Ontology Based Context Modeling and Reasoning using OWL

Ontology Based Context Modeling and Reasoning using OWLAuthors: Xiao Hang Wang, Da Qing Zhang, Tao Gu, Hung Keng PungInstitute for Infocom Research, Singapore

Some slides adopted from earlier presentation of Sangkeun Lee; IDS Lab

Akmal KhanMultimedia and Mobile Communication Lab, SNU, Korea

1AgendaBackgroundIntroductionCONON(Context Ontology)Ontology Reasoning & User Defined ReasoningExperiments & ComparisonConclusions & Questions

Pervasive computing

Pervasive computingalso known as:

What is Context?Context is any information that can be used to characterize the situation of an entity.An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and application themselves.

Context TypesLocation position, orientation, velocity, trajectory, etc. Identity preference, profile, social relationship, biometrics, etc.Time Sequence of events, duration, etc.Activity walking, sleeping, sitting, etc. Task meeting, reading, working, etc. Environment temperature, humidity, brightness, loudness, etc.Computing resources device, appliances, etc.Emotion

What is Context-Aware?A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the users task.GoalTo acquire and utilize information about the context of a device to provide services that are appropriate to the particular people, place, time, events, etc.

EX) A cell phone will always vibrate and never beep in a concert, if the system knows the location of the cell phone (i.e., user) and/or the concert schedule.

Acquiring ContextExplicitlyBy requiring the user to specifye.g. Current LocationImplicitlyBy monitoring user and computer-based activitye.g. monitoring of user interaction to turn of a device after a period of inactivitye.g. monitoring of battery power for adaptation of power-intensive applicationsAcquisition of contextSmart environmentsEmbed sensors in ultra-mobile devices

Context Models

Context ModelContext model is one of the infrastructure and is essential for efficient manipulation of context information.Context model includes various categories and high complexityDue to comprehensibility and manageabilityEnables adaptation of complex architecture

It is important not only to define the range of context correctlyTo understand the characteristics of each context model

Context RepresentationKey-Value models: use a set of attributes and their associated values.Markup models: structure context into a hierarchy using tags.Graphical models: express relationships between context entities.Object-Oriented models: structure context into object classes and their implicit relationships.Logic models: express context in terms of facts and rules.Ontologies: combination of logic models and O-O models they structure context into object classes and their explicit relationships.AgendaBackgroundIntroductionCONON(Context Ontology)Ontology Reasoning & User Defined ReasoningExperiments & ComparisonConclusions & QuestionsIntroductionContext-awareness an important step in pervasive computingIncreasing need for developing formal context model to facilitateContext RepresentationContext SharingInteroperability of heterogeneous systemsIntroduction: Previous WorksVarious context data modelsContext Toolkit: Attribute-value TuplesCoolTown: Web based data modeleach object has a corresponding Web descriptionKaren et al: ER and UMLGaia: First-order pridicates written in DAML+OILHowever,None of them has addressed Formal knowledge sharingQuantitative evaluation for the feasibility of context reasoning in pervasive computing environments

Introduction: Whats in this paper?In this paper, the authors presentAn ontology-based formal context model to address critical issuesFormal context representationKnowledge sharingLogic based context reasoningDetailed design of their context model and logic based reasoning schemeQuantitative evaluation for context reasoning in pervasive computingWhy Ontology Model?OntologyThe shared understanding of some domainsOften conceived as a set of entities, relations, functions, axioms and instancesReasons for developing context models based on ontologyKnowledge sharingThe use of context ontology enables computational entities to have a common set of concepts about contextLogic InferenceContext aware computing can exploit various existing logic reasoning mechanismsKnowledge reuseWe can compose large-scale context ontology without starting from scratch

AgendaBackgroundIntroductionCONON(Context Ontology)Ontology Reasoning & User Defined ReasoningExperiments & ComparisonDiscussion & ConclusionsCONON: The Context OntologyFundamental: Location, User, Activity, Computational EntitySkeleton of contextAct as indices into associated informationUpper OntologyContext in each domain shares common conceptsEncourages the reuse of general conceptsProvides flexible interface for defining application-specific knowledgeCONON Upper Ontology

Specific Ontology for Home Domain

AgendaBackgroundIntroductionCONON(Context Ontology)Ontology Reasoning & User Defined ReasoningExperiments & ComparisonDiscussion & ConclusionsContext ReasoningThe authors present a smart phone scenarioE.g. when the user is sleeping in the bedroom or taking a shower in the bathroom, incoming calls are forwarded to voice mail boxThe use of context reasoning has two foldsChecking the consistency of contextDeducing high-level implicit context from low-level explicit contextTwo categories of context reasoningOntology reasoningUser-defined reasoningOntology Reasoning

Example: Ontology reasoning

User-defined Context Reasoning

AgendaBackgroundIntroductionCONON(Context Ontology)Ontology Reasoning & User Defined ReasoningExperiments & ComparisonDiscussion & ConclusionsExperiment

The prototype context reasoners are built using Jena2

Summary of Context Models

Adopted from : A Survey of Context Modeling By: Seungseok-Kang ;IDS LabComparison

Ontologies Evaluation

Based on usage of ontology languagesUsing ontology design principlesOntologies Evaluation

With respect to pervasive computing themes

Themes reference by reference ontology based modelsAgendaBackgroundIntroductionCONON(Context Ontology)Ontology Reasoning & User Defined ReasoningExperiments & ComparisonDiscussion & ConclusionsDiscussionThree major factorsSize of context informationComplexity of reasoning rulesCPU speedThe authors insist that it is feasible for non-time-critical applicationsFor time-critical applications such as security and navigating systemsWe need to control the scale of context dataset and the complexity of rule setOff-line manner static complex reasoning tasksDe-coupling context processing and context usage is needed in order to achieve satisfactory performanceThe design of context model should take account of scalability issue

QuestionsThe major factorsSize of context information Enhanced CoCA: heuristics (loading only relevant context data)Complexity of reasoning rulesCPU speed: Not our concernHow can we control the complexity of reasoning rules?We need to define the minimal set of rule languageExpressively powerful enough to be used in actual context-aware systemGuarantees acceptable performanceIs there a way of applying only relevant reasoning rules?What happen if the user-defined rule becomes no longer satisfied?Presented system doesnt consider

Technology Roadmap

ConclusionsOWL encoded context Ontology (CONON)Modeling context in pervasive computing environmentLogic based context reasoningUpper Ontology + Domain-specific OntologyPrototype implementation and ExperimentFeasible for non-time-critical applicationsDiscussion: what we need to care for time-critical applications

Center for E-Business TechnologyIDS Lab. Seminar - 37Thank you