lead beneficiary: aristotle university of thessaloniki – auth (professor demos angelides) iris...

LEAD BENEFICIARY: ARISTOTLE UNIVERSITY OF THESSALONIKI – AUTh (Professor Demos Angelides)

IRIS SUMMER ACADEMY 2011hosted by VCE

03 – 07 September 2011, Zell am See, Austria

The IRIS Risk Knowledge Portal

Georgios Meditskos, Nick Bassiliades Logic Programming & Intelligent Systems group

Dept. of InformaticsAristotle University of Thessaloniki, Greece

http://irisportal.csd.auth.gr

http://irisportal.csd.auth.gr/




Outline• Introduction• The Risk Ontology• The Risk Knowledge Portal– Architecture– Functionality– Implementation

• Demonstration• Next Steps




The Role of Semantics in IRIS

• All the variables associated with the Risk Assessment Process are represented in terms of a Risk Ontology– An upper-level schema that describes terms, relationships

and restrictions of risk identification and assessment• It is a formal representation of the IRIS Risk Glossary

that is used for defining Risk Case Studies (Risk Identification)




Benefits of Semantics• Formal vocabulary of terms publicly available for use by

different organizations– integration of risk assessment practices from

different domains• Derivation of implicit/hidden relationships through reasoning

– semantic integration and consistency checking using state-of-the-art ontology reasoners

• Ability to “link” risk terms with existing semantic descriptions in the Web of Data– building a network of semantically

interconnected concepts (Linked Data)




The Role of the Risk Knowledge Portal• To better manage and disseminate the case studies of the risk registry

– Simplifies the definition and management of risk knowledge• form-based editing vs. Microsoft Excel worksheets• easier representation of risk relationships

– Searching/browsing capabilities based on semantic relationships• To make the underlying risk registry public

– A common web reference for risk management– Continuous updating/refinement of risk knowledge

• To provide basic social features for the active contribution of users to the risk definition/refinement process– comments, discussions, etc.

• Future -> Integrator for Risk Assessment




Outline• Introduction• The Risk Ontology• The Risk Knowledge Portal– Architecture– Functionality– Implementation





Ontology Requirements• Should be able to represent all the needed terms,

relationships and restrictions of the domain– e.g. case studies, risk cases, risk variables and their

relationships• Should be simple– a complex ontology affects reasoning and querying

performance• an important requirement in our case since there is a need for

online (real-time) reasoning tasks




The Ontology• OWL 2 RL Language• 27 classes• 31 properties• A revision of a previous risk ontology (2010)– modified to meet the requirements of the portal

http://irisportal.csd.auth.gr/ontology/iris.owl

http://irisportal.csd.auth.gr/ontology/iris.owl




Top-level Class Hierarchy




Case Study• There are no subclasses• Properties– acronym– editor– hasRiskCases ()– version– versionDate




Risk Case• Properties– appearsInCaseStudy ()– code– consistsOf (Risks) ()– hasFactors ()– hasComponents ()– hasMechanisms ()– hasImpacts ()– editor




Risk• Properties– belongsTo (Risk Case) ()– hasComponent ()– hasFactor ()– hasImpact ()– hasMechanism ()– rickClass (Category)




Risk Variable• Properties– isVariableOf (Risk) ()– with subproperties• isFactorOf• isImpactOf• isComponentOf• isMechanismOf




Category• There are no subclasses• Specific–only instances

(owl:oneOf)• It is used in order to define

instances relevant to categories




Basic Ontology Restrictions• A Case Study may have zero or more Risk Cases– Each Risk Case belongs to a single Case Study

• A Risk Case has one or more Risks– Each Risk belongs to a single Risk Case

• A Risk has one or more Risk Variables (Factors, Components, Mechanisms, Impacts)– A Risk Variable may belong to multiple Risks




Outline• Introduction• The Risk Ontology• The Risk Knowledge Portal–Architecture– Functionality– Implementation





• Risk Knowledge Portal– Web application (.NET 4 Web Forms)

Risk Ontology Service

Web Service(SOAP Messages)

UserIRIS Risk Knowledge Portal

Triple Store(Sesame)

Reasoner(OWLIM)

Architecture




Risk Ontology Service• A WSDL Web Service (in JAVA) that supports operations for

querying and updating the risk ontology• Front-end

– receives SOAP messages from the IRIS Portal• Back-end

– it communicates with Sesame (the triple store) and OWLIM Lite (rule-based ontology reasoner)




Outline• Introduction• The Risk Ontology• The Risk Knowledge Portal– Architecture

