1. An Architecture for Decision Support in the Age of Semantic Web
Fuhua Oscar Lin
Athabasca University, Canada
Outline
Introduction
Intelligent agents
Service-Oriented Architecture
An example: A Graduate Program Scheduling Service
Conclusions and Future Work

2. What is the Semantic Web?
A mesh of information linked up in such a way as to be easily processable by machines, on a global scale.
An efficient way of representing data on the World Wide Web, or as a globally linked database.
Was thought up by Tim Berners-Lee, inventor of the WWW, URIs, HTTP, and HTML.

3. Organizations, Computing, and BIS at the Age of Semantic Web
need
adaptive
on-demand
businesses and processes to meet dynamic business strategies.
Trends (M. Wooldridge, 2001)
Ubiquity
Interconnection
Delegation
Human-Orientation
Intelligence
Business Information Systems
Focus:
Collaboration
Integration

4. Organizations
Organizations need to be more efficient and productive, more agile and more competitive.  Businesses of all sizes need an array of new tools to connect with customers and partners by providing (and preferably not producing):
The right quality service,
To the right customer,
Through the right channel,
At the right price,
At the right time.

5. Intelligent Agents (IA)
Agents are pieces of software that work autonomously and proactively.
IA help the user in different ways
hide the complexity of difficult tasks
perform some tasks on behalf of their users
teach the end users
monitor events and procedures
help the users collaborate and cooperate
The user doesn't necessarily "listen“ to what the agent "says"

6. Intelligent Agents (IA) (Con’t)
Agents as metaphors
metaphors of interaction
between the user and a program
direct manipulation (traditional programs)
cooperative assistant (IA)
the concept of IA is a tool for system analysis
it is not an absolute characterization that divides the world to agents and non-agents

7. Intelligent Agents (Con’t)
Generic Agent
Percepts
Sensors
Environment
Agent
Actions
Effectors
Design
Event-driven architecture

8. Intelligent Agents Within an IA Agent Communication layer
Other Agents
Communication layer
Sensors
Coordination layer
Agent
Organization layer
Definition layer
Effectors
API layer
Design
Layered architecture

9. Intelligent Agents (Con’t)
Definition layer
reasoning mechanism
learning mechanism
goals
facts
resources

10. Intelligent Agents (Con’t)
Generic Agent
Percepts
Sensors
Internet
Environment
Agent
Actions
Effectors
Agent-Ready?
Design

11. Personal Agents A Personal agent on the Semantic Web will
receive some tasks and preferences from the person,
seek information from Web sources and seek services from Web Services for computational resources, communication with other agents,
compare information about user requirements and preferences,
select some choices, and
give answers to the user.

12. Service-Oriented Architecture (SOA)
Open standards
Cross-platform pursuit
Are becoming prosperous in recent years to enable IT technology to facilitate enterprises perform business services more efficiently and effectively.
Services also must be properly marked-up to make them computer-interpretable, use-apparent, and agent-ready.
They must contain pointers to the corresponding service ontologies.

13. Service-Oriented Architecture (SOA) (Con’t)
They are currently contributing to shape
the processes of business modeling,
solution creation,
service delivery, and
software architecture design, development and deployment.

14. Mission Service-Oriented Application-Centric Integration-Focused
Collaboration Enforced

15. Scheduling Problems Manufacturing factories Transportation systems
Universities
Hospitals
Publishing houses
and so on

16. Migration to an Internet Scheduling Agent. (Yen and Wu, 2004)
Database
Internet Scheduling Agent
Reply/request messages,
Problem descriptions,
scheduling
Results from other agents
Request/reply messages,
Scheduling results
Scheduling data
and instructions
Communication
Agent
Wrapper
Scheduling
engine
Scheduling results,
engine capability
User
Internet
User Interface
Original standalone
Scheduling system

17. Are the Environments Agent-Ready?
How to locate services
Integration
Interoperability
Communication issues
Complex services

18. Web Services
A Web Service is a software system designed to support interoperable machine-to-machine interaction over a network.
It has an interface described in a machine-processable format (WSDL)
Other systems interact with the Web Service in a manner prescribed by its description using SOAP-message, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards.
The Power of Web Services, in addition to their great interoperability and extensibility thanks to the use of XML, is that they can be combined in order to achieve complex operations.

