1. 1 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 1 Advanced databases – Defining and combining heterogeneous databases: Ontology matching Bettina Berendt Katholieke Universiteit Leuven, Department of Computer Science http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ Last update: 5 November 2007

2. 2 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 2 Agenda DBs & ontologies: What‘s new in ontology matching? A classification of schema-based ontology matching Example OLA Example Semantic Integration Through Invariants Evaluating matching Involving the user: Explanations

3. 3 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 3 Recap: The match problem for relational databases  Given two schemas S1 and S2, find a mapping between elements of S1 and S2 that correspond semantically to each other

4. 4 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 4 Ocean Lake BodyOfWater River Stream Sea NaturallyOccurringWaterSource Recap: The water ontology (and how it would give rise to a match problem) Tributary Brook Rivulet Properties: feedsFrom: River Properties: emptiesInto: BodyOfWater (Functional) (Inverse Functional) (Inverse) Properties: containedIn: BodyOfWater (Transitive) Properties: connectsTo: NaturallyOccurringWaterSource (Symmetric) Ex.: How would this map to the taxonomy: WaterEntity River LakeOrPond OceanOrSea (and their properties)?

5. 5 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 5 Motivation: Applications n All of the applications of schema matching n Specific (Semantic-) Web-related applications: l Agent communication l Web Services integration

6. 6 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 6 Similarities between (database schemas and ontologies) or (databases and knowledge bases) n Same problems: Match conceptual schemata, map instances n Much overlap in expressivity, including l Objects, l properties, l aggregation, l generalization, l set-valued properties, l constraints n Structural similarities: l Matching structure of relational tables  matching class hierarchies  Can re-use schema matching methods from DB literature

7. 7 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 7 Database (schema)s and ontologies: differences n Databases are often created to structure a given set of data, whereas ontologies are created to describe the common structure of a domain (independent of data and applications)  Schema-based (as opposed to instance-based) matching more common. n Ontologies often have richer expressivity  This can be exploited by matching algorithms. n Ontologies are designed to be shared and extended  An interesting type of re-use matching: re-use upper ontology Characteristic #1 Characteristic #2 Charact eristic #3

8. 8 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 8 Note: Further differences and their effects (with different effects on the matching problem; not treated in this lecture) n Databases are often created to structure a given set of data, whereas ontologies are created to describe the common structure of a domain (independent of data and applications)  Different primary roles of constraints l In DB: integrity constraints  ensure integrity of the data (= instances) l In ontologies: express meaning, ensure consistency (either of the ontology or of the instances) n Different foci of the processing engines: l SQL engines: answer queries, reason with views, ensure data integrity; l Inference engines: derive new information via automated inference; taxonomic reasoning is key n Database schemas often do not provide explicit semantics for their data (lost after design, not part of the DB spec.)  Ontologies are logical systems that obey formal semantics, e.g., we can interpret ontology definitions as a set of logical axioms

9. 9 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 9 Agenda DBs & ontologies: What‘s new in ontology matching? A classification of schema-based ontology matching Example OLA Example Semantic Integration Through Invariants Evaluating matching Involving the user: Explanations

10. 10 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 10 The match operator (1): Basics n Match operator: f(o,o‘) = alignment between o and o‘ l for schemas/ontologies o, o‘ n Alignment l a set of mapping elements n Mapping elements l elements of o, elements of o‘, relation & some further info:

11. 11 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 11 The match operator (2): Potential extra inputs n r: external resources (thesauri,...) n p: parameters (weights, thresholds,...) n An input alignment A to be completed by the process

12. 12 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 12 Recap: Rahm & Bernstein‘s classification of schema matching approaches

13. 13 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 13 The methods that are important when the schema is in the foreground (OM characteristic #1)

14. 14 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 14 The extension by Shvaiko & Euzenat (2005) [Partial view]

15. 15 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 15 A classification of approaches Schema Matching lecture today (characteristic #2) today: explanations

16. 16 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 16 Agenda DBs & ontologies: What‘s new in ontology matching? A classification of schema-based ontology matching Example OLA Example Semantic Integration Through Invariants Evaluating matching Involving the user: Explanations

