:: eBiquity Research Group :: CSEE :: UMBC :: :: :: A Context Broker for Building Smart Meeting...

:: eBiquity Research Group :: CSEE :: UMBC ::

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A Context Broker for Building A Context Broker for Building Smart Meeting RoomsSmart Meeting Rooms

Harry Chen, Tim Finin, Anupam JoshiUniv. of Maryland, Baltimore County

AAAI Spring Symposium 2004

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OutlineOutline

• Introduction• Issues in building context-aware systems• How Semantic Web languages can help

• Background• The Semantic Web vision and ontologies

• Context Broker Architecture (CoBrA)• Approach, design, and prototypes

• Ongoing work & concluding remarks

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Computing Evolution …Computing Evolution …

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Pervasive ComputingPervasive Computing

Thank God! PerCom is

here…

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Intelligence is the KeyIntelligence is the Key

Sync. Download. Done.

Configuration? Too much

work…

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Context-Aware SystemsContext-Aware Systems

• Context-awareness is a key aspect of the intelligent pervasive computing systems

• Systems that can anticipate users’ needs and act in advance by “understanding” their context• A system that knows I am the speaker• A system that knows you are the audiences• A system that knows we are in a conference• …

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What’s Context?What’s Context?

• 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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Related WorkRelated Work

• Since the early 90’s, people have been interested in building context-aware systems• Olivetti: Call forwarding & teleporting systems …• Xerox PARC: Active map, PARC Tab …• Georgia Tech.: Context toolkit, cyberguide …• MIT: Office assistant, location-aware information

delivery, intelligent room …• UC Berkley: Context Fabric• UIUC: Gaia• HP Labs: Cooltown, CoolAgents …• …

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The Shortcomings of the The Shortcomings of the Previous SystemsPrevious Systems

• Lacking an adequate representation for context modeling and reasoning

• Individual agents are responsible for managing their own context knowledge

• Users often have no control over the information that is acquired by the sensors

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Research IssuesResearch Issues

• Context Modeling & Reasoning • How to represent context, so that it can be

processed and reasoned by the computers• Knowledge Maintenance & Sharing

• How to maintain consistent context knowledge and share that information with other systems

• User Privacy Protection• How to let users to control the sharing and the use

of their contextual information that is acquired by the hidden sensors

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Our Research ContributionsOur Research Contributions

• CoBrA: a broker-centric agent architecture for supporting pervasive context-aware systems• Using SW languages to define ontologies for

context modeling and reasoning• Using logic inference to interpret context and to

detect and resolve inconsistent knowledge• Allowing users to defined policies to control the

use of their contextual information

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Other ContributionsOther Contributions

• EasyMeeting: a smart meeting room prototype that exploits CoBrA• Providing relevant services and information

to meeting participants based on their situational needs

• Allowing users to control the use and the sharing their location and social context

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Semantic Web & OntologiesSemantic Web & Ontologies

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About the Semantic WebAbout the Semantic Web

• An extension to the present World Wide Web.• The focus is on enabling computing machines

to be able to reason about web information in addition to display web information. • NOTE: displaying information does not necessarily

require “deep” understanding of the information.• NOTE: in order to reason about information often

requires “deep” understanding of the information.

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The Current WebThe Current WebResource:

Identified by URI’sUntyped

Links:“href”, “src” …non-descriptive

Users:Exciting world - semanticsof resource, however, gleanedfrom content

Machine:Very little information available - significance of the links only evident fromthe context around the anchor

(adopted from Eric Miller’s presentation http://www.w3.org/2004/Talks/0120-semweb-umich/)

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The Semantic WebThe Semantic Web(adopted from Eric Miller’s presentation http://www.w3.org/2004/Talks/0120-semweb-umich/)

Resource:Globally identified by URI’sor locally scoped (blank)ExtensibleRelational

Links:Identified by URI’sExtensibleRelational

Users:Even more exciting world, richeruser experience

Machine:More processable informationis available (Data Web)

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The Semantic Web Layer CakeThe Semantic Web Layer Cake

we arehere

“The Semantic Web will globalize KR, just as the WWW globalize hypertext” -- Tim Berners-Lee

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Semantic Web OntologiesSemantic Web Ontologies

• Formally, an ontology is an explicit specification of a conceptualization.

• For the developers, building ontologies is about defining shared vocabularies and associated semantic relations• SonyEricsson T68i is a type of cellphone• All SonyEricsson T68i supports Bluetooth• Harry has a SonyEricsson T68i device• => Harry’s cellphone supports Bluetooth.

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• KR languages for defining ontologies• W3C Recommendations

• RDF/RDFS -- represents information as N-Triples (subject, predicate, object); supports basic class-subclass & properties.

• OWL (Web Ontology Language) -- adds more vocab. for describing classes and properties, cardinality, equality, XML datatypes, enumerations etc.

http://www.w3.org/2001/sw/Semantic Web LanguagesSemantic Web Languages

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OWL? Ontologies? But where?OWL? Ontologies? But where?

Pervasive computing is

great!