19. Intelligent High-Level Services
Intelligent, high-level services like information brokers, search agents, information filters, intelligent information integration, and knowledge management, are what the users want from the Semantic Web.
They are possible only if a number of ontologies populate the Web, enabling semantic interoperation between the agents and the applications on the Semantic Web, i.e. semantic mapping between terms within the data, which requires content analysis.

20. Enable High-Level Semantic Web Services
We need one special kind of ontology --- ontologies of services themselves. These ontologies should include a machine-readable description of services (as to how they run), the consequences of using the service (e.g., the fee), and an explicit representation of the service logic (e.g., automatic invocation of another service).
Services have their properties, capabilities, interfaces, and effects, all of which must be encoded in an unambiguous, machine-understandable form, to enable agents to recognize the services and invokes them automatically.

21. An Example of Intelligent Services
An agent of a student to plan her/his course study during pursuing a program on behalf of the student must be able to determine:
How to find the course schedule Web pages;
How to invoke the planning facility
What arguments to pass;
What kind of results to expect;
What are the conditions of planning the study (e.g. cost, privacy)
The agent will then reason about these issues and, provided that there are no collision with its logic, will automatically invoke the service eventually.

22. Ontologies
Technically, an ontology is a text-based piece of reference-knowledge, put somewhere on the Web for agents to consult it when necessary, and represented using the syntax of an ontology-representation language.

23. Ontologies
The way to make the DB and KB machine-understandable, machine-processable, and agent-ready, is to mark it up with pointers to a number of shareable scheduling ontologies.
Every such an ontology should provide a set of knowledge terms, including the vocabulary, the semantic interconnections, and some simple rules of inference and logic for some particular topic or services. (J. Hendler, “Agents and the semantic web,” IEEE Intelligent Systems, vol. 16, pp.30-37, 2001)

24. Languages for Representing Ontologies and KB and Services
Most of them are based on XML (eXtensible Markup Language), XML Schema, RDF (Resource Definition Framework), and RDF Schemas (W3C)
Interoperability and knowledge sharing between different scheduling applications can be achieved by using appropriate languages for representing ontologies and scheduling content and services.
For developing ontologies, higher-level languages built on top of those four such as DAML, OIL, DAML+OIL, and OWL, are a good choice.

25. UIA … TA-1 TA-i TA-l … … … WS-1 … WS-j … WS-k WS-m … … STA’-1 STA’-l
delegate
delegate
TA-1
TA-i
TA-l … …
request …
request
WS-1 …
WS-j …
WS-k
WS-m …
support
support …
support
STA’-1
STA’-l
STA’-n … …

26. HTML and XML HTML XML Layout-oriented
A fixed set of tags to format text
XML
Structure-oriented
Tags are arbitrary (user-defined) and bear some semantic information themselves

27. HTML and XML <html> <head>
<title> My Contact Information </title>
</head>
<body>
<p>Fuhua Lin </p>
<p>School of Computing and Information Systems,
Athabasca University, Alberta, Canada, T9S 3A3 </p>
</body>
</html>
<MAILINGADDRESS>
<NAME> Fuhua Lin </NAME>
<DEPT> School of Computing and Information Systems </DEPT>
<ORGANIZATION> Athabasca University </ORGANIZATION>
<PROVINCE> Alberta </PROVINCE>
<COUNTRY> Canada </COUNTRY>
<POSTALCODE> T9S 3A3 </POSTALCODE>
</MAILINGADDRESS>

