1. Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Chapter 9: Ontology Management
Service-Oriented Computing: Semantics, Processes, Agents – Munindar P. Singh and Michael N. Huhns, Wiley, 2005
© Singh & Huhns

2. Highlights of this Chapter
Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Highlights of this Chapter
Motivation
Standard Ontologies
Consensus Ontologies
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

3. 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?
Traditional approach: manually develop standard ontologies [top down]
Emerging approach: determine “correct” ontology via consensus [bottom up]
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

4. 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, …
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

5. An Example Upper Ontology
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

6. OASIS Universal Business Language (UBL)
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

7. Standardization Pros Even if imperfect, standards can
Save time and improve effectiveness
Facilitate specialized tools where appropriate
Improve the reach of a solution over time and space
Suggest directions for improvement
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

8. 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Difficult to maintain: often out of date by the time completed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
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

9. 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
Such high-level concepts are nontrivial
Work toward consensus in chosen domain
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

10. 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)
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

11. Relating Ontologies: No Overlap Safety in Numbers Possibly equivalent
Truck
Wheel
APC
Tire
No Overlap
Truck
Wheel
APC
Tire
equivalence
partOf
Possibly equivalent
Safety in Numbers
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

12. Relating Ontologies
A concept in one ontology can have one of seven mutually exclusive relationships with a concept in another:
Subclass Of
Superclass Of
Part Of
Has Part
Sibling Of
Equivalent To
Other (topic-specific)
Each ontology adds constraints that can help to determine the most likely relationship
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

13. Initial Experiment: 55 Individual Simple Ontologies about Life
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

14. 55 Merged Ontologies Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

15. 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
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

16. Consensus Ontology for Mutual Understanding
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

17. 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
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

18. 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)
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

19. 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?
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

20. 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
Chapter 9
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns