1. Ontology alignment Patrick Lambrix Linköpings universitet

2. Strategies based on linguistic matching Structure-based strategies Constraint-based approaches Instance-based strategies Use of auxiliary information Combining different approaches Alignment Strategies Strategies based on linguistic matching Strategies based on linguistic matching SigO: complement signaling synonym complement activation GO: Complement Activation

3. Alignment Strategies Strategies based on linguistic matching Structure-based strategies Structure-based strategies Constraint-based approaches Instance-based strategies Use of auxiliary information Combining different approaches

4. Alignment Strategies Strategies based on linguistic matching Structure-based strategies Constraint-based approaches Constraint-based approaches Instance-based strategies Use of auxiliary information Combining different approaches O1O1 O2O2 Person Animal Human

5. Alignment Strategies Strategies based on linguistic matching Structure-based strategies Constraint-based approaches Instance-based strategies Instance-based strategies Use of auxiliary information Combining different approaches Ontology instance corpus

6. Alignment Strategies Strategies based linguistic matching Structure-based strategies Constraint-based approaches Instance-based strategies Use of auxiliary information Use of auxiliary information Combining different approaches thesauri alignment strategies dictionary intermediate ontology

7. Alignment Strategies Strategies based on linguistic matching Structure-based strategies Constraint-based approaches Instance-based strategies Use of auxiliary information Combining different approaches Combining different approaches

8. Ontology Alignment and Mergning Systems

9. An Alignment Framework

10. Evaluation - cases  GO vs. SigO  MA vs. MeSH GO-immune defense GO: 70 termsSigO: 15 terms SigO-immune defense GO-behavior GO: 60 termsSigO: 10 terms SigO-behavior MA-eye MA: 112termsMeSH: 45 terms MeSH-eye MA-nose MA: 15 termsMeSH: 18 terms MeSH-nose MA-ear MA: 77 termsMeSH: 39 terms MeSH-ear

11. Evaluation Matchers Term, TermWN, Dom, Learn (Learn+structure), Struc Parameters Quality of suggestions: precision/recall Threshold filtering : 0.4, 0.5, 0.6, 0.7, 0.8 Weights for combination: 1.0/1.2 KitAMO (http://www.ida.liu.se/labs/iislab/projects/KitAMO)

12. Evaluation Terminological matchers

13. Evaluation Basic learning matcher

14. Evaluation Domain matcher

15. Evaluation Comparison of the matchers CS_TermWN CS_Dom CS_Learn Combinations of the different matchers combinations give often better results no significant difference on the quality of suggestions for different weight assignments in the combinations Structural matcher did not find (many) new correct alignments (but: good results for systems biology schemas SBML – PSI MI)

16. Evaluation Matchers TermWN Parameters Quality of suggestions: precision/recall Double threshold filtering using structure: Upper threshold: 0.8 Lower threshold: 0.4, 0.5, 0.6, 0.7, 0.8 Chen, Tan, Lambrix, Structure-based filtering for ontology alignment, IEEE WETICE workshop on semantic technologies in collaborative applications, pp 364-369, 2006.

17. Evaluation The precision is increased after filtering. - a linguistic alignment algorithm using WordNet - the upper threshold is 0.8

18. Evaluation The recall is constant in most cases after filtering - a linguistic alignment algorithm using WordNet - the upper threshold is 0.8

19. Issues Evaluation methodology: Golden standards e.g. OAEI: Anatomy (FMA – GALEN) Systems available, but not always the alignment algorithms. Connections types of algorithms – types of ontologies Recommending ’best’ alignment strategies

20. Further reading http://www.ontologymatching.org Ontology alignment evaluation initiative: http://oaei.ontologymatching.org Lambrix, Tan, SAMBO – a system for aligning and merging biomedical ontologies, Journal of Web Semantics, 4(3):196-206, 2006. Lambrix, Tan, A tool for evaluating ontology alignment strategies, Journal on Data Semantics, VIII:182-202, 2007.