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Chapter 9: Ontology Management Service-Oriented Computing: Semantics, Processes, Agents – Munindar P. Singh and Michael N. Huhns, Wiley, 2005
Chapter 9:Ontology Management
Service-Oriented Computing: Semantics, Processes, Agents– Munindar P. Singh and Michael N. Huhns, Wiley, 2005
Chapter 9 2Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
Highlights of this Chapter Motivation Standard Ontologies Consensus Ontologies
Chapter 9 3Service-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? Traditional approach: manually
develop standard ontologies [top down]
Emerging approach: determine “correct” ontology via consensus [bottom up]
Chapter 9 4Service-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 5Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
An Example Upper Ontology
Anything
AbstractObject Event
Place TangibleThing Process
Individual Stuff
Animal Agent Solid Liquid Gas
Human
Set Number Representation
Category
Chapter 9 6Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
OASIS Universal Business Language (UBL)
-ID-taxTypeCode-currencyCode
TaxScheme
-registrationName-companyID-taxLevelCode-exemptionReasonCode
PartyTaxScheme
-ID-ratePercent
TaxCategory
-taxableAmount-taxAmount
TaxSubTotal
-totalTaxAmountTaxTotal
AllowanceCharge Item Party
AddressjurisdictionAddress
registrationAddress
Chapter 9 7Service-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 8Service-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 9Service-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 10Service-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 11Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
Relating Ontologies:
Truck
Wheel
APC
Tire
Truck
Wheel
APC
Wheel
APC
Tireequivalence
equivalencepartOf
Possibly equivalent
Safety in Numbers
No Overlap
Chapter 9 12Service-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 Of2. Superclass Of3. Part Of4. Has Part5. Sibling Of6. Equivalent To7. Other (topic-specific)
Each ontology adds constraints that can help to determine the most likely relationship
Chapter 9 13Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
Initial Experiment:55 Individual Simple Ontologies about Life
Chapter 9 14Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
55 Merged Ontologies
Chapter 9 15Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
Methodology for Merging and Reinforcement
Merging used smart substring matching and subsumptionFor example, living livingThingHowever, living X livingRoombecause 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 16Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
Consensus Ontology for Mutual Understanding
Chapter 9 17Service-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 18Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and
Michael Huhns
Alternative ApproachesWe 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 19Service-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 20Service-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
