The document discusses ontology alignment, which is the process of finding correspondences between concepts in different ontologies to allow them to be used together. It notes that there is no single unified ontology, so alignment helps integrate overlapping conceptualizations. The key constructs for expressing alignments are relations like equivalence and subclass between concepts. Techniques discussed for finding mappings include string-based, linguistic/language-based, taxonomy comparison, and using example instances. The challenges of alignment evaluation and interpretation of results are also covered.

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
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3. Agenda
 Why ontology alignment?
 Alignment relations
 Alignment techniques
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4. Why is Ontology Alignment done?
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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.
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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
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9. Reusing vocabularies
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10. The myth of a unified vocabulary
 There will always be multiple ontologies
 Partly overlapping
 In multiple languages
 Each with their own perspective
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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!
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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
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13. How do we represent the alignment
between two concepts?
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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
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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
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17. Example: partial alignment between
citations
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18. Example: alignment between XML
Schemas
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19. Example: alignment between
thesauri
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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
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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
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22. Alignment Techniques
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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)
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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.
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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>
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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.
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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
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36. The intrinsic fuzziness of
alignment

37. AAT
WordNet
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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)
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