G. Papadopoulos, N. Bassiliades Department of Informatics Aristotle University of Thessaloniki

1. Monitoring Conformance to the Internal Regulation of an MSc Course using Ontologies and Rules G. Papadopoulos, N. Bassiliades Department of Informatics Aristotle University of Thessaloniki Greece

2. Main Idea • What? • Effort to develop a Semantic Web Information System that employs a formal representation of the Internal Regulation (IR) of an MSc course • Why? • Provide an indisputable way for humans and agents to use regulations to check compliance of candidate and current MSc students • How? • OWL ontology for the structure and constraints of the IR • SWRL rule set for the functionality of the course • Appropriate software (DL-reasoner and SWRL rule engine) to monitor the compliance of student’s performance to the IR and detect any deviations early. 

3. Advantages • Use of declarative languages • Instead of hard-coding IR into the University’s ERP • Easier maintenance of the IR • Knowledge can be maintained even from non-programmers • Open knowledge environment • External agents can re-use knowledge to their ends • Ability to gain knowledge or draw conclusions • Monitor the compliance to the recommendations of the IR • Using inference mechanisms

4. Structure of Presentation • Semantic Web, Ontologies and Rules • The Internal Regulations Ontology • System Architecture • Classes and Relations • Restrictions and Reasoning • Rules and Inference • Evaluation • Conclusions and Future Work

5. Semantic Web The Semantic Web is a research initiative to create a metadata-rich Web of resources that can describe themselves semantically (meaning of metadata) Metadata describe properties about resources or relations between resources Properties and relations need to follow known and interconnected vocabularies in order to be commonly understood

6. Ontologies Ontologies are formally (mathematically) defined vocabularies of: Types of resources (Concepts or Classes) Properties and Relations that classes can have Restrictions on Properties and Relations Types of values, Cardinality of values, etc. OWL is the official W3C ontology language Based on Description Logic (DL)

7. Ontologies and Reasoning The formal semantics of OWL enable the application of reasoning techniques in order to make logical derivations class membership equivalent classes ontology consistency instance classification Derivations are performed by reasoners Systems able to handle and apply the semantics of the ontology language

8. Why Rules are needed? Ontologies shortcomings for some tasks: Querying: DL reasoning has low reasoning and querying performance over the ontology instances Non-monotonicity: DLs follow open world assumption Sometimes it is preferable to have non-monotonicity (e.g. negation as failure) Expressivity: Rules extend the expresiveness of DL ontology languages Integrity constraints: Constraints over instances Derived attributes: Values of properties logically depend on the values of other properties of the same or other instances

9. Semantic Web Rule Language (SWRL) SWRL gives an extended OWL axiom to include Horn-like clauses It has maximum compatibility with OWL Built on top of OWL (same semantics) Avoids certain landmines of logic, such as negation and disjunction

10. Requirements for Modeling Internal Regulations In our case, both Ontologies and Rules are needed Ontologies (OWL) will be used to model Concepts (classes) Properties of Concepts Relations of Concepts (hierarchical and more) Restrictions on Concepts, Properties and Relations Characteristics of Relations (e.g. symmetric, transitive) Rules will be used as constructors for composite (derived) properties Properties whose values is calculated using values of other properties or related instances

11. Structure of Presentation • Semantic Web, Ontologies and Rules • The Internal Regulations Ontology • System Architecture • Classes and Relations • Restrictions and Reasoning • Rules and Inference • Evaluation • Conclusions and Future Work

12. The Internal Regulations Text that describes the regulations governing the operation of the MSc course, specific administrative matters, organizational structure control of compliance with established rules and sanctions for improper application or manipulation of them. It is a piece of text in natural language (Greek)

13. The IR Role • Currently interactions can be made only betweenhumans (studentsandsecretariat) • The IR text is playing a passive role only. • With the use of the semantically-enabled system we aim to elevate passive entities (e.g. the IR) into active ones that can participate in a consultation process with humans.

