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:: Ebiquity Research Group :: CSEE :: UMBC ::

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An Ontology for Context-Aware An Ontology for Context-Aware Pervasive Computing EnvironmentsPervasive Computing Environments

Harry Chen, Tim Finin, Anupam Joshi

UMBC

IJCAI 2003 - ODS


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OverviewOverview

• Introduction• Issues in pervasive context-aware systems

• OWL in context-aware systems• Key uses of OWL and SW ontologies

• Context Broker Architecture• CoBrA ontologies and use cases

• Conclusions


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Computing Evolution …Computing Evolution …


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The VisionThe Vision

• Pervasive Computing: a natural extension of the present human computing life style• Using computing technologies will be as

natural as using other non-computing technologies (e.g., pen, paper, and cups)

• Computing services will be something that is available anytime and anywhere.


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Yesterday: Gadget RulesYesterday: Gadget Rules

Cool toys…

Too bad they can’t talk to each other…


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Today: Communication RulesToday: Communication Rules

Sync. Download. Done.

Configuration? Too much

work…


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Tomorrow: Services Will RuleTomorrow: Services Will Rule

Thank God! Pervasive

Computing is here.


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One Step Towards the VisionOne Step Towards the Vision

• Context-aware systems: computer systems that can anticipate the needs of users and act in advance by “understanding” their context• Systems know I am the speaker• Systems know you are the audiences• Systems know we are in a meeting• …


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ContextsContexts

• By context, we mean the situational conditions that are associated with a user• Location, room temperature, lighting

conditions, noise level, social activities, user intentions, user beliefs, user roles, personal information etc.


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Research IssuesResearch Issues

• Context Modeling & Reasoning • How to build representations of context that can be

processed and reasoned about by the computers

• Knowledge Maintenance & Sharing• How to maintain consistent knowledge about the

context and share that information with other systems

• User Privacy Protection• How to give users the control of their situational

information (e.g., information acquired by the hidden sensors)


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OWL in Context-Aware SystemsOWL in Context-Aware Systems


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The OWL LanguageThe OWL Language

• A Semantic Web language for defining web ontologies (classes, properties, and restrictions), sponsored by W3C

• Extends the KR models defined in RDF & RDF-S.

• RDF/XML is the normative exchange format.


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Key Uses of OWL (1)Key Uses of OWL (1)

• Use OWL to define ontologies of context • people, devices, events, time, space etc.

• Use the ontology semantics of OWL to reason about context• Deduce context knowledge that can’t be

directly acquired from the sensors• Detect inconsistent knowledge that results

from imperfect sensing


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Key Uses of OWL (2)Key Uses of OWL (2)

• Use OWL (RDF/XML) as the KR language for knowledge sharing• Knowledge sharing => minimizing the cost of and

redundancy in context sensing

• Use OWL as a meta-language to define other languages that are used in context-aware systems• Policy languages for privacy and security• Content languages for agent communications


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Context Broker ArchitectureContext Broker Architecture


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Context Broker ArchitectureContext Broker Architecture

SemanticWeb

PervasiveComputing

Software Agents

CoBrACoBrA

CoBrA not CORBA!


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ObjectivesObjectives

• Developing an agent architecture to support pervasive context-aware systems• Provides ontologies for context modeling and

reasoning• Includes a logic inference engine to reason with

contextual information and to detect and resolve inconsistent context knowledge

• Defines a policy language that users can use to control the use and the sharing of their context information


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A Bird’s Eye View of CoBrAA Bird’s Eye View of CoBrA


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An EasyMeeting ScenarioAn EasyMeeting Scenario

Alice enters a conference room

The broker detects Alice’s presence

B

Policy says, “can share with any agents in the room”

A

B

The broker buildsthe context model

Web

Alice “beams” her policy to the broker

B

Policy says, “inform my personal agent of my location”

AB .. isLocatedIn ..


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An EasyMeeting ScenarioAn EasyMeeting Scenario

Her agent informs the broker of her

role and intentions

+

The broker tells herlocation to her agent

A

The projector agent wants to help Alice

The projector agentasks slide show info.

B

The projector agent sets up the slides

The broker informsthe subscribed agents

B


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CoBrA Research RoadmapCoBrA Research Roadmap

Jan 2003 Mar 2003 Jun 2003

F-OWL (v0.2) F-OWL (v0.3)

EasyMeeting (v0.1)

CoBrA-Ont (v0.1) CoBrA-Ont (v0.2) CoBrA-Ont (v0.3)

Ontologies for modeling contexts (114 Classes, 124 Properties) A prototype of an intelligent meeting room built on CoBrAAn OWL reasoner built on Flora-2 (F-logic) in XSB(Full RDF-S and OWL-Lite; some OWL-DL)


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The CoBrA OntologyThe CoBrA Ontology

• Goal: it attempts to capture a set of common ontologies for describing• People, places, devices, agents, services

and non-computing objects in an intelligent meeting room environment

• The properties and relationships between these entities and the environment


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The CoBrA Ontology (v0.2)The CoBrA Ontology (v0.2)

Ontology for “Place”

Ontology for “Agent”

Ontology for “Agent’s Location Context”

Ontology for“Agent’s Activity Context”


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Versions of the OntologyVersions of the Ontology

• Our paper describes version 0.2• http://daml.umbc.edu/ontologies/cobra/0.2/cobra-ont

• The latest version is 0.3• http://daml.umbc.edu/ontologies/cobra/0.3/

• What’ new in 0.3• Ontologies are grouped into 6 different OWL

documents • Added DAML-time ontology and FIPA device

ontology• Redo events and people ontologies• And more…


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An Example: Location ContextAn Example: Location Context

• Part 1: define vocabularies for talking about places on a university campus• OWL Classes: Campus, Building, Room,

Restroom, Hallway, Stairway etc.