17. 17 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 17 Basic ideas n In principle, a fully automatic approach; user can – at most – adapt parameters n input: two ontologies in OWL-Lite n Use a dedicated typed graph representation of the language that concentrates the necessary information for computing the similarity between OWL entities n a similarity measure that encompasses all OWL-Lite features n Basic idea: similarity between two elements depends on their pairwise similarity and that of all adjacent elements n Use the computed measure for generating an alignment n Special provisions for key problems (collection comparison, circularities)

18. 18 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 18 OL graphs This ontology (expressed in UML)...... becomes this OL graph:

19. 19 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 19 OL graphs n categories of nodes: l class (C) l object (O) l relation (R) l property (P) l property instance (A) l datatype (D) l datavalue (V) l property restriction labels (L) n edges express relationships: l rdfs:subClassOf between two classes or two properties (S) l rdf:type (I) between objects and classes, property instances and properties, values and datatypes l A between classes and properties, objects and property instances l owl:Restriction (R) expressing the restriction on a property in a class l valuation (U) of a property in an individual

20. 20 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 20 The similarity measure for a pair of nodes n Anchor pair and contributors define similarity : n x, x‘: nodes n N(x): the set of all relationships in which x participates n Sim is the similarity  [0,1] n Weights are normalized to sum to 1 n F(x) = {x; y;  F} E

21. 21 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 21 The set of similarities assigns a URI reference to each node from C  O  R  P  D  A

22. 22 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 22 Example (partial view) When thresholding by 0.5 minimal similarity, only Human – Person is returned

23. 23 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 23 Agenda DBs & ontologies: What‘s new in ontology matching? A classification of schema-based ontology matching Example OLA Example Semantic Integration Through Invariants Evaluating matching Involving the user: Explanations

24. 24 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 24 Basic idea n A methodology for responding to Characteristic #2: l Different application ontologies are not created independently of each other, but as extensions of a core ontology n In principle, a highly interactive (semi-automated) method: users are asked to describe the axiomatic properties of their ontologies

25. 25 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 25 Example: PSL core – axiomatizing a set of intuitive semantic primitives for describing the fundamental concepts of manufacturing processes Primitive Lexicon: n Relations: l (object ?x) l (activity ?a) l (activity_occurrence ?occ) l (timepoint ?t) l (before ?t1 ?t2) l (occurrence_of ?occ ?a) l (participates_in ?x ?occ ?t) n Functions: l (beginof ?occ) l (endof ?occ) n... Axiom 1 The before relation only holds between timepoints. (forall (?t1 ?t2) (if(before ?t1 ?t2) (and (timepoint ?t1) (timepoint ?t2)))) Axiom 2 The before relation is a total ordering. (forall (?t1 ?t2) (if(and (timepoint ?t1) (timepoint ?t2)) (or (= ?t1 ?t2) (before ?t1 ?t2) (before ?t2 ?t1))))... Process Specification Language PSL: Core http://www.mel.nist.gov/psl/psl-ontology/psl_core.html

26. 26 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 26 Basic idea: ontologies extend PSL

27. 27 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 27 Definitional extensions n Preserving semantics is equivalent to preserving models of the axioms. l preserving models = isomorphism n classify models by using invariants (properties of models that are preserved by isomorphism). l automorphism groups, endomorphism semigroups n Classes of activities and objects are specified using these invariants.

28. 28 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 28 Example: Markovian activities = activities whose preconditions depend only on the state prior to the occurrences Defined by class (3) : NB: class (4): there are additional nonmarkovian constraints on the legal occurrences of the activity

29. 29 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 29 Twenty questions Application ontology designers are asked questions about their classes (e.g., myclass) in order to map these classes to PSL, e.g.: Answer 1  translation definition Answers 1 and 2  translation definition

30. 30 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 30 Ontology mappings n Ontology designers of different extensions of PSL are asked these questions. n Classes from different extensions of PSL can be mapped to each other if they preserve the same invariants.

