1. Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Chapter 6: Modeling and Representation
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
Integration versus Interoperation
Common Ontologies
Knowledge Representations
Relationships
Hierarchies
Modeling Fundamentals
Unified Modeling Language (UML)
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

3. Integration versus Interoperation
Tight coupling
Loose coupling
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

4. Modeling and Composing Services
Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Modeling and Composing Services
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

5. Dimensions of Abstraction: 1
Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Dimensions of Abstraction: 1
Abstractions of information resources capture knowledge that is relevant for interoperation; think of these as constraints that must be discovered and represented
Data
Domain specifications
Value ranges, e.g., Price >= 0
Allow/disallow null values
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

6. Dimensions of Abstraction: 2
Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Dimensions of Abstraction: 2
Structure
Taxonomic representations and relationships such as in schemas and views, e.g., securities are stocks
Specializations and generalizations of domain concepts, e.g., stocks are a kind of liquid asset
Value maps, e.g., S&P A+ rating corresponds to Moody’s A rating
Semantic data properties, sufficient to characterize the value maps, e.g., some stock price databases consider daily averages; others closing prices
Cardinality constraints
Integrity constraints, e.g., each stock must have a unique SEC identifier
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

7. Dimensions of Abstraction: 3
Process
Procedures, i.e., how to process information, e.g., how to decide what stock to recommend
Preferences for accesses and updates in case of data replication (based on recency or accuracy of data)
Preferences to capture view update semantics
Contingency strategies, e.g., whether to ignore, redo, or compensate
Contingency procedures, i.e., how to compensate transactions
Flow, e.g., where to forward requests or results
Temporal constraints, e.g., report tax data every quarter
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

8. Dimensions of Abstraction: 4
Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Dimensions of Abstraction: 4
Policy
Security, i.e., who has rights to access or update what information? (e.g., customers can access all of their accounts, except blind trusts)
Authentication, i.e., a sufficient test to establish identity (e.g., passwords, retinal scans, or smart cards)
Bookkeeping (e.g., logging all accesses)
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

9. Value Maps: 1
A value map relates the values expressed by different services
Key properties
Totality
Order preservation
Consistent inversion
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

10. Value Maps: 2
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

11. Ontology
A specification of a conceptualization or a set of knowledge terms for a particular domain, including
The vocabulary: concepts and relationships
The semantic interconnections: relationships among concepts and relationships
Some simple rules of inference and logic
Some representation languages for ontologies:
Uniform Modeling Language (UML)
Resource Description Framework Language Schema (RDFS)
Web Ontology Language (OWL)
Some ontology editors: Protégé, Webonto, OilEd
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

12. Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Common Ontologies
A shared representation is essential to successful communication and interoperation
For humans: physical, biological, and social world
For computational agents: common ontology (terms used in communication)
Representative efforts are
Cyc (and Opencyc)
WordNet (Princeton); LDOCE; OED
Several upper-level ontologies, including by IEEE
Mostly stable concepts such as space, time, person, which can be used within various domains
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

13. Ontologies and Articulation Axioms
Service-Oriented Computing: Semantics, Processes, Agents
August 2004
Ontologies and Articulation Axioms
Mapping by
hand, but
with tool
support
Developing a
common ontology:
All at once
Incrementally via
consensus
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns
© Singh & Huhns

14. Knowledge Representation
Expressive power
Procedural (how) versus declarative (what)
Declarative pros: enables standardization, optimization, improved productivity
Declarative cons: nontrivial to achieve and causes short-term loss of performance
Trade-offs shifted by Web to favor declarative modeling
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

15. Frames versus Descriptions
Frame-based approaches are intuitive but rely on names of classes and properties to indicate meaning
Description logics provide a computationally rigorous means to represent meaning; difficult for people
Managing this trade-off is a major challenge for Knowledge Representation
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

16. Exercise: Which Conceptualization is Most Expressive and Flexible?
awg22SolidBlueWire(ID5)
blueWire(ID5, AWG22, Solid)
solidWire(ID5, AWG22, Blue)
wire(ID5, AWG22, Solid, Blue)
wire(ID5)^size(ID5, AWG22)^type(ID5, solid)^color(ID5, Blue)
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

17. Mappings among Ontologies
Term-to-term (one-to-one), e.g.,
hookupWireO1 ´ wireO2
Many-to-one, e.g.,
solidWireO1(x, size, color) Æ strandedWireO1(x, size, color) ´ wireO2(x, size, color, (Stranded|Solid))
Many-to-many, e.g.,
solidBlueWireO1(x, size) Æ
solidRedWireO1(x, size) Æ
strandedBlueWireO1(x, size) Æ
strandedRedWireO1(x, size)
solidWireO2(x, size, (Red|Blue)) Æ
strandedWireO2(x, size, (Red|Blue))
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

18. Unified Modeling Language (UML) for Ontologies
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

19. Comparison of Modeling Languages
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns

20. Chapter 6 Summary Shared models are essential for interoperation
Based on shared ontologies or conceptualizations
Good models must accommodate several important considerations
Modeling requires several subtle considerations
Declarative representations facilitate reasoning about and managing models
Formalization enables ensuring correctness of models and using them for interoperation
Chapter 6
Service-Oriented Computing: Semantics, Processes, Agents - Munindar Singh and Michael Huhns