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

2. 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 students performance to the IR and detect any deviations early.

3. Use of declarative languages Instead of hard-coding IR into the Universitys 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. 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. 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. 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. 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. Currently interactions can be made only between humans (students and secretariat) 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.

15. 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. 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. 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, …

20. Article 5 Instructors The Governing Board delegates teaching duties primarily to: Faculty of the Departments of Informatics and Economics. Faculty members in other parts of Aristotle and other Higher Education Institutions (HEIs) in Greece or abroad. Peer, Visiting Professors in Greece or abroad and specialists. Researchers (holding a doctorate) of recognized research centers and independent research institutes or similar nationally recognized centers or institutes abroad, where they. Members of the Scientific Personnel of the Technological Educational Institutes (TEI) as long as they hold a doctorate, Prestigious Scientists, who have specialized knowledge or experience relevant to the subject of the Joint Postgraduate Course on Informatics and Management (JPC IM).

21. 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. Restriction about background studies Restriction about number and type of modules students must attend

23. 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 engine using SWRLJess bridge

24. 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. Article 8 Candidate Evaluation process The selection of graduate students is taking into account the criteria referred to in Article 4 paragraph 1a of Law 3685/2008. These criteria are grouped into six parameters. Each parameter is measured in scale 0 - 10 and it has is a weight factor. More specifically the parameters and the weights are the following: Personal Interview 7%. The degree grade, type of degree, placement of the candidate among fellow students 40% Published work, additional degrees or postgraduate diplomas 8%. Foreign language proficiency 15%. Performance in the GMAT test 25%. Working experience 5%.

28. 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. As a test case we have used this years 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. 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. 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 students performance to the IR and detect deviations early

32. Currently, we are developing the web- based monitoring conformance system Populate the ontology instances from Universitys 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 Thank you! Any Questions?