Monitoring Conformance to the Internal Regulation of an MSc Course using Ontologies and Rules

Monitoring Conformance to the Internal Regulation of an MSc Course using Ontologies and RulesG. Papadopoulos, N. BassiliadesDepartment of InformaticsAristotle University of ThessalonikiGreece

1Main IdeaWhat? Effort to develop a Semantic Web Information System that employs a formal representation of the Internal Regulation (IR) of an MSc courseWhy? 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 IRSWRL rule set for the functionality of the courseAppropriate software (DL-reasoner and SWRL rule engine) to monitor the compliance of students performance to the IR and detect any deviations early.2AdvantagesUse of declarative languages Instead of hard-coding IR into the Universitys ERPEasier maintenance of the IR Knowledge can be maintained even from non-programmersOpen knowledge environment External agents can re-use knowledge to their endsAbility to gain knowledge or draw conclusionsMonitor the compliance to the recommendations of the IRUsing inference mechanisms3Structure of PresentationSemantic Web, Ontologies and Rules The Internal Regulations Ontology System ArchitectureClasses and RelationsRestrictions and ReasoningRules and InferenceEvaluationConclusions and Future Work4Semantic WebThe 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 resourcesProperties and relations need to follow known and interconnected vocabularies in order to be commonly understood5OntologiesOntologies are formally (mathematically) defined vocabularies of:Types of resources (Concepts or Classes)Properties and Relations that classes can haveRestrictions on Properties and RelationsTypes of values, Cardinality of values, etc.OWL is the official W3C ontology languageBased on Description Logic (DL) 6Ontologies and ReasoningThe formal semantics of OWL enable the application of reasoning techniques in order to make logical derivationsclass membershipequivalent classesontology consistencyinstance classificationDerivations are performed by reasonersSystems able to handle and apply the semantics of the ontology language7Why Rules are needed?Ontologies shortcomings for some tasks:Querying: DL reasoning has low reasoning and querying performance over the ontology instancesNon-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 languagesIntegrity constraints: Constraints over instancesDerived attributes: Values of properties logically depend on the values of other properties of the same or other instances8Semantic Web Rule Language (SWRL)SWRL gives an extended OWL axiom to include Horn-like clausesIt has maximum compatibility with OWLBuilt on top of OWL (same semantics)Avoids certain landmines of logic, such as negation and disjunction9Requirements for Modeling Internal RegulationsIn our case, both Ontologies and Rules are needed Ontologies (OWL) will be used to modelConcepts (classes)Properties of ConceptsRelations of Concepts (hierarchical and more)Restrictions on Concepts, Properties and RelationsCharacteristics 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 instances10Structure of PresentationSemantic Web, Ontologies and Rules The Internal Regulations Ontology System ArchitectureClasses and RelationsRestrictions and ReasoningRules and InferenceEvaluationConclusions and Future Work11The Internal RegulationsText 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)12The IR RoleCurrently 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. 13System Architecture

14System Users (course-side)SecretaryChecks compliance to regulations of students already attending the courseDeploys rules to calculate derived values to be stored back to the ontologyCourse administrator Maintains ontology and rulesWhen governing board modifies the regulations (at the end of each academic year). Reasoners check consistency of evolved ontology15System Users (student-side)Students already attending the courseCheck their compliance to regulationsResits, performance scholarships, absences, Candidate students Check compliance of their profile with admission regulations Employ rules to calculate admission score16Ontology Design and ConstructionMethodology Ontology Development 101 guideStudy IR text to find important conceptsIdentify entities Main: Student, Instructor, Secretariat, Secondary: FacultyStaff, GoverningBoard, Identify main proceduresAdmissions, module registration, module attendance, module completion, course completion, 17Ontology Classes

18Ontology Relations

19Restrictions and ReasoningArticle 5InstructorsThe 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).20Restrictions and ReasoningWe 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

21Restrictions about EconomicsCandidateStudent

Restriction about background studiesRestriction about number and type of modules students must attend22Rules and InferenceRules capture dynamic relations between classes that could not be modeled using OWLoperational knowledge vs. domain knowledgeThe rules have been developed using the "SWRL Rules" tab from Protg. Inference is performed by the JESS rule engine using SWRLJess bridge23SWRLJess bridgeData (OWL) and rules (SWRL) exported from Protg to JESS OWL classes and instances are transformed to JESS templates and factsSWRL deductive rules are transformed to production rulesEntail results of the conclusion in working memoryConclusions are exported back to Protg Become part of the main ontology

24ExampleStudents AdmissionArticle 8Candidate Evaluation processThe 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%.25Calculation of the Average Score of Candidate Students

26Admission of the Students with the Top-20 Score

27SQWRL (Semantic Query-Enhanced Web Rule Language)A SWRL-based language for querying OWL ontologiesSQWRL provides SQL-like operations to retrieve knowledge from OWLNeeded in order e.g. to sort the grades into a collection and retrieve the top-20 onesEvaluationAs a test case we have used this years candidate student evaluation process72 students were interviewed by the selection committee Have been scored for each criterion Data fed into Protg SWRLJess Tab/bridge selected the top 20 from each of the two categories using SWRL rulesStructure of PresentationSemantic Web, Ontologies and Rules The Internal Regulations Ontology System ArchitectureClasses and RelationsRestrictions and ReasoningRules and InferenceEvaluationConclusions and Future Work30SummaryDeveloped 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 AUTHAs defined in the Internal Regulation this courseUsing DL-reasoners and SWRL-aware rule engines we monitor the compliance of students performance to the IR and detect deviations early31Future WorkCurrently, we are developing the web-based monitoring conformance system Populate the ontology instances from Universitys ERP, using data extractorsProvide interfaces for course secretary, administrator and students (current and candidate)FutureMake ontology and rules more fine-grained and more generalAlign the ontology with existing ones (e.g. LKIF)32Ontology available at:http://tinyurl.com/IR-IM-JPC-AUTH-owl

Thank you!

Any Questions?33CandidateStudentEvaluation(?z) Grade_and_Type_of_Degree(?z,?a) swrlb:multiply(?s1,0.4,?a)GMAT(?z,?b) swrlb:multiply(?s2,0.25,?b) Language_learning(?z,?c) swrlb:multiply(?s3,0.15,?c) Publications(?z,?d) swrlb:multiply(?s4,0.08,?d) Professional_Activity(?z,?e)swrlb:multiply(?s5,0.05,?e) Interview(?z,?f) swrlb:multiply(?s6,0.07,?f) swrlb:add(?a1,?s1,?s2) swrlb:add(?a2,?s3,?s4) swrlb:add(?a3,?s5,?s6) swrlb:add(?a,?a1,?a2)swrlb:add(?sum,?a,?a3) inverse_of_Student_Valuation_Info(?z,?stud) Valuation_Average(?stud,?sum)InformaticsCandidateStudent(?z) ^Valuation_Average(?z,?a) sqwrl:makeBag(?S1,?a) sqwrl:greatestN(?S2,?S1,20) ^ sqwrl:element(?x,?S2) ^ swrlb:equal(?x,?a) Valuation_Base_Pass_True(?z,true)