31. 31 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 31 Agenda DBs & ontologies: What‘s new in ontology matching? A classification of schema-based ontology matching Example OLA Example Semantic Integration Through Invariants Evaluating matching Involving the user: Explanations

32. 32 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 32 The Ontology Alignment Evaluation Initiative http://oaei.ontologymatching.org/2007/

33. 33 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 33 (How did OLA score?)

34. 34 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 34 Agenda DBs & ontologies: What‘s new in ontology matching? A classification of schema-based ontology matching Example OLA Example Semantic Integration Through Invariants Evaluating matching Involving the user: Explanations

35. 35 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 35 Example ontologies  To be matched by S-Match with the help of (among others) WordNet

36. 36 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 36 Explanations – level 1: explanations in English

37. 37 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 37 Explanations – level 2: source metadata information

38. 38 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 38 Miscellaneous (discussion points from last week)

39. 39 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 39 Large ontologies in the life sciences (examples)

40. 40 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 40 Different languages have different (lexicalized) concept boundaries Older brother Younger brother Older sister Younger sister

41. 41 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 41 Next lecture DBs & ontologies: What‘s new in ontology matching? A classification of schema-based ontology matching Example OLA Example Semantic Integration Through Invariants Evaluating matching Involving the user: Explanations Ontology Evolution

42. 42 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 42 References / background reading; acknowledgements n Overviews: P. Shvaiko, J. Euzenat: A Survey of Schema-based Matching Approaches. Journal on Data Semantics, 2005. http://www.dit.unitn.it/~p2p/RelatedWork/Matching/JoDS-IV-2005_SurveyMatching-SE.pdf M. Uschold and M. Grüninger. Ontologies and semantics for seamless connectivity. SIGMOD Record, 33(3), 2004. http://www.sigmod.org/sigmod/record/issues/0412/12.uschold-9.pdf N. Noy: Semantic Integration: A Survey of Ontology-based Approaches. SIGMOD Record, 33(3), 2004. http://www.dit.unitn.it/~p2p/RelatedWork/Matching/13.natasha-10.pdf http://www.dit.unitn.it/~p2p/RelatedWork/Matching/13.natasha-10.pdf n OLA: J. Euzenat, P. Valtchev Similarity-based ontology alignment in OWL-lite In Proceedings of ECAI, 2004. http://ftp.inrialpes.fr/pub/exmo/publications/euzenat2004c.pdf http://ftp.inrialpes.fr/pub/exmo/publications/euzenat2004c.pdf n Invariants: M. Grüninger and J. Kopena. Semantic integration through invariants. In A. Doan, A. Halevy, and N. Noy, editors, Workshop on Semantic Integration at ISWC-2003, Sanibel Island, FL, 2003. http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-82/SI_paper_08.pdf n Explanations in S-Match: Pavel Shvaiko, Fausto Giunchiglia, Paulo Pinheiro da Silva & Deborah L. McGuinness (2005). Web Explanations for Semantic Heterogeneity Discovery. In The Semantic Web: Research and Applications. Springer: LNCS 3532. http://dit.unitn.it/~p2p/RelatedWork/Matching/35320303.pdf http://dit.unitn.it/~p2p/RelatedWork/Matching/35320303.pdf

43. 43 Berendt: Advanced databases, winter term 2007/08, http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/ 43 Acknowledgements n pp. 26-27 taken from: Michael Gruninger (undated). Using Model-Theoretic Invariants for Semantic Integration. http://www.isd.mel.nist.gov/research_areas/research_engineering/Perform ance_Metrics/PerMIS_2004/Proceedings/Gruninger.pdf n p. 39 taken from: Toralf Kirsten, Andreas Thor, & Erhard Rahm (2007). Instance-based matching of large life science ontologies. In Data Integration in the Life Sciences (pp. 172-187). Springer: LNCS 4544. http://dbs.uni-leipzig.de/file/rev-ontomatch-dils-2007-final.pdf n p. 40 taken from: P. Koch (2005). Vorlesung: Probleme der romanischen Wortbildung. http://homepages.uni- tuebingen.de/peter.koch/Semester/Sose05/Handout%201%20Lexikologi e%20Semantik.pdf