1. Ontology Alignment
Course “Ontology Engineering”

2. Goals of the lecture
 Understand why ontology alignment is done
 Know what constructs can be used to express
an alignment between two concepts
 Know what options there are to find mappings
2

3. Agenda
 Why ontology alignment?
 Alignment relations
 Alignment techniques
3

4. Why is Ontology Alignment done?
4

5. Interoperability problem II
A private company wants to participate in a
marketplace
E.g. eBay:
Home > Buy > Cameras & Photo > Digital Cameras >
Digital SLR > Nikon > D40
Needed: correspondences between entries of its
catalogs and entries of a common catalog of a
marketplace.
5

6. Example use of vocabulary
alignment
“Tokugawa”
AAT style/period SVCN period
Edo (Japanese period) Edo
Tokugawa
AAT is Getty’s SVCN is local in-house
Art & Architecture Thesaurus ethnology thesaurus

7. Alignment architecture for P2P

8. Two kinds of interoperability
 Syntactic interoperability
– using data formats that you can share
– XML family is the preferred option
 Semantic interoperability
– How to share meaning / concepts
– Technology for finding and representing semantic
links
8

9. Reusing vocabularies
9

10. The myth of a unified vocabulary
 There will always be multiple ontologies
 Partly overlapping
 In multiple languages
 Each with their own perspective
10

11. Links between ontologies
 “Ontology Alignment” / “Ontology Mapping”
– use ontologies jointly by defining a limited set of
links
– Benefit from knowledge encoded in the other
ontology
– Enable access across applications/collections.
– Partial by nature!
11

12. Why ontology alignment?
Summary:
 There is no single ontology of the world
 People work with different viewpoints and
thus multiple conceptualizations
 But: these concepts often overlap
 Semantic relations between ontologies help
integrating information sources
 Currently seen as a major issue in
development of distributed (web) systems
12

13. How do we represent the alignment
between two concepts?
13

14. Link types between concepts in different
ontologies
Equality Individual → individual
owl:sameAs “Den Haag” = “The Hague”
Equivalence class → class
owl:EquivalentClass wood-material = wood
Subclass class → class
rdfs:subClassOf aat:Artist ⊇ wn:Artist
Instance of individual → class
rdf:type tgn:Africa ∈ wn:Continent
Disjoint class → class
owl:disjointWith aat:wood ⊥ wn:plastic 14

15. Types of links between concepts in
different thesauri
skos:mappingRelation
- skos:closeMatch
- skos:exactMatch
- skos:broadMatch
- skos:narrowMatch
- skos:relatedMatch
15

16. SKOS mapping properties
- skos:closeMatch - skos:narrowMatch
- symmetricProperty -subPropertyOf
- skos:exactMatch skos:narrower
- subPropertyOf -inverseOf
skos:closeMatch skos:broadMatch
- transitiveProperty
- symmetric property
-skos:relatedMatch
-subPropertyOf
- skos:broadMatch skos:related
- subPropertyOf
skos:broader -symmetric property
- inverseOf
skos:narrowMatch
16

17. Example: partial alignment between
citations
17

18. Example: alignment between XML
Schemas
18

19. Example: alignment between
thesauri
19

20. Types of links between properties in
different ontologies
Links between properties:
 equivalentProperty
 subPropertyOf
 inverseOf
E.g.
 painterOf – creatorOf
 Trick: wn:hyponym subPropertyOf
rdfs:subClassOf
20

21. Types of links between concepts in
different ontologies
 Domain-specific links
– Van Gogh (ULAN) born-in Groot-Zundert
(TGN)
– Derain (ULAN) related-to Fauve (AAT))
– Wandelkaart Pyreneeën RANDO.07 Haute-
Ariège - Vicdessos (Pied à Terre) related to
Pyrénées (TGN)
– Part-of relations
21

22. Alignment Techniques
22

23. Alignment tools
 Input: two ontologies, each consisting of a set
of discrete entities
• HTML table headers
• XML elements
• Classes
• Properties
 Output: relationships holding between these
entities (equivalence, subsumption, etc.) +
confidence measure.
 Cardinality (e.g., 1:1, 1:m)
23

24. Alignment techniques
 Syntax: comparison of characters of the terms
– Measures of syntactic distance
– Language processing
• E.g. Tokenization, single/plural,
 Relate to lexical resource
– Relate terms to place in WordNet hierarchy
 Taxonomy comparison
– Look for common parents/children in taxonomy
 Instance based mapping
– Two classes are similar if their instances are similar.
24

25. String-based techniques (1)
 Exact string match
 Prefix
– takes as input two strings and checks whether the
first string starts with the second one
– net = network; but also hot = hotel
 Suffix
– takes as input two strings and checks whether the
first string ends with the second one
– ID = PID; but also word = sword

26. String-based techniques (2)
 Edit distance
– takes as input two strings and calculates the
number of edition operations, (e.g., insertions,
deletions, substitutions) of characters
– required to transform one string into another,
normalized by length of the maximum string
– EditDistance ( NKN , Nikon ) = 0.4 (2/5)

27. Language-based techniques
 Tokenization
– parses names into tokens by recognizing punctuation, cases
– Hands-Free Kits => hands, free, kits
 Lemmatization
– analyses morphologically tokens in order to find all their possible
basic forms
– Kits => Kit
 Elimination
– discards “empty” tokens that are articles, prepositions,
conjunctions . . .
– a, the, by, type of, their, from

28. Linguistic techniques
using WordNet senses
 A subClassOf B if A is a hyponym of B
– Pine subClassOf Tree
 A hasPart B if A is a holonym of B
– Europe hasPart Greece
 A = B if they are synonyms
– Quantity = Amount
 A disjoint B if they are antonyms or ar siblings
in the same part of hierarchy
– Pine disjoint Oak

29. Linguistic techniques: gloss-based
 WordNet gloss comparison
– The number of the same words occurring in both
input glosses increases the similarity value.
– The equivalence relation is returned if the
resulting similarity value exceeds a given
threshold
– Maltese dog is a breed of toy dogs having a long
straight silky white coat Afghan hound is a tall
graceful breed of hound with a long silky coat

30. Structural technique:
taxonomy comparison

31. Techniques for Part-of Relations
Phrase (Hearst) patterns:
add <part> to <whole>
<whole> is made of <part>
<part> gives the <whole> its
<whole>-containing <part>
<whole> consists of <part>
31

32. Overview of alignment techniques

33. Alignment issues (1)
 Nature of the input
– Underlying data models
– Schema-level vs. Instance-level
– Example: Link WordNet to Wikipedia
 Interpretation of the output
– Approximate vs. exact
– Graded vs. absolute confidence
 Performance varies
> semi-automatic alignment.
33

34. Involving the human in alignment
evaluation

35. Evaluation of alignments
 Judging individual alignments
– Precision
 Comparison to a reference alignment
– Recall
– Precision?
 Comparing the logical consequences of the
models
 End-to-end evaluation
35

36. The intrinsic fuzziness of
alignment

37. AAT
WordNet
37

38. Literature / acknowledgment
 Some slides from this lecture are based on a
tutorial of Pavel Shvaiko and Jerome Euzenat
http://dit.unitn.it/~accord/Presentations/ESWC'05
 Some slides are from Antoine Isaac (STICH)
38