Chapter 9 Ontology Management ServiceOriented Computing Semantics Processes

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

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

Motivation n Ontologies provide n n n But how do we ensure the parties involved agree upon the ontologies? n n Chapter 9 A basis for communication among heterogeneous parties A way to describe services at a high level Traditional approach: manually develop standard ontologies [top down] Emerging approach: determine “correct” ontology via consensus [bottom up] Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 3

Some Standard Ontologies n n n Chapter 9 IEEE Standard Upper Ontology Common Logic (language and upperlevel ontology) Process Specification Language Space and time ontologies Domain-specific ontologies, such as health care, taxation, shipping, … Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 4

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

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

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

Standardization Cons n Standardization of domain-specific ontologies is n n n Chapter 9 Cumbersome: standardization is more a sociopolitical than a technical process Difficult to maintain: often out of date by the time completed Often violated for competitive reasons Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 8

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

Inducing Common Ontologies n n Instead of beginning with a standard, develop consensus to induce common ontologies Assumptions: n n No global ontology Individual sources have local ontologies Which are heterogeneous and inconsistent Motivation: Exploit richness of variety in ontologies n n Chapter 9 To see where they reinforce each other To make indirect connections (next page) Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 10

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

Relating Ontologies n A concept in one ontology can have one of seven mutually exclusive relationships with a concept in another: 1. 2. 3. 4. 5. 6. 7. n 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 12

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

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

Methodology for Merging and Reinforcement n Merging used smart substring matching and subsumption For example, living. Thing However, living X living. Room because they have disjoint subclasses n n Retained the classes and subclass links that appeared in more than 5% of the ontologies n n 864 classes with more than 1500 subclass links were merged into 281 classes related by 554 subclass links 281 classes were reduced to 38 classes with 71 subclass links Merged concepts that had the same superclass and subclass links n Chapter 9 Result has 36 classes related by 62 subclass links Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 15

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

Consensus Directions n n The above approach considered lexical and syntactic bases for similarity Other approaches can include n n n Chapter 9 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 Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 17

Alternative Approaches We may construct large ontologies by n Inducing classes from large numbers of instances using data-mining techniques n Building small specialized ontologies and merging them (Ontolingua) n Top-down construction from first principles (Cyc and IEEE SUO) Chapter 9 Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns 18

Aside: Categorizing Information Consensus is driven by practical considerations n Should service providers classify information where it n n n 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 19

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