloom
• OLVG: duodenum perforation abnormality,
DICE: Gut perforation cause
• OLVG: HIV
cause
DICE: AIDS
• OLVG: Aorta thoracalis dissectie type B location,
DICE: Dissection of artery abnormality

Extension:
approximate matching
 Terms are not precisely defined
 Terms are not precisely used
Exact reasoning will not be useful
A B A½B?

Approximate matching
 Translate every class-name into a
propositional formula
(both DNF and CNF versions)
A ⊆ B = (∪Ai ⊆ ∩Bk) = ∀i,k (Ai ⊆ Bk)
 ignore increasing number. of
(i,k)-subsumption pairs
 varies from classical to trivial

Results
(obtained on different domain)
600000
500000
400000
B subClass of A
300000 A subClass of B
equivalences
200000
100000
0
0
3
4
5
6
8
9
1
2
7
0
0.
0.
0.
1.
0.
0.
0.
0.
0.
0.
0.

Ontology mapping
using background knowledge
Case study 2
Work with Heiner Stuckenschmidt
@ VU

Case Study:
 Map GALEN & Tambis,
using UMLS as background knowledge
 Select three topics with sufficient overlap
• Substances
• Structures
• Processes
 Define some
partial & ad-hoc manual mappings
between individual concepts
 Represent mappings in C-OWL
 Use semantics of C-OWL
to verify and complete mappings

Case Study:
verification & verification &
derivation UMLS derivation
(medical terminology)
lexical mapping lexical mapping
GALEN Tambis
(medical ontology) derived mapping (genetic ontology)

Ad hoc mappings: Substances
UMLS GALEN
Notice: mappings high and low in the hierarchy, few in the middle

Ad hoc mappings: Substances
UMLS Tambis
Notice different grainsize: UMLS course, Tambis fine

Verification of mappings
=
UMLS:Chemicals
UMLS:Chemicals_ Tambis:Chemical
viewed_structurally
Tambis:enzyme
⊥?
UMLS:Chemicals_
viewed_functionally
UMLS:enzyme
=

Deriving new mappings
UMLS:substance UMLS:Phenomenon_
or_process
UMLS:Chemicals ⊥
Galen:
ChemicalSubstance
UMLS:OrganicChemical
⊇
=
⊆

Ontology mapping:
a way out of
the medical tower of Babel?

“Conclusions”
 Ontology mapping is (still) hard & open
 Many different approaches will be required:
• linguistic,
• structural
• statistical
• semantic
• …
 Currently no roadmap theory on
what's good for which problems

Challenges
 roadmap theory
 run-time matching
 “good-enough” matches
 large scale evaluation methodology
 hybrid matchers (needs roadmap theory)

Ontology mapping:
a way out of
the medical tower of Babel?
?