– Functionality– Implementation





Overview• User accounts

– role-based authentication

• Risk Ontology Management

• Faceted Browsing/Filtering/Searching

• Favorites




User Roles• Three role-based security levels

– Users: • limited access • they are allowed to search, browse/navigate the risk ontology • able to comment, share ideas and participate in discussions with other users (not

implemented yet)

– Knowledge Engineers: • have all the privileges of Users • they are also capable of posting new case studies and risk cases or updating the ones that

they have submitted• they are not allowed to modify posts of other users

– Administrators: • have all the privileges of Users and Knowledge Engineers • they are also capable of modifying the entire risk ontology • they are responsible for the management of the user accounts (not implemented yet)




Posting New Risks• Two types of post– New Case Studies– New Risk Cases

• with their risk variables

• In order to post a Risk Case, the Case Study should already exist– each Risk Case must belong to a single Case Study




Faceted Searching/Browsing• Users are able to search and browse

– Case Studies– Risk Case– Risk Variable

• For each type, different facets/filters may be defined– e.g. give me all the Case Studies that are associated (indirectly) with a

specific Risk Variable– A dynamic SPARQL query is generated




Favorites• Users can add Case Studies, Risk Cases, Risks and Risk

Variables to their favorites– easy access– keep track of updates and activities




Repository Queries• Dynamically generated SPARQL 1.1 queries based on

users activity– searching, filtering, browsing the ontology

• Queries are submitted via the Risk Ontology Service to OWLIM– it supports non-trivial inference with tens of millions of

statements




Sample SPARQL Query 1 Retrieve all Case Studies

SELECT DISTINCT ?ID ?Acronym ?Description ?Editor ?Version ?VersionDate WHERE {

?ID rdf:type iris:CaseStudy; iris:acronym ?Acronym; iris:description ?Description; iris:version ?Version; iris:versionDate ?VersionDate; iris:editor ?Editor.

}




Sample SPARQL Query 2

SELECT DISTINCT ?CaseStudy ?Acronym ?Description ?Editor ?Version ?VersionDate WHERE { ?CaseStudy rdf:type iris:CaseStudy; iris:acronym ?Acronym; iris:description ?Description; iris:version ?Version; iris:date ?date; iris:versionDate ?VersionDate; iris:editor ?Editor. ?CaseStudy iris:hasRiskCases ?RiskCase0. ?RiskCase0 iris:hasVariables iris:component622427b2-a64a-4e0a-83e4-83c4b309b618. }

variable id

Retrieve all Case Studies that are related to the “blades deflections” variable




Updates

• Currently OWLIM does not support the SPARQL 1.1 UPDATE construct– is used to update the ontology via SPARQL

• Will be available in the next release• We use the native OWLIM API for adding and

removing triples




Custom Rules• Ability to enhance the reasoning results with custom

inferences• OWLIM supports the definition of rules using a triple-

based rule language• Both OWL entailments and custom rules are

translated into JAVA byte-code




Custom Rule ExampleId: AssociateRiskCaseAndComponents

x <rdf:type> <iris:RiskCase>x <iris:consistsOf> rr <iris:hasComponent> c

-------------------------------x <iris:hasComponents> c

riskCase_1 consistsOf Risk_1

Component_1hasComponent

hasComponents

Component_n

hasComponent

hasComponents




Summary of Technologies• Portal– ASP.NET 4 Web forms– jQuery (Ajax-based communication)– SQL SERVER 2008 R2 (for the social features)

• Risk Ontology Service– JAVA 6– Sesame (triple store)– OWLIM Lite 4 (reasoner)– SPARQL 1.1– Apache Tomcat




Risk Assessment Calculation• Portal will become the integrator of various risk

assessment tools• The following calculations are required to assess

overall risk probability– “summation” of the probability of occurrence of the

components– “summation” of the probability of occurrence of risk

variables occurrence, i.e. factors, components, mechanisms, and impacts




Risk Assessment• The ontology will evolve in order to “host” data for

probabilities and various other numerical data values– Will be re-inserted from older ontology versions

• Specialized custom rules will be used to summate the values provided by the users or other tools

• Special APIs will be used to communicate with these other tools (e.g. Matlab)

• Database will hold past risk assessment cases if needed




More Social Features

• User profiles

• Comments

• Connections among users

lead beneficiary: aristotle university of thessaloniki – auth (professor demos angelides) iris...

Documents

risk ontology

risk terms

iris risk glossary

risk assessment slide

risk assessment process

lead beneficiary

data slide

austria ontology requirements