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USC Graduate Student Day Columbia, SC March 2006

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

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USC Graduate Student Day Columbia, SC March 2006

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

  2. 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 Background Knowledge Jingshan Huang - Computer Science & Engineering Department

  3. 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 Motivation Jingshan Huang - Computer Science & Engineering Department

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

  5. Goal: construct a merged ontology from numerous ones • Input to PUZZLE: G1, G2, …, Gn • Merge G1 and G2 into G12 • Merge G12 and G3 into G123 • … • Output of PUZZLE: a merged G Overview of PUZZLE System Jingshan Huang - Computer Science & Engineering Department

  6. 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 How We Merge Two Ontologies Jingshan Huang - Computer Science & Engineering Department

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

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

  9. relocate into P P’ Revisit the example shown before Where to put A in the left graph? B C A 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 Details of Relocation – Linguistic Matching Jingshan Huang - Computer Science & Engineering Department

  10. 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 = (v1* w1 + v2 * (w2 + w2’ * f1) + v3 * (w3 + w3’ * f2))/n1 with correcting factors f1 = v1/n1 and f2 = (v1 + v2)/n1 Details of Relocation – PropertyMatching Jingshan Huang - Computer Science & Engineering Department

  11. semantic bridge A C B i2 j2 k2 i1 j1 k1 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 Details of Relocation – Determine Relationships Jingshan Huang - Computer Science & Engineering Department

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

  13. Characteristics of Ontology Schemas in our Experiment Jingshan Huang - Computer Science & Engineering Department

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

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

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

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

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