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Ontology AlignmentCourse “Ontology Engineering”
Goals of the lecture Understand why ontology alignment is done Know what constructs can be used to express  an alignment...
Agenda Why ontology alignment? Alignment relations Alignment techniques                            3
Why is Ontology Alignment done?                              4
Interoperability problem IIA private company wants to participate in a  marketplaceE.g. eBay:Home > Buy > Cameras & Photo ...
Example use of vocabulary         alignment      “Tokugawa” AAT style/period                SVCN period  Edo (Japanese per...
Alignment architecture for P2P
Two kinds of interoperability Syntactic interoperability  – using data formats that you can share  – XML family is the pr...
Reusing vocabularies                       9
The myth of a unified vocabulary   There will always be multiple ontologies   Partly overlapping   In multiple language...
Links between ontologies “Ontology Alignment” / “Ontology Mapping”  – use ontologies jointly by defining a limited set of...
Why ontology alignment?Summary: There is no single ontology of the world People work with different viewpoints and  thus...
How do we represent the alignment     between two concepts?                                    13
Link types between concepts in different                ontologiesEquality              Individual → individualowl:sameAs ...
Types of links between concepts in         different thesauriskos:mappingRelation  - skos:closeMatch  - skos:exactMatch  -...
SKOS mapping properties- skos:closeMatch           - skos:narrowMatch     - symmetricProperty         -subPropertyOf- skos...
Example: partial alignment between             citations                                 17
Example: alignment between XML             Schemas                                 18
Example: alignment between          thesauri                             19
Types of links between properties in         different ontologiesLinks between properties: equivalentProperty subPropert...
Types of links between concepts in         different ontologies Domain-specific links  – Van Gogh (ULAN) born-in Groot-Zu...
Alignment Techniques                       22
Alignment tools Input: two ontologies, each consisting of a set  of discrete entities     •   HTML table headers     •   ...
Alignment techniques Syntax: comparison of characters of the terms   – Measures of syntactic distance   – Language proces...
String-based techniques (1) Exact string match Prefix  – takes as input two strings and checks whether the    first stri...
String-based techniques (2) Edit distance  – takes as input two strings and calculates the    number of edition operation...
Language-based techniques Tokenization   – parses names into tokens by recognizing punctuation, cases   – Hands-Free Kits...
Linguistic techniques         using WordNet senses A subClassOf B if A is a hyponym of B  – Pine subClassOf Tree A hasPa...
Linguistic techniques: gloss-based WordNet gloss comparison  – The number of the same words occurring in both    input gl...
Structural technique:taxonomy comparison
Techniques for Part-of RelationsPhrase (Hearst) patterns:  add <part> to <whole>  <whole> is made of <part>  <part> gives ...
Overview of alignment techniques
Alignment issues (1) Nature of the input  – Underlying data models  – Schema-level vs. Instance-level  – Example: Link Wo...
Involving the human in alignment            evaluation
Evaluation of alignments Judging individual alignments  – Precision Comparison to a reference alignment  – Recall  – Pre...
The intrinsic fuzziness of       alignment
AATWordNet                37
Literature / acknowledgment Some slides from this lecture are based on a  tutorial of Pavel Shvaiko and Jerome Euzenathtt...
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Ontology engineering: Ontology alignment

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Ontology engineering: Ontology alignment

  1. 1. Ontology AlignmentCourse “Ontology Engineering”
  2. 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. 3. Agenda Why ontology alignment? Alignment relations Alignment techniques 3
  4. 4. Why is Ontology Alignment done? 4
  5. 5. Interoperability problem IIA private company wants to participate in a marketplaceE.g. eBay:Home > Buy > Cameras & Photo > Digital Cameras > Digital SLR > Nikon > D40Needed: correspondences between entries of its catalogs and entries of a common catalog of a marketplace. 5
  6. 6. Example use of vocabulary alignment “Tokugawa” AAT style/period SVCN period Edo (Japanese period) Edo TokugawaAAT is Getty’s SVCN is local in-houseArt & Architecture Thesaurus ethnology thesaurus
  7. 7. Alignment architecture for P2P
  8. 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. 9. Reusing vocabularies 9
  10. 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. 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. 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. 13. How do we represent the alignment between two concepts? 13
  14. 14. Link types between concepts in different ontologiesEquality Individual → individualowl:sameAs “Den Haag” = “The Hague”Equivalence class → classowl:EquivalentClass wood-material = woodSubclass class → classrdfs:subClassOf aat:Artist ⊇ wn:ArtistInstance of individual → classrdf:type tgn:Africa ∈ wn:ContinentDisjoint class → classowl:disjointWith aat:wood ⊥ wn:plastic 14
  15. 15. Types of links between concepts in different thesauriskos:mappingRelation - skos:closeMatch - skos:exactMatch - skos:broadMatch - skos:narrowMatch - skos:relatedMatch 15
  16. 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. 17. Example: partial alignment between citations 17
  18. 18. Example: alignment between XML Schemas 18
  19. 19. Example: alignment between thesauri 19
  20. 20. Types of links between properties in different ontologiesLinks between properties: equivalentProperty subPropertyOf inverseOfE.g. painterOf – creatorOf Trick: wn:hyponym subPropertyOf rdfs:subClassOf 20
  21. 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. 22. Alignment Techniques 22
  23. 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. 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. 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. 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. 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. 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. 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. 30. Structural technique:taxonomy comparison
  31. 31. Techniques for Part-of RelationsPhrase (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. 32. Overview of alignment techniques
  33. 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. 34. Involving the human in alignment evaluation
  35. 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. 36. The intrinsic fuzziness of alignment
  37. 37. AATWordNet 37
  38. 38. Literature / acknowledgment Some slides from this lecture are based on a tutorial of Pavel Shvaiko and Jerome Euzenathttp://dit.unitn.it/~accord/Presentations/ESWC05 Some slides are from Antoine Isaac (STICH) 38

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