developing course management systems using the semantic web

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Developing Course Management Systems using the Semantic Web

Chapter 8

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Next generation web (Web 2.0)

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Introduction• Next generation web (Web 2.0) will be based on the

semantic web• Success of the web 2.0 vision is dependent on

development of practical and useful semantic web-based applications.

• Semantic web:• We have

– Semantic technologies (OWL, RDF, SPARQL, RDQL)– Language to represent knowledge– Language to query knowledge bases– Language to describe business rules

• We need application models and applications in commercial settings

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Topics for discussion• Course management system based

on semantic web S-CMS– Well known CMSs is Blackboard.com

(ublearns) and WebCT.com• S-CMS provides

– Information and solution management for students and faculty

– Automate different procedures such enroll or register for classes

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S-CMS Architecture

University database XML feeds Html feeds Other data source formats

connectivity layer

Sourcelayer

Conn.layer

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S-CMS Architecture

OWL instancesRUD/SUDwarehouse

Instance generatorRUD (OWL schema)

SUD (OWL Schema)


connectivity layer

Sourcelayer

Conn.layer

Instancelayer

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Inferencelayer

Rules Repository (SWRL)

Rules Engine(Bossom)

Semantic query engine

RQL RDQL BuchingaeQuerylayer

S-CMS Architecture



SUD (OWL Schema)


connectivity layer

Sourcelayer

Conn.layer

Instancelayer

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SUD (OWL Schema)

Inferencelayer





S-CMS Architecture


connectivity layer

Sourcelayer

Conn.layer

Instancelayer

Applicationlayer Rules

EditorQueryEditor

Course management

Reportgenerator

Dynamic Web site

student

teacher

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SUD (OWL Schema)

Inferencelayer





S-CMS Architecture


connectivity layer

Sourcelayer

Conn.layer

Instancelayer

Applicationlayer Rules

EditorQueryEditor

Course management

Reportgenerator

Dynamic Web site

student

teacherAdmin

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S-CMS Architectural layers• Seven distinct layers: Source,

connection, instance, query, inference, application, presentation

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The Source Layer• Responsibility: Data and information

needed for course management• Data stored in RDBMS• Faculty, courses, students, degrees,

enrollment relations, personal information about students and teachers.

• Draw a relational model to represent details of this layer.

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The Source Layer (contd.)• HTML data: An Eclipse plug-in was built to

handle all the information available on a web page in HTML format (Suggestion: request sites to provide RSS feeds)

• Databases: many university-wide databases– Data integration: directly into the application (this is

expensive time and skill-wise, not extensible, fragile, new sources means new code)

– Semantic layer: use of semantics and ontologies makes the integration process automatic

• Scale of the project: This particular case study used 200 tables and 600 views and the number of students in the range of 13000.

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The Connection layer• Responsibility: connecting to various

data source using a variety of protocols• SQL connectors, XML connectors, Web

connectors• In other applications:

– Web services enabled data (SOAP/REST over HTTP)

– RSS feed (XML over http)– Pure text – Legacy databases– Pure HTML from public sites

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The Connection Layer (Contd.)

• Maintains a pools of connections to several data sources• Connections to relations data sources and HTML web sites(!) : HTML

Eclipse plugin was developed.

public class CMSServerImpl extends UnicastRemoteObject implements CMSServer

…..// a method in this class

URL url = new URL( "http://www.buffalo.edu/etc.html" );

Reader in = new Reader( new InputStreamReader( url.openStream() ) );inputLine = in.readLine();

// parse and extract data from this inputLine and loop

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The Connection Layer (contd.)• Major difficulty: Obtain a copy of

the University database with real data from the administration.

• This needs authorization that takes about 3 months.

• Database with sensitive fields (PIN and phone numbers) were blacked out. Had to work with pseudo data.

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The Instance Layer• Responsibility: for managing semantic

web information such as ontologies describing university domain information such as course, students, projects and teachers.

• It is also in charge of transformation of relational data into ontological instances.

• This layer creates knowledge-base that will be used by the upper layers.

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The Instance Layer• Major challenge: need to query across

multiple heterogeneous, autonomous and distributed (HAD) university data produced by multiple organizational units.

• Different types of data, varying formats, different meanings, referenced using different names integration challenges

• Four types of heterogeneity were identified:

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Heterogeneities Identified1. System heterogeneity: Application reside on different

hardware and operating system platforms2. Syntactic heterogeneity: Information sources use different

representations and encodings3. Structural heterogeneity: Different document layouts,

formats, and schema.4. Semantic heterogeneity: intended meanings are different• Solution for seamless integration: use of semantic

integration• Global scheme was used to address structural and

syntactic heterogeneity.• Ontologies was used to overcome semantic heterogeneity

– Ontologies can be used in communication between humans, among software systems, and to improve design and quality of the software systems.

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Ontology Creation• OWL was the chosen language• The development of an ontology-driven

application starts with creation of an ontology schema

• The ontology schemas in this application contain the definition of various classes, attributes and relationships

• Protégé was selected as ontology editor

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Ontology creation (contd.)• OWL documents: University Resource

Descriptor (RUD) and Student University Descriptor (SUD)

• Inference over OWL documents needed to answer questions such as– Who are the teachers and students?– What courses are offered by a department?– Which courses are assigned for a specific

teacher?– For which courses a student is enrolled?– Which projects are assigned to a course?– What are the students’ grades in a course?

