Leveraging Data and Structure in Ontology Integration

1. Leveraging Data and Structure in Ontology Integration Octavian Udrea1 Lise Getoor1 Renée J. Miller2 1University of Maryland College Park 2University of Toronto

2. Contents • Motivation and goals • Short overview of OWL Lite • The ILIADS method • Experimental evaluation

3. ILIADS • Goal: • Produce high-quality integration via a flexible method able to adapt to a wide variety of ontology sizes and structures • Method: • Combining statistical and logical inference • Use schema (structure) and data (instances) effectively • Solution: • Integrated Learning In Alignment of Data and Schema (ILIADS)

4. Contributions • Show how to combine statistical and logical inference effectively • Show that a small amount of inference yields high qualitative gain • Show that parameters needed to perform inference over data and structure are robust • Provide a thorough evaluation on 30 pairs of real-world ontologies (with ground truth)

5. Contents • Motivation and goals • Short overview of OWL Lite • The ILIADS method • Experimental evaluation

6. Example OWL Lite ontologies (discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty) (discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty) (resultsF rom, rdfs:subPropertyOf, associatedWith)

7. Example OWL Lite ontologies • Anentitycan be a: • Class (discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty) (discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty) (resultsF rom, rdfs:subPropertyOf, associatedWith)

8. Example OWL Lite ontologies • Anentitycan be a: • Class • Instance (discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty) (discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty) (resultsF rom, rdfs:subPropertyOf, associatedWith)

9. Example OWL Lite ontologies • Anentitycan be a: • Class • Instance • Property (discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty) (discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty) (resultsF rom, rdfs:subPropertyOf, associatedWith)

10. Example OWL Lite ontologies (discoveredBy, owl:inverseOf, discoverer) (discoveredBy, owl:type, owl:FunctionalProperty) (discoveredBy, owl:inverseOf, discoverer) (associatedWith, owl:type, owl:TransitiveProperty) (resultsF rom, rdfs:subPropertyOf, associatedWith)

11. Inference in OWL Lite

12. Inference in OWL Lite

13. Inference in OWL Lite

14. The integration problem

15. The integration problem

16. The integration problem

17. The integration problem

18. Contents • Motivation and goals • Short overview of OWL Lite • The ILIADS method • Experimental evaluation

19. State of the art • Robust statistical methods • Well-known similarity measures • Used for matching data (entities) and schema • May use graph structure of schema • Logical inference • Not combined with statistical inference • Basis for most schema mapping and ontology integration methods • Approaches integrate schema, but not data

20. Issues • How to combine statistical inference with logical inference • Takes into account data, structure, etc. so it’s no longer obvious • In particular, how to quantify the results of logical inference into a similarity-like form? • How to do logical inference in a tractable manner • For OWL-Lite, EXPTIME-complete for the worst case for the entire ontology

21. The ILIADS algorithm repeat until no more candidates • Compute local similarities • Select promising candidates • For each candidate • Select relationship • Perform logical inference • Update score with the inference similarity • Select the candidate with the best score end

22. The ILIADS algorithm repeat until no more candidates • Compute local similarities • Select promising candidates • For each candidate • Select relationship • Perform logical inference • Update score with the inference similarity • Select the candidate with the best score end

23. Computing local similarities • simlexical: Jaro-Winkler and Wordnet • simstructural: Jaccard for neighborhoods • simextensional: Jaccard on extensions • parameters: λx, λs, λe • different for classes, instances and properties

24. The ILIADS algorithm repeat until no more candidates • Compute local similarities • Select promising candidates • For each candidate • Select relationship • Perform logical inference • Update score with the inference similarity • Select the candidate with the best score end

25. Selecting promising candidates • Select candidates with sim(e,e’) > λt • Use a policy based on entity type to order, e.g.: • Class alignments first • Instance alignments first • Alternate between classes and instances

26. The ILIADS algorithm repeat until no more candidates • Compute local similarities • Select promising candidates • For each candidate • Select relationship • Perform logical inference • Update score with the inference similarity • Select the candidate with the best score end

27. Selecting relationship • Must decide on relation type • subClassOf vs. equivalentClass • subPropertyOf vs. equivalentProperty • Determination is difficult, especially under the OWL open-world semantics • Use a simple extension based technique based on a threshold λr

28. Selecting relationship

29. Selecting relationship

30. The ILIADS algorithm repeat until no more candidates • Compute local similarities • Select promising candidates • For each candidate • Represent candidate relationship • Perform logical inference • Update score with the inference similarity • Select the candidate with the best score end

31. Performing logical inference For the candidate pair (e,e’): • Select an axiom to apply • The logical consequences are the pairs of entities (e(i), e(j)) that have just become equivalent • Repeat a small number of times (5) to maintain tractability

32. Performing logical inference

33. Performing logical inference

34. Performing logical inference

35. Performing logical inference (TheodorEscherich, owl:sameAs, T.S. Escherich) is a logical consequence of the candidate (E-ColiPoisoning, owl:sameAs, E-Coli)

36. The ILIADS algorithm repeat until no more candidates • Compute local similarities • Select promising candidates • For each candidate • Represent candidate relationship • Perform logical inference • Update score with the inference similarity • Select the candidate with the best score end

37. Updating score For the candidate pair (e,e’): • Initial local similarity sim(e,e’) • Inference similarity over all consequences: • Updated similarity:

38. Updating score

39. Updating score

40. Consistency • The constructed alignment is not guaranteed to be consistent • ILIADS can only detect inconsistencies that appear in the few logical inference steps • Pellet used to check consistency after ILIADS • Experimentally, inconsistent ontologies in less than .5% of runs

41. Contents • Motivation and goals • Short overview of OWL Lite • The ILIADS method • Experimental evaluation

42. Experimental framework • 30 pairs of real-world ontologies • From 194 to over 20,000 triples • From a variety of domains: medical, geographical, economical, biological • Ground truth provided by human reviewers • Multiple iterations to ensure the best human-provided alignment • Datasets available: • http://www.cs.umd.edu/linqs/projects/iliads

43. Experimental framework • Evaluation: precision, recall and F1 quality • F1 = 2 * Precision * Recall / (Precision + Recall) • 7 independent runs • ILIADS Variations: • ILIADS-tailored uses the best set of parameters for each pair of ontologies • ILIADS-fixed uses one set of parameters for all pairs of ontologies • Used to evaluate robustness of the parameters

44. Experimental framework • ILIADS compared to two leading tools: • FCA-merge [Stumme and Maedche, IJCAI 2001] • uses formal concept analysis and an external document corpus • COMA++ [Aumueller et al., SIGMOD 2005] • implements multiple match strategies, including fragment and reuse-based matching

45. Precision/recall

46. Precision/recall

47. Precision/recall

48. Precision/recall

49. Precision/recall comparison

50. Precision/recall for ontologies with substantial instance data