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

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

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

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

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

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

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

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is Cumbersome: standardization is more a sociopolitical than a technical process Difficult to maintain: often out of date by the time completed Often violated for competitive reasons Chapter 9 Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael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

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

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

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

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

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

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

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