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

2. 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)
picture of tower of babel?

3. 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…

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

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

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

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

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

9. 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”
increasing degree of semantics/formality

10. 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)

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

12.  Ontology mapping is old & inevitable
db schema integration
federated databases
Ontology mapping is inevitable
ontology language is standardised,
don't even try to standardise contents
compare relational (only structural, not semantics) against ontology (constrain semantics, logical axoims)

13.  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)

14.  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

15. 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”

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

17. Different approaches to ontology matching
Linguistics & structure
Shared vocabulary
Instance-based matching
Shared background knowledge
going to review the first 3 quickly, spend most time on the fourth one

18. 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
problem: ontologies are semantic objects, these methods entirely ignore the semantics
decreasing order of boredom

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

20. Matching through shared vocabularyQ
Up(Q) Q
Low(Q)
U Low(Q) µ Q µ I Up(Q)
Early results with post-doc

21. Matching through shared vocabulary
Used in mapping geospatial databases from German land-registration authorities (small)
Used in mapping bio-medical and genetic thesauri (large)
Early results with post-doc

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

23. Matching through shared instances
Early results with post-doc

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

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

26. Matching using shared background knowledge
ontology 1
ontology 2
Early results with post-doc

27. Ontology mapping using background knowledge Case study 1
Work with Zharko Philips Michel VU
AMC
PHILIPS

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

29. 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)

30. 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

31. 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)

32. Linguistic mapping: Compare each pair of concepts
Use labels and synonyms of concepts
Heuristic method to discover equivalence and subclass relations
More specific than
Long
brain
tumor
Long
tumor
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”)

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

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

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

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

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

38. Extension: approximate matching
Terms are not precisely defined
Terms are not precisely used
Exact reasoning will not be useful B A
A ½ B ?

39. 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

40. Results (obtained on different domain)

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

42. 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
Partial -> complete later
Ad-hoc -> verify later

43. (medical terminology)
Case Study:
UMLS
(medical terminology)
verification & derivation
verification & derivation
Animate diagram
Derived mapping only possible after identity assumption on equal domains
lexical mapping
lexical mapping
derived mapping
GALEN
(medical ontology)
Tambis
(genetic ontology)

44. Ad hoc mappings: Substances
UMLS
GALEN
Notice: UMLS has two views vs. GALEN mixed,
Notice: mappings high and low in the hierarchy, few in the middle
Notice: mappings high and low in the hierarchy, few in the middle

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

46. Verification of mappings
UMLS:Chemicals =
Tambis:Chemical
UMLS:Chemicals_ viewed_structurally ?
Tambis:enzyme
UMLS:Chemicals_ viewed_functionally =
Either: mapping is wrong or UMLS classes are non-disjoint
UMLS:enzyme

47. Deriving new mappings   =  UMLS:substance
UMLS:Phenomenon_ or_process
UMLS:Chemicals 
Galen: ChemicalSubstance
UMLS:OrganicChemical  = 

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

49. “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

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

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