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RUD: University Resource Descriptor ontology• All the information mentioned

earlier is represented in OWL.• It has also has hundreds of

relationships between concepts.• Relationships will be used in

inference layer to infer new knowledge.

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SUD: Student University Descriptor Ontology• SUD is a resource that describes a

university student.• Each student has SUD.

– Name, id, courses taken, degree, telephone number, age, etc.

• SUD is described using OWL as shown in the next slide

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…<owl:Class rdf:ID:”Student”>

<rdfs:subClassOf> <owl:Restriction>

<owl:cardinality rdf:datatype=http://www.w3.org/2001/XMLSchema#int”>1 </owl:cardinality> <owl:onProperty>

<owl:DatatypeProperty ref:ID=”AverageScore”/> </owl:onProperty>

</owl:Restriction>…

SUD OWL definition

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Ontology Population<student rdf:ID=“LeeHall11204199”>(…)<StudentID rdf:datatype= http://www.w3.org/2001/XMLSchema#nonNegativeInteger>2041999</StudentID><StudentName>Lee Hall</StudentName><Degree>Computer Science</Degree><StudentEmail>[email protected]</StudentEmail><Studies> <Subject rdf:ID=“Service_Oriented_Systems”> <SubjectName>Service Oriented Systems</SubjectName>(...) </Subject> </Studies>(…)</Student>

This is a SUD instance. RUD and SUD instances are automatically created.The book list a list of difficulty they faced when creating this ontology.

http://www.w3.org/2001/XMLSchema#nonNegativeInteger%3E2041999

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The Query Layer• Responsibility: allow querying of the

knowledge-base• Query layer provides an interface to the

knowledge-base formed by all the SUD and RUD ontology instances that were automatically generated.

• This interface understands four languages: – RDF query language (RQL)– RDF data query language (RDQL)– Buchingae– SPARQL

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Sample queries (RDQL & Buchingae)

• Buchingae is a simple web-oriented rule language. By "web-oriented", we mean that you can directly refer to URI resources such as web ontology elements or RDF resources when writing rules. Buchingae is a language appropriate for specifying production rules.

• RDQL is a query language that treats RDF as pure data.

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Sample queries (contd.)SELECT ?X, ?C, ?ZWHERE (?X <http://apus.uma.pt/RUD.owl#HasGPA> ?Y), (?X <http://apus.uma.pt/RUD.owl#Studies> ?C) (?Y <http://apus.uma.pt/RUD.owl#Value> ?Z)and Z>3.0

// this a RDQL query for selecting all student with GPA>3.0

Query qu is p:Studies(?st, ?course) and p:Teaches(?prof, ?course);

Can you guess what this query does?

These queries are NOT meant to be designed by end users of the system.Sys admin creates a set of queries and makes them available thru’ user-friendly Interface.

http://apus.uma.pt/RUD.owl#HasGPA

http://apus.uma.pt/RUD.owl#HasGPA

http://apus.uma.pt/RUD.owl#Studies

http://apus.uma.pt/RUD.owl#Studies

http://apus.uma.pt/RUD.owl#Value

http://apus.uma.pt/RUD.owl#Value

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The Inference Layer• Responsibility: allows carrying out

inference using semantic rules on the knowledge-base.

• For example: Infer if the changes in the program was effective. (Of course, effectiveness should have been defined using the rules.)

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The Inference Layer (contd.)

• A rule management system was implemented to extract and isolate course management logic from procedural code.

• Since rules for enrollment may change often, it is not a good practice to embed this in the source code.

• This option of detaching enrollment rules from application logic gives administrators an effective way to create rule base and to change the rules.

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The Inference Layer (contd.)

• The rules are defined using SWRL (Semantic Web Rule Language)

• They correspond to axioms about classes or the instances stored in the ontology warehouse (knowledge-base).

• By applying rules we can infer new facts.

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SWRL query sample

rulebase rb01{ (….) rule R01 is if classTaken (?x, RUD:CS6100) and classTaken (?x, RUD:CS6550) then eligible (?x, RUD:CS8050)}

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The Application layer• Responsibility: Interface to the

Teachers and Students• Teachers can create projects

associated with the course, and define semantic rules for enrollment (say, prerequisites).

• Students can interact with the courses and projects.

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Application layer (contd.)• The whole project was implement using Eclipse IDE

(can be easily done using Netbeans IDE)• S-CMS Manager: This is used by teacher, for viewing

courses, adding rules related to the courses, add project and rules related to projects, etc.

• Dynamic Web enrollment: This is for students to enroll in courses.

• Report Generator: for teachers to list a class or grades etc.

• Grading ontology and its plug-in: Grading feature is a plug-in that allows a teacher to define grading policy.

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Evaluation• S-CMS was benchmarked in an university

environment for a specific department.• Server with SQL 2000 and a client machine

running S-CMS.– Intel P4, 3.0Ghz, 512MB, 40Gb disks, MS

windowsXP, connectivity 100Mb Lan– 13000 ontological instances– Scaled well for the student population– Start up loading of data took about 7 min 32 sec

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Summary• Development of semantic web has

the potential to revolutionize the WWW

• S-CMS discussed was fully based semantic technologies.

• Easily adaptable and extensible• Wave of the future, if not the current

trend.

developing course management systems using the semantic web

Documents

semantic web information

web services

web page

web connectorsin

html web sites

semantic web scmswell

data soaprest

data sourcesconnections