1. Chapter 9: Ontology Management Service-Oriented Computing: Semantics, Processes, Agents – Munindar P. Singh and Michael N. Huhns, Wiley, 2005

2. Chapter 92Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Highlights of this Chapter Motivation Standard Ontologies Consensus Ontologies

3. Chapter 93Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Motivation Ontologies provide A basis for communication among heterogeneous parties A way to describe services at a high level But how do we ensure the parties involved agree upon the ontologies? Traditionally: manually develop standard ontologies Emerging approach: determine “correct” ontology via consensus

4. Chapter 94Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Some Standard Ontologies IEEE Standard Upper Ontology Common Logic (language and upper- level ontology) Process Specification Language Space and time ontologies Domain-specific ontologies, such as health care, taxation, shipping, …

5. Chapter 95Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns An Example Upper Ontology

6. Chapter 96Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns OASIS Universal Business Language (UBL)

7. Chapter 97Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Standardization Pros Even if imperfect, standards can Save time and improve effectiveness Enable specialized tools where appropriate Improve the reach of a solution over time and space Suggest directions for improvement

8. Chapter 98Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Standardization Cons Standardization of domain-specific ontologies is Cumbersome: standardization is more a sociopolitical than a technical process Difficult to maintain: often out of date by the time completed Often violated for competitive reasons

9. Chapter 99Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Standardization: Proposed Approach Use standard languages (XML, RDF, OWL, …) where appropriate Take high-level concepts from standard models: Domain experts are not good at KR Lot of work in the best of cases Work toward consensus in chosen domain

10. Chapter 910Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Inducing Common Ontologies Instead of beginning with a standard, develop consensus to induce common ontologies Assumptions: No global ontology Individual sources have local ontologies Which are heterogeneous and inconsistent Motivation: Exploit richness of variety in ontologies To see where they reinforce each other To make indirect connections (next page)

11. Chapter 911Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Relating Ontologies: Truck Wheel APC Tire Truck Wheel APC Wheel APC Tire equivalence partOf Possibly equivalent Safety in Numbers No Overlap

12. Chapter 912Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Relating Ontologies A concept in one ontology can have one of seven mutually exclusive relationships with a concept in another: 1. Subclass Of 2. Superclass Of 3. Part Of 4. Has Part 5. Sibling Of 6. Equivalent To 7. Other (topic-specific) Each ontology adds constraints that can help to determine the most likely relationship

13. Chapter 913Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Initial Experiment: 55 Individual Simple Ontologies about Life

14. Chapter 914Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 55 Merged Ontologies

15. Chapter 915Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Methodology for Merging and Reinforcement Merging used smart substring matching and subsumption For example, living  livingThing However, living  X  livingRoom because they have disjoint subclasses 864 classes with more than 1500 subclass links were merged into 281 classes related by 554 subclass links Retained the classes and subclass links that appeared in more than 5% of the ontologies 281 classes were reduced to 38 classes with 71 subclass links Merged concepts that had the same superclass and subclass links Result has 36 classes related by 62 subclass links

16. Chapter 916Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Consensus Ontology for Mutual Understanding

17. Chapter 917Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Consensus Directions The above approach considered lexical and syntactic bases for similarity Other approaches can include Folksonomies (as in tag clouds) Richer dictionaries Richer voting mechanisms Richer forms of structure within ontologies, not just taxonomic structure Models of authority as in the WWW

18. Chapter 918Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Alternative Approaches We may construct large ontologies by Inducing classes from large numbers of instances using data-mining techniques Building small specialized ontologies and merging them (Ontolingua) Top-down construction from first principles (Cyc and IEEE SUO)

19. Chapter 919Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Aside: Categorizing Information Consensus is driven by practical considerations Should service providers classify information where it Belongs in the “correct” scientific sense? Where users will look for it? Case in point: If most people think a whale is a kind of fish, then should you put information about whales in the fish or in the mammal category?

20. Chapter 920Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns Chapter 9 Summary For large-scale systems development, coming to agreement about acceptable ontologies is nontrivial Standardization helps, but suffers from key limitations Consensus approaches seek to figure out acceptable ontologies based on available small ontologies Should always use standards for representation languages