An Ontology for Context-Aware Pervasive Computing Environments

1
An Ontology for Context-Aware Pervasive Computing
Environments

• Harry Chen, Tim Finin, Anupam Joshi
• UMBC
• IJCAI 2003 - ODS

2
Overview

• 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

3
Computing Evolution
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The 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 Rules
6
Today Communication Rules
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Tomorrow Services Will Rule
Thank God! Pervasive Computing is here.
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One 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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Contexts

• 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 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 Systems
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The 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)

• 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 cant be directly
  acquired from the sensors
• Detect inconsistent knowledge that results from
  imperfect sensing

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Key Uses of OWL (2)

• Use OWL (RDF/XML) as the KR language for
  knowledge sharing
• Knowledge sharing gt 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 Architecture
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Context Broker Architecture
Pervasive Computing
Semantic Web
CoBrA
Software Agents
CoBrA not CORBA!
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Objectives

• 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 Birds Eye View of CoBrA
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An EasyMeeting Scenario
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An EasyMeeting Scenario
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CoBrA Research Roadmap
Jan 2003
Mar 2003
Jun 2003
An OWL reasoner built on Flora-2 (F-logic) in
XSB (Full RDF-S and OWL-Lite some OWL-DL)
A prototype of an intelligent meeting room built
on CoBrA
Ontologies for modeling contexts (114 Classes,
124 Properties)
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The 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)
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Versions of the Ontology

• Our paper describes version 0.2
• http//daml.umbc.edu/ontologies/cobra/0.2/cobra-on
  t
• 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 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 CoBrA
Place
AtomicPlace
CompoundPlace
Hallway
ParkingLot
Building
Stairway
Room
Campus
Restroom


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Places in CoBrA
Place
AtomicPlace
CompoundPlace
Hallway

AtomicPlaceInBuilding
Room
Restroom
Stairway
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Where is Harry?

• Premise (static knowledge)
• R210 rdftype 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 Sensors

• Premise (static knowledge)
• R210 rdftype AtomicPlace.
• ParkingLot-B rdftype 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 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-OWL
Premises
animalsJohn a animalsPerson. animalsMark a
animalsPerson animalshasFather
animalsJohn. animalshasFather
rdfssubPropertyOf animalshasParent. animalshasC
hild owlinverseOf animalshasParent.
Query
Who is Johns child? What classes does John
belong to? Who are the parents of Mark?
F-OWL Query
animals_JohnClass animals_hasChild -gt
X. animals_Mark animals_hasParent -gt X.
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More 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 Progress

• Adopting some censuses ontologies for modeling
  time and space (e.g., DAML spatial temporal
  ontology, Region Connection Calculus (RCC),
  Allens 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)

• The speaker doesnt 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) gt Yes!
• isLocated(RM223) gt I dont know!

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Privacy Policy Use Case (2)

• The problem of inference!
• Knowing your phone white pages gt I know where
  you live
• Knowing your email address (.mil, .gov) gt 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 Blueprints
B
Room Booker
(Semantic Web)
SOAP/OWL
Services
FIPA-ACL/OWL
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Conclusions
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Conclusions

• 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?

• Harry Chen
• http//umbc.edu/hchen4/
• Email harry.chen_at_umbc.edu
• eBiquity.ORG - a pervasive computing news portal
• http//ebiquity.org/