14. System Architecture

15. System Users (course-side) • Secretary • Checks compliance to regulations of students already attending the course • Deploys rules to calculate derived values to be stored back to the ontology • Course administrator • Maintains ontology and rules • When governing board modifies the regulations (at the end of each academic year). • Reasoners check consistency of evolved ontology

16. System Users (student-side) • Students already attending the course • Check their compliance to regulations • Resits, performance scholarships, absences, … • Candidate students • Check compliance of their profile with admission regulations • Employ rules to calculate admission score

17. Ontology Design and Construction • Methodology “Ontology Development 101” guide • Study IR text to find important concepts • Identify entities • Main: Student, Instructor, Secretariat, … • Secondary: FacultyStaff, GoverningBoard, … • Identify main procedures • Admissions, module registration, module attendance, module completion, course completion, …

18. Ontology Classes

19. Ontology Relations

20. Restrictions and Reasoning Article 5 Instructors TheGoverningBoarddelegatesteachingdutiesprimarilyto: • FacultyoftheDepartmentsofInformaticsandEconomics. • FacultymembersinotherpartsofAristotleandotherHigherEducationInstitutions (HEIs) inGreeceorabroad. • Peer, VisitingProfessorsinGreeceorabroadandspecialists. • Researchers (holding a doctorate) ofrecognizedresearchcentersandindependentresearchinstitutesorsimilarnationallyrecognizedcentersorinstitutesabroad, wherethey. • MembersoftheScientificPersonneloftheTechnologicalEducationalInstitutes (TEI) aslongastheyhold a doctorate, • PrestigiousScientists, whohavespecializedknowledgeorexperiencerelevanttothesubjectoftheJoint Postgraduate Course on “Informatics and Management” (JPC IM).

21. Restrictions and Reasoning • We used class relations and restrictions to represent regulations. • E.g. External associates are all those instructors who do not belong to the Faculty Staff of either Informatics or Economics departments of AUTH

22. Restrictions about EconomicsCandidateStudent Restriction about background studies Restriction about number and type of modules students must attend

23. Rules and Inference • Rules capture dynamic relations between classes that could not be modeled using OWL • operational knowledge vs. domain knowledge • The rules have been developed using the "SWRL Rules" tab from Protégé. • Inference is performed by the JESS rule engineusing SWRLJess bridge

24. SWRLJess bridge • Data (OWL) and rules (SWRL) exported from Protégé to JESS • OWL classes and instances are transformed to JESS templates and facts • SWRL deductive rules are transformed to production rules • Entail results of the conclusion in working memory • Conclusions are exported back to Protégé • Become part of the main ontology

25. ExampleStudents Admission Article 8 CandidateEvaluationprocess TheselectionofgraduatestudentsistakingintoaccountthecriteriareferredtoinArticle 4 paragraph 1a ofLaw 3685/2008. Thesecriteriaaregroupedintosixparameters. Eachparameterismeasuredinscale 0 - 10 andithasis a weightfactor. Morespecificallytheparametersandtheweightsarethefollowing: • PersonalInterview 7%. • Thedegreegrade, typeofdegree, placementofthecandidateamongfellowstudents 40% • Publishedwork, additionaldegreesorpostgraduatediplomas 8%. • Foreignlanguageproficiency 15%. • Performanceinthe GMAT test 25%. • Workingexperience 5%.

26. Calculation of the Average Score of Candidate Students

27. Admission of the Students with the Top-20 Score

28. SQWRL (Semantic Query-Enhanced Web Rule Language) A SWRL-based language for querying OWL ontologies SQWRL provides SQL-like operations to retrieve knowledge from OWL Needed in order e.g. to sort the grades into a collection and retrieve the top-20 ones

29. Evaluation • As a test case we have used this year’s candidate student evaluation process • 72 students were interviewed by the selection committee • Have been scored for each criterion • Data fed into Protégé • SWRLJess Tab/bridge selected the top 20 from each of the two categories using SWRL rules

30. Structure of Presentation • Semantic Web, Ontologies and Rules • The Internal Regulations Ontology • System Architecture • Classes and Relations • Restrictions and Reasoning • Rules and Inference • Evaluation • Conclusions and Future Work

31. Summary • Developed an OWL ontology and a SWRL rule set, to describe formally and declaratively the structure and the functionality of the Joint MSc Course on “Informatics and Management” of AUTH • As defined in the Internal Regulation this course • Using DL-reasoners and SWRL-aware rule engines we monitor the compliance of student’s performance to the IR and detect deviations early

32. Future Work • Currently, we are developing the web-based monitoring conformance system • Populate the ontology instances from University’s ERP, using data extractors • Provide interfaces for course secretary, administrator and students (current and candidate) • Future • Make ontology and rules more fine-grained and more general • Align the ontology with existing ones (e.g. LKIF)

33. Ontology available at: http://tinyurl.com/IR-IM-JPC-AUTH-owl Thankyou! Any Questions?