1. USC Graduate Student DayColumbia, SCMarch 2006 Presented by: Jingshan Huang Computer Science & Engineering Department University of South Carolina PhD Student Research Area: Ontology, SOC, MAS Advisor: Dr. Michael N. Huhns Understanding Ontologies for Web Service Coordination Jingshan Huang, Rosa Laura Zavala Gutiérrez, Benito Mendoza García, and Michael N. Huhns

2. Jingshan Huang - Computer Science & Engineering Department Background Knowledge Q: What is Web service? A: A functionality that can be engaged over the Web Q: What is Ontology? A: - A computational model of some portion of the world - A declarative knowledge representation model - A list of nodes + properties + relationships

3. Jingshan Huang - Computer Science & Engineering Department Motivation I need some publications before graduation Different Web services need to understand each other Ontologies help in this mutual understanding Impractical to have a global ontology for all Web services Challenge is then to merge/align different ontologies We present a new merging algorithm: PUZZLE

4. Jingshan Huang - Computer Science & Engineering Department §Schema-level merging §Completely automatic §Considers both linguistic and contextual features §Incorporates WordNet §Integrates heuristic knowledge §Three reasoning rules Characteristics of PUZZLE

5. Jingshan Huang - Computer Science & Engineering Department Overview of PUZZLE System §Goal: construct a merged ontology from numerous ones §Input to PUZZLE: G 1, G 2, …, G n §Merge G 1 and G 2 into G 12 §Merge G 12 and G 3 into G 123 §… §Output of PUZZLE: a merged G

6. Jingshan Huang - Computer Science & Engineering Department How We Merge Two Ontologies §Merge G1 and G2 = relocate every node in G1 into G2 §Relocate G1’s root into G2’s root §Traverse G1 in a breadth-first order §Based on the new position(s) of each node’s parent(s), relocate that node into G2

7. Jingshan Huang - Computer Science & Engineering Department Relocate a Node into the Target Ontology 4 mutually exclusive outcomes of the relocation P A relocate into P’ B A/C P’ B C B A C B C A B A C

8. Jingshan Huang - Computer Science & Engineering Department Details of Relocation — Overview §Concept meaning = linguistic feature + contextual feature §Linguistic feature = concept name §Contextual feature = property list + relationships with other concepts

9. Jingshan Huang - Computer Science & Engineering Department Details of Relocation – Linguistic Matching Revisit the example shown before P A relocate into P’ B C All the candidate concepts should be considered §For an exact match or synonym, put it into A’s candidate equivalent-class list §For an anti-postfix or hyponym, put it into A’s candidate subclass list §For a postfix or hypernym, put it into A’s candidate superclass list Where to put A in the left graph?

10. Jingshan Huang - Computer Science & Engineering Department Details of Relocation – Property Matching Consider all the combinations between properties from 2 lists § Find the total-match first § Then the name-match § Lastly the datatype-match The similarity v between 2 property lists depends on the numbers found above: v = (v 1 * w 1 + v 2 * (w 2 + w 2 ’ * f 1 ) + v 3 * (w 3 + w 3 ’ * f 2 ))/n 1 with correcting factors f 1 = v 1 /n 1 and f 2 = (v 1 + v 2 )/n 1

11. Jingshan Huang - Computer Science & Engineering Department Details of Relocation – Determine Relationships Three rules are applied to determine relationships §Rule 1: superclass/subclass relationship of a class is transferable to its equivalent class(es) §Rule 2: if two concepts share the same parent, they are either equivalent-class, superclass/subclass, or sibling §Rule 3: an extension of Rule 2, and embodies the idea of a semantic bridge j2j2 j1j1 C i2i2 i1i1 A k2k2 k1k1 B semantic bridge

12. Jingshan Huang - Computer Science & Engineering Department Experiment Data §A set of ontologies in the domains of “Building” §Constructed by graduate students in computer science and engineering Experiment Purpose §Determine whether a correctly merged ontology is generated

13. Jingshan Huang - Computer Science & Engineering Department Average over 16 Original Ontologies Merged Ontology Max Depth 78 # of Total Nodes 1464 # of Inner Nodes 642 # of Leaf Nodes 547 # of Total Properties 23182 Characteristics of Ontology Schemas in our Experiment

14. Jingshan Huang - Computer Science & Engineering Department Evaluation of PUZZLE - 1 Precision and Recall Measurements of Resultant Ontology

15. Jingshan Huang - Computer Science & Engineering Department Evaluation of PUZZLE – 2 Merging Convergence Experiment

16. Jingshan Huang - Computer Science & Engineering Department §Adopt machine learning techniques §Take into account partOf, hasPart, causeOf, and hasCause relationships §Test our system with more general ontologies Future Work

17. Jingshan Huang - Computer Science & Engineering Department Thank you!!! §Suggestions? §Comments? §Questions?