Semantic web in the context broker architecture
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Semantic Web in the Context Broker Architecture. Harry Chen, Tim Finin, Anupam Joshi Univ. of Maryland, Baltimore County PerCom 2004. Outline. Introduction Issues in building context-aware systems How Semantic Web languages can help Background The Semantic Web vision and ontologies

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Semantic web in the context broker architecture

Semantic Web in the Context Broker Architecture

Harry Chen, Tim Finin, Anupam Joshi

Univ. of Maryland, Baltimore County

PerCom 2004


Outline

Outline

  • 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


Computing evolution

Computing Evolution …


Pervasive computing

Pervasive Computing

Thank God! PerCom is here…


Intelligence is the key

Configuration?

Too much work…

Sync. Download. Done.

Intelligence is the Key


Context aware systems

Context-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


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.


Related work

Related 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 …


The shortcomings of the previous systems

The Shortcomings of the Previous 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


Research issues

Research 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


Our research contributions

Our 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


Other contributions

Other 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


Semantic web ontologies

Semantic Web & Ontologies


About the semantic web

About 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.


The current web

The Current Web

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

  • Resource:

    • Identified by URI’s

    • Untyped

  • 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


The semantic web

The 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)

    • Extensible

    • Relational

  • Links:

    • Identified by URI’s

    • Extensible

    • Relational

  • Users:

    • Even more exciting world, richeruser experience

  • Machine:

    • More processable informationis available (Data Web)


The semantic web layer cake

The Semantic Web Layer Cake

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

we arehere


Semantic web ontologies1

Semantic 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.


Semantic web languages

Semantic Web Languages

http://www.w3.org/2001/sw/

  • 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.


Owl ontologies but where

Pervasive computing is great!

OWL? Ontologies? But where?


How does owl help

How does OWL Help?

ontology

language

service

description

lang.

context

model

{ PerCom }

meta lang

(policy)

Interop

language

XSLT/XML

friendly

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


Context broker architecture cobra

Context Broker Architecture(CoBrA)


Context broker architecture

Context Broker Architecture

Pervasive

Computing

Semantic

Web

CoBrA

Software Agents

CoBrAnot CORBA!


A bird s eye view of cobra

A Bird’s Eye View of CoBrA


Key features of cobra

Key Features of CoBrA

  • Using OWL to define ontologies for context modeling and reasoning

  • Taking a rule based approach to interpret and reason about context

  • Using a policy language and engine to control the sharing of user context


An easymeeting scenario

The broker detects

Alice’s presence

Alice “beams” her

policy to the broker

Alice enters a conference room

»

B

B

»

»

Policy says,

“inform my personal

agent of my location”

The broker builds

the context model

Policy says,

“can share with any

agents in the room”

B

A

.. isLocatedIn ..

Web

B

A

An EasyMeeting Scenario


An easymeeting scenario1

The broker tells her

location to her agent

The broker informs

the subscribed agents

The projector agent

asks slide show info.

A

B

B

The projector agent

wants to help Alice

Her agent informs

the broker of her

role and intentions

The projector agent

sets up the slides

+

An EasyMeeting Scenario


Research work in cobra

Research Work in CoBrA

Context

Reasoner

PrivacyProtection

CoBrA

Ontologies

EasyMeeting

Prototype


The cobra ontology v0 4

The CoBrA Ontology (v0.4)

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


Example 1 location inference

Example 1: Location Inference

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

    => Step 1: define a domain spatial ontology


A simple umbc ontology

A Simple UMBC Ontology


Location inference

Location Inference

Assume the broker is told that Harry is located in RM-201A


Location inference1

Location Inference

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

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

Based on A & B => …


Location inference2

Location Inference


Example 2 spotting a sensor error

Example 2: Spotting a Sensor Error

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 at the same time.

Conclusion: There is an error in the knowledge base.


Context reasoner

Context Reasoner

Jena OWL/RDFS

Reasoner

Sensing

Information

KB

(MySQL)

Context

Knowledge

JESS Rule

Engine

Context Broker


Easymeeting prototype 1

BT Sensor

JADE

BrokerJADE

EasyMeeting Prototype #1

Room ECS201

MySQL

CWM

Tomcat Server

N-Triple + Jena + RDQL

N-Triple + Jena + RDQL

Context information (FIPA + OWL-XML)

HTTP

Server

Harry’s Policy

The URL of Harry’s Policy (FIPA+N3)


Easymeeting prototype 2

EasyMeeting Prototype #2


Work in progress

Work in Progress


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.


Enhancing the reasoner adding privacy protection

Enhancing the Reasoner; Adding 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


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…


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))


Cobra eclipse viewer cev

CoBrA Eclipse Viewer (CEV)

Inspired by the Java Spider application

http://www.javaspider.org

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


Building a standard ontology for supporting percom apps

Building a Standard Ontology 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


Conclusions

Conclusions


Semantic web for percom

Semantic 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


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”


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