28. XML Schema
Provides the necessary framework for creating XML documents by specifying the valid structure, constraints, the number of occurrences of specific elements, default values, and data types to be used in the corresponding XML documents.
The encoding syntax of XML Schema is XML, and just like XML itself XML Schema documentations use namespaces that are declared using the xmlns attribute.
Namespaces define contexts within which the corresponding tags and names apply.
<xsd:schema xmlns: xsd=
<xsd:element name=“MAILINGADDRESS” type=“AddressType”/>
<xsd:complexType name=“Address_Type” >
<xsd:element name=“NAME” type=“xsd:string”
minOccurs=“1” maxOccurs =“2” />
<xsd:element name= “DEPT” type=“xsd:string” /> …
</xsd:complexType>
</xsd:schema>

29. RDF
a framework to represent data about data (metadata), and a model for representing data about “things on the Web” (resources).
Author
“Oscar Lin”

30. Ontological Support for Web-based Scheduling
As the technology advances, the Web of today is likely to get gradually transformed into the Semantic Web, a huge network of machine-understandable and machine-processable human knowledge, not just ordinary information.
The Semantic Web ( is expected to provide explicit representation of the semantics of data in the form of various domain theories stored on many Web servers as a myriad of shareable ontologies, as well as advanced, automated, ontology-supported, and agent-ready reasoning services. That way, ontologies will provide the necessary armature around which knowledge bases will be built.

31. Ontology Design
(Noy, 2000) pointed out three fundamental rules in ontology design:
There is no one correct way to model a domain— there are always viable alternatives.
Ontology development is necessarily an iterative process.
Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain.

32. Protégé 2000
An extensible infrastructure and allows the easy construction of domain ontology, customized data entry forms.
An API that can easily be extended by Web Services.

33. Scheduling Semantic Web

34. Background
To be effective, advisors and faculty need to understand each individual student’s educational and career objectives, their time and financial constraints, and their progress through the program. These can then be matched against degree requirements, course start dates, course availability, and prerequisites in order to generate a personalized course path. The provison of effective program advice for the average of 30 graduate students per faculty member is a time consuming task. In order to meet the students’ needs and to reduce the workload of the advisors, we are developing an intelligent agent able to provide the students with an automated MSc IS Graduate program scheduling service (GPSS). On behalf of the students’ advisors, the agent will assist them in generating up-to-date, personalized, and optimal study plans by constantly monitoring and utilizing related information resources.

35. Related Work
Most research in Adaptive Educational Hypermedia [2] has focused on techniques for adaptation at navigational level and at content level. For example, Dolog, et al. (2004) [3] have proposed Smart Space for Learning which supports access to individualized learning materials and experiences using Personal Learning Assistants. At the program planning level, California State University has set the implementation of the PeopleSoft degree audit system at all campuses [4] to actively monitor a student’s course decisions during registration and to provide information that informs course decisions. To be scaleable and to allow for growth beyond this individual application, advise systems must adhere to formally defined structures. Kay [5], Chen & Mizoguchi, [6] have noted the advantage of using ontologies for learner/user models. Razmerita, et al., (2003) [7] proposed a generic ontology-based user modeling architecture, OntobUM, based on the IMS LIP ( /profiles/index.html). We have built our prototype to these XML specifications, thus creating building blocks upon which further support and administrative systems can be built.

36. GUI monitor monitor GUI delegate Learner Notification Web Service UIA
notify
update
request
delegate
Scheduling
Agent
request/
provide
Learner Information
Web Service
Learner
Data Base
Agent
Platform
GUI
request/provide
Course Schedule
Web Service
Advisor/Registrar
/Administrator
monitor
MSc IS Ontology
Web Service
delegate/
notify DB
Monitoring
Agent
Agent
Platform
monitor
Program/Course
Data Base
GUI
Administrator

