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

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.

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

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.

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"

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

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

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

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

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

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.

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.

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.

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

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

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

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

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.

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.

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.

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.

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.

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)

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.

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 … …

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

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>

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=http://www.w3.org/1999/XMLSchema> <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>

RDF a framework to represent data about data (metadata), and a model for representing data about “things on the Web” (resources). Author http://io.acad.athabascau.ca/~oscar “Oscar Lin”

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 (http://www.semanticweb.org) 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.

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.

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.

Scheduling Semantic Web

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.

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 (http://www.imsglobal.org /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.

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

… 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

MSIS Ontology

Interface

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-8859-1"?> <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>

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

Courses Petri Nets COMP695 COMP636 COMP605 COMP617 COMP602 COMP667

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

Student Models GPSS traces the learner’s profile by building a Student Model. We use the IMS LIP 1.0.1 standard (http://www.imsglobal.org/profiles/index.html) suitably extended to represent the student’s profile. IMS LIP 1.0.1 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.

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 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 ---------------------------------------------------------------------------------- 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

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

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>1234567</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>

<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>

<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>

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