• Part 2: define properties and relationships of different places• OWL Classes: AtomicPlace & CompoundPlace• OWL Properties: isSpatiallySubsumedBy &

spatiallySubsumes


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Places in CoBrAPlaces in CoBrA

Place

CompoundPlaceAtomicPlace

RoomStairway

Hallway

Restroom

ParkingLotBuilding

Campus

… …


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Places in CoBrAPlaces in CoBrA

Place

AtomicPlace

HallwayParkingLot

AtomicPlaceNotInBuilding…

Room Restroom Stairway

AtomicPlaceInBuilding

CompoundPlace


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Where is Harry?Where is Harry?

• Premise (static knowledge): • R210 rdf:type Room.• ECS-Building spatiallySubsumes R210.• ECS-Building isSpatiallySubsumedBy UMBC.

• Premise (dynamic knowledge)• Harry isLocatedIn R210.

• Conclusion:• Harry isLocatedIn AtomicPlaceInBuilding.• Harry isLocatedIn ECS-Building.• Harry isLocatedIn UMBC.


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Spotting Error in SensorsSpotting Error in Sensors• Premise (static knowledge):

• R210 rdf:type AtomicPlace.• ParkingLot-B rdf:type AtomicPlace.

• Premise (dynamic knowledge):• Harry isLocatedIn R210.• Harry isLocatedIn ParkingLot-B.

• Premise (domain knowledge):• No person can be located in two different

AtomicPlace during the same time interval.

• Conclusion:• There is an error in the knowledge base.


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F-OWL (v0.3)F-OWL (v0.3)

• F-OWL is an implementation of the OWL inference rules in Flora-2.• Flora-2 is an F-Logic (Frame Logic) based

language in XSB (Prolog).• F-Logic is an object-oriented knowledge

representation language.

• Similar to TRIPLE, F-OWL defines the ontology models in rules.


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An Example of F-OWLAn Example of F-OWL

animals:John a animals:Person.animals:Mark a animals:Person ; animals:hasFather animals:John.animals:hasFather rdfs:subPropertyOf animals:hasParent.animals:hasChild owl:inverseOf animals:hasParent.

Premises

QueryWho is John’s child? What classes does John belong to?Who are the parents of Mark?

F-OWL Queryanimals_John:Class [animals_hasChild -> X].animals_Mark [animals_hasParent -> X].


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More about F-OWLMore about F-OWL

• F-OWL is still under development.• F-OWL v0.3 (as of today) supports a full

RDF-S inference and limited OWL inference (OWL-Lite and some OWL Full).

• http://umbc.edu/~hchen4/fowl/


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Work In ProgressWork In Progress

• Adopting some censuses ontologies for modeling time and space (e.g., DAML spatial & temporal ontology, Region Connection Calculus (RCC), Allen’s temporal interval calculus)

• Implementing a rule based inference engine to reason about the temporal and spatial relations that are associated context events

• Using REI, a security policy language based on deontic concepts, to develop a policy-based systems to protect user privacy


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Privacy Policy Use Case (1)Privacy Policy Use Case (1)

• The speaker doesn’t want others to know the specific room that he is in, but does want others to know that he is present on the school campus

• He defines the following policies:• Can share my location with a granularity of ~1 km

radius

• The broker: • isLocated(UMBC) => Yes!• isLocated(RM223) => I don’t know!


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Privacy Policy Use Case (2)Privacy Policy Use Case (2)

• The problem of inference!• Knowing your phone + white pages => I

know where you live• Knowing your email address (.mil, .gov) =>

I know you works for the government

• The broker models the inference capability of other agents• mayKnow(X, homeAdd(Y)) :- know(X,phoneNum(Y))


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CoBrA BlueprintsCoBrA Blueprints

QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.

Mocha P4 PC

(19.8x16.1x6.2cm, 1250g)

B

Services

RoomBooker

SOAP/OWL

P

Personal Agent(FIPA/JADE)

FIPA-ACL/OWL

(Semantic Web)


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ConclusionsConclusions


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ConclusionsConclusions

• Semantic Web languages will play an important role in the future pervasive context-aware systems• It provides a means for modeling context and

reasoning about them. • It allows independently developed agents to share

context knowledge

• The Context Broker Architecture distinguishes itself from other frameworks in the use of Semantic Web technologies.


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Questions?Questions?

• Harry Chen• http://umbc.edu/~hchen4/• Email: [email protected]

• eBiquity.ORG - a pervasive computing news portal• http://ebiquity.org/

:: ebiquity research group :: csee :: umbc :: :: :: an ontology for context-aware pervasive...

Documents

ebiquity research group

context systems

context knowledge

computing evolution

vision contextaware

representations of context

ods slide

meeting slide