37. … University 1 University 2 University N GUI monitor delegate Learner
Notification
Web Service
UIA
notify
update
Advisor/Registrar
/Administrator
Learner Information
Web Service
request
delegate
Scheduling
Agent DB
Monitoring
Agent
MSc IS Ontology
Web Service
Learner
Data Base
GUI
Brokering Services
monitor
delegate/
notify
request/provide
Course Schedule
Web Service
Course Schedule
Web Service
Course Schedule
Web Service …
MSc IS Ontology
Web Service
MSc IS Ontology
Web Service
MSc IS Ontology
Web Service
Program/Course
Data Base
Program/Course
Data Base
Program/Course
Data Base DB
Monitoring
Agent DB
Monitoring
Agent DB
Monitoring
Agent
University 1
University 2
University N

38. MSIS Ontology

39. Interface

40. PNML-represented Program Model
Furthermore, Course Dependency Relations are modeled as a Petri net and represented by a Petri Net Markup Language
<?xml version="1.0" encoding="ISO "?>
<pnml>
<net id="n1" type="BlackTokenNet">
<name>unnamed</name>
<transition id="COMP501">
<name>
<value>1</value>
</name>
</transition>
<place id="READYCOMP501">
<marking>
<value> </value>
</marking>
<value>READYCOMP501</value>
<initialMarking>
</initialMarking>
</place> …
<arc id="a1" source="READYCOMP501" target="COMP501">
<inscription>
<value>MANDATORY</value>
</inscription>
</arc>
</net> </pnml>

41. Courses Petri Nets COMP636 COMP695 ISC DONE COMP617 COMP605 COMP667
READY
COMP695
ISC
DONE
COMP617
COMP501
READY
COMP605
COMP667
COMP602
COMP504
READY
COMP504
DONE
COMP504
COMP648
DONE
CTP
COMP603
COMP503
READY
COMP503
DONE
COMP660
COMP503
COMP607
COMP689
COMP610
COMP501
READY
COMP504
DONE
COMP501
COMP604
DONE
DONE
CTE
COMP501
READY
COMP641
COMP604
COMP601
READY
COMP601
DONE
COMP601
COMP503
READY
COMP674

42. Courses Petri Nets COMP695 COMP636 COMP605 COMP617 COMP602 COMP667

43. Program Done COMP696 1 1 COMP697-9 With a choice pattern. COMP697
CTE
COMP696 1
Program
Done
Done
ISF
Done
ISC 1
COMP697-9
Done
CTP
With a choice pattern.
Done
COMP697
Done
COMP698
COMP697
COMP698
COMP699

44. Student Models
GPSS traces the learner’s profile by building a Student Model. We use the IMS LIP standard ( suitably extended to represent the student’s profile. IMS LIP addresses the interoperability of Internet-based Learner Information systems and supports the exchange of learner information. This model represents, in addition to the student’s basic information, the student’s skills concerning every MSc IS Ontology’s curriculum building block and courses together with the student’s Job objectives and backgrounds. The representation of the student’s profile is updated by the system every time the student completes an online course.

45. Typical Job Objectives [MSIS 2000]:
=============================
JO01: Advancement in current job
JO02: First or middle IS management
JO03: Management consultant
JO04: Internal consultant/senior staff
JO05: CIO
JO06: Business analyst
JO07: Entrepreneur
JO08: Outsourcer/systems integrator
JO09: Project manager
JO10: Systems specialist
JO11: Technical specialist
JO12: IT Liaison
JO13: A Ph.D. program leading to teaching
JO14: Electronic commerce specialist
The relations between the job objectives and the career tracks: CT
OB01 | ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
02 | x x x x x
03 | x x x x x x x
04 | x x x x x x x
05 | x x
06 | x x x x x x
07 | x x x
08 | x x x x x
09 | x x x
10 | x x
11 | x x x x x x x
12 | x x x
13 | x
14 | x