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How does OWL Help?How does OWL Help?ontologylanguage service

descriptionlang.

contextmodel

Interoplanguage

meta lang(policy)

XSLT/XMLfriendly

{ PerCom }

OWL provided a uniformed language which met many needs in developing a complex pervasive computing system.

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Context Broker ArchitectureContext Broker Architecture(CoBrA)(CoBrA)

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Context Broker ArchitectureContext Broker Architecture

SemanticWeb

PervasiveComputing

Software Agents

CoBrACoBrA

CoBrA not CORBA!

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

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Key Features of CoBrAKey Features of CoBrA• Using OWL to define ontologies for context

modeling and reasoning• COBRA-ONT, SOUPA -- Standard Ontology for

Ubiquitous & Pervasive Applications• Taking a rule based approach to interpret and

reason about context• Jena + Jess, Theorist (assumption-based

reasoning) • Using a policy language and engine to control

the sharing of user context• Rei -- a policy language that exploits denotic

concepts & speech acts (UMBC)

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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 ScenarioHer 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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Research Work in CoBrAResearch Work in CoBrA

CoBrAOntologies

ContextReasoner

PrivacyProtection

EasyMeetingPrototype

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The CoBrA Ontology (v0.4)The CoBrA Ontology (v0.4)

http://daml.umbc.edu/ontologies/cobra/0.4/

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Example 1: Location InferenceExample 1: Location Inference

• Goal: reason about a person’s location using the available sensing information.

=> Step 1: define a domain spatial ontology

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A Simple UMBC OntologyA Simple UMBC Ontology

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Location InferenceLocation InferenceAssume the broker is told that Harry is located in RM-201A

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Location InferenceLocation Inference

A: the used spatial relations are “rdfs:subProeprtyOf” the “inRegion” property

B: “inRegion” is of type “Transitive Property”

Based on A & B => …

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Location InferenceLocation Inference

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Example 2: Spotting a Sensor ErrorExample 2: Spotting a Sensor ErrorPremise (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 at the same time.

Conclusion: There is an error in the knowledge base.

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Context ReasonerContext Reasoner

Jena OWL/RDFSReasoner

KB(MySQL)

JESS Rule Engine

SensingInformation

ContextKnowledge

Context Broker

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EasyMeeting Prototype #1EasyMeeting Prototype #1

Room ECS201

BrokerJADE

BT Sensor

JADE The URL of Harry’s Policy (FIPA+N3)

Context information (FIPA + OWL-XML)HTTPServer

Harry’s Policy

MySQL

N-Triple + Jena + RDQL

CWMTomcat Server

N-Triple + Jena + RDQL

http://ebiquity.org/

http://users.ebiquity.org/~hchen4/download/msg1.txt

http://users.ebiquity.org/~hchen4/

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EasyMeeting Prototype #2EasyMeeting Prototype #2

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Work in ProgressWork in Progress

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Things that I’m working on…Things that I’m working on…

• Enhancing the broker’s reasoning• Implementing a policy-based privacy

protection mechanism• Building an Eclipse Plug-in for

monitoring the “brain” of the broker• Working with other researchers to

define a shared ontology for supporting PerCom applications.

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Enhancing the Reasoner; Enhancing the Reasoner; Adding Privacy ProtectionAdding Privacy Protection

• Using an assumption-based reasoner (called Theorist) to support default and abductive reasoning• Tries to explain the observed sensing

information by making hypotheses (abduction), and then predicts users’ future actions (defaults)

• Using the Rei policy language & engine to support privacy protection

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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 > ~1 km radius

• The broker:• isLocated(US) => Yes!• isLocated(Maryland) => Yes!• isLocated(BaltimoreCounty) => Yes!• isLocated(UMBC) => Yes!• isLocated(ITE-RM-201A) => 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 Eclipse Viewer (CEV)CoBrA Eclipse Viewer (CEV)

For exploring the knowledge and user policies that are stored in the Context Broker; for monitoring the broker’s reasoning process.

Inspired by the Java Spider application http://www.javaspider.org

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Building a Standard Ontology Building a Standard Ontology for Supporting PerCom Apps.for Supporting PerCom Apps.

• Standard Ontology for Ubiquitous and Pervasive Application (SOUPA)• Semantic Web in UbiComp SIG• http://pervasive.semanticweb.org/

• The bigger goal of SW-UbiComp SIG• Bring together SW+PerCom researchers• Exploring the use of ontologies in PerCom

http://pervasive.semanticweb.org/

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ConclusionsConclusions

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Semantic Web for PerComSemantic Web for PerCom

• Semantic Web languages & ontologies can facilitate knowledge sharing, context reasoning, and user privacy protection in a PerCom environment

• CoBrA is a new pervasive context-aware architecture that exploits the Semantic Web technologies

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Questions?Questions?

• CoBrA (ontologies, CEV, source code)• http://cobra.umbc.edu

• SW-UbiComp SIG• http://pervasive.semanticweb.org/

• PerCom news & development• http://www.ebiquity.org/

• Harry Chen• Google “Harry Chen”

http://cobra.umbc.edu/

http://pervasive.semanticweb.org/

http://www.ebiquity.org/

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