46. Relations between the career tracks and the courses [by F. Lin]
COMP501  All
COMP503  All
COMP504  All
COMP601  All
COMP601  CT02, 03, 05, 06, 07, 08, 13.
COMP603  CT13.
COMP604  CT01, 05, 06, 07, 09, 10, 11, 12, 14, 16.
COMP605  CT01, 02, 07, 11, 12, 13, 14.
COMP607  CT01, 02, 05, 09, 11, 13, 15.
COMP610  CT01, 02, 09, 10, 12, 13, 14.
COMP695  All
COMP617  CT01, 06, 07, 10, 11, 12, 16.
COMP636  CT02, 05, 11, 09, 16.
COMP641  CT01, 11, 15
COMP648  CT15, 11, 10, 01, 02, 05, 06, 07, 14, 12, 16.
COMP660  CT09, 01, 02, 03, 05, 06, 07, 08, 10, 11, 14, 16.
COMP667  CT01, 04, 05, 06, 07, 08, 09, 10, 11, 13, 14, 15.
COMP674  CT01
COMP689  CT01, 02, 03, 05, 06, 07, 08, 09, 10, 11, 14.
Representative Career Tracks [MSIS, 2000]
====================================
ID Name
CT01 Academia (path to Doctorate)
CT02 Consulting
CT03 Data Management and Data Warehousing
CT04 Decision Making
CT05 Electronic Commerce
CT06 Enterprise Resources Planning
CT07 Global IT Management
CT08 Knowledge Management
CT09 Managing the IS Function (Internal to IS)
CT10 Management of the IS Function
CT11 New Ways of Working
CT12 Project Management
CT13 System Analysis & Design
CT14 Technology Management
CT15 Human Factors
CT16 Telecommunications
CT01

48. A Study Plan XML File <?xml version="1.0" encoding="UTF-8"?>
<Study_Plans> <learner_hid> <identifier_kind>ISO_IEC_21484_13</identifier_kind> <identifier_value> </identifier_value> </learner_hid>
<parameters>
<startSemester>Spring 2005</startSemester>
<graduateSemester>Fall 2005</graduateSemester>
<jobObjectives>
<jobObjective>JO02</jobObjective>
<jobObjective>JO03</jobObjective>
<jobObjective>JO04</jobObjective>
</jobObjectives>
<creditSemesters>
<creditSemester>
<credit>3</credit>
<semester>Spring 2005</semester>
</creditSemester>
<credit>6</credit>
<semester>Fall 2005</semester>
<creditSemester>
<credit>6</credit>
<semester>Winter 2006</semester>
</creditSemester>
<semester>Spring 2006</semester>
</creditSemesters>
</parameters>
<plan>
<number>Plan #1</number>
<semester>
<id>Spring 2005</id>
<course>
<id>COMP501</id>
<description>Systems Development With Emerging Technology</description>
</course>

49. <plan>
<number>Plan #2</number>
<semester>
<id>Spring 2005</id>
<course>
<id>COMP501</id>
<description>Systems Development With Emerging Technology</description>
</course>
<id>COMP604</id>
<description>Enterprise-wide Network Architecture</description>
<id>COMP689</id>
<description>Advanced Distributed Systems</description>
</semester>
<course>
<id>COMP604</id>
<description>Enterprise-wide Network Architecture</description>
</course>
</semester>
<semester>
<id>Fall 2005</id>
<course> <id>COMP689</id>
<description>Advanced Distributed Systems</description>
<id>COMP691</id>
<description>Master Project</description>
</plan>

50. <semester>
<id>Fall 2005</id>
<course>
<id>COMP691</id>
<description>Master Project</description>
</course>
</semester>
</plan>
<plan>
<number>Plan #3</number>
<id>Spring 2005</id>
<id>COMP501</id>
<description>Systems Development With Emerging Technology</description>
<id>COMP604</id>
<description>Enterprise-wide Network Architecture</description>
<semester>
<id>Fall 2005</id>
<course>
<id>COMP689</id>
<description>Advanced Distributed Systems</description>
</course>
</semester>
<id>Winter 2006</id>
<id>COMP691</id>
<description>Master Project</description>
</plan>
</Study_Plans>

51. Thank You for Your Attention and Participation!
Questions?