ambient intelligence through ontologies l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Ambient Intelligence through Ontologies PowerPoint Presentation
Download Presentation
Ambient Intelligence through Ontologies

Loading in 2 Seconds...

play fullscreen
1 / 33

Ambient Intelligence through Ontologies - PowerPoint PPT Presentation


  • 144 Views
  • Uploaded on

Ambient Intelligence through Ontologies. Vassileios Tsetsos b.tsetsos@di.uoa.gr P-comp Research Group http://p-comp.di.uoa.gr . What is an ontology?. A formal , explicit specification of a shared conceptualization . (Studer 1998, original definition by Gruber in 1993)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Ambient Intelligence through Ontologies' - allie


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
ambient intelligence through ontologies

Ambient Intelligence through Ontologies

Vassileios Tsetsos b.tsetsos@di.uoa.gr

P-comp Research Group

http://p-comp.di.uoa.gr

what is an ontology
What is an ontology?
  • A formal, explicitspecification of a sharedconceptualization. (Studer 1998, original definition by Gruber in 1993)
  • Formal: it is machine-readable
  • Explicit specification: it explicitly defines concepts, relations, attributes and constraints
  • Shared: it is accepted by a group
  • Conceptualization: an abstract model of a phenomenon
what is an ontology3
What is an ontology?
  • Taxonomy, classification, vocabulary, logical theory, …
  • Concepts/classes, relations, properties/slots, instances/objects, restrictions/constraints, axioms, rules
heavyweight vs lightweight
Heavyweight vs. Lightweight
  • They differ in expressiveness, reasoning capabilities, complexity, decidability.
  • Lightweight
    • E-R diagrams, UML
  • Heavyweight
    • Description Logics, frames, first order logic
  • There are W3C standards for each case (RDF, RDF Schema, OWL)
  • We should choose carefully!
types of ontologies 1
Types of Ontologies (1)
  • Upper Level Ontologies
    • Describe very general concepts.
    • SUO (IEEE Standard Upper Ontology)
  • KR Ontologies
    • Representation primitives => Semantically- described grammars of ontology languages.
    • OKBC, OWL KR, RDF Schema KR
types of ontologies 2
Types of Ontologies (2)
  • Domain Ontologies
    • Are specializations of Upper Level Ontologies, reusable in a given domain (e.g., a generic ontology for smart environments)
    • Unified Medical Language System (UMLS)
  • Application Ontologies
    • They model all the knowledge required for a particular application (e.g., an ontology for a specific smart classroom)
some examples
Some examples
  • IEEE SUO
  • RDF(S) KR
many advantages
Many advantages
  • Provide formal model descriptions that allow reasoning
  • They support common queries:
    • Queries about the truth of statements (Is there a printer in room I9?)
    • Queries expecting an object to be returned (Where is John?)
  • Are quite scalable (especially Semantic Web ones)
  • Provide interoperability as they are agreed by a community (…at least this should be the case!)
  • SW ontology languages
    • are XML-based => XML advantages
    • have been standardized and are widely used
pervasive computing pc
Pervasive Computing (PC)
  • Computing paradigm that envisages:
    • Ubiquitous networking and service access
    • Intelligence
    • Intuitive HCI
    • Context-awareness
    • Seamless interoperation between heterogeneous agents
    • Privacy and Security
ontology applications in pc
Ontology applications in PC
  • Context modeling & reasoning
    • Context ontologies (location, time) which define structure and properties of contextual information
  • Semantic Web Services
    • Semantic description => automated discovery and matchmaking, composition, invocation, …
  • Semantic interoperability between heterogeneous systems (e.g., agents) through a shared set of concepts
  • Security and trust
cobra 1
CoBrA (1)
  • eBiquity Research Group, UMBC
    • http://ebiquity.umbc.edu
  • A broker-centric agent architecture that aims to reduce the cost and difficulties in building pervasive context-aware systems.
  • In this architecture, a Context Broker is responsible to:
    • Acquire & maintain contexts on the behalf of resource-poor devices & agents
    • Enable agents to contribute to and access a shared model of contexts
    • Allow users to use policy to control the access of their personal information
cobra 2
CoBrA (2)
  • Context Broker: maintains a model of the present context and shares this model of context knowledge with other agents, services and devices.
cobra ontologies
CoBrA ontologies
  • A set of ontologies that specialize the SOUPA Ontology.
  • They model the context and the processes of pervasive environments.
  • E.g., CoBrA Place
    • models different types of “Place” on a university campus
soupa 1
SOUPA (1)
  • Standard Ontology for Ubiquitous and Pervasive Applications (SOUPA)
    • eBiquity @ UMBC, http://pervasive.semanticweb.org
    • Written in OWL
gaia 1
Gaia (1)
  • A PC infrastructure for smart spaces
  • CORBA-based middleware for the management of Spaces
  • Ontologies written in DAML+OIL
gaia 2
Gaia (2)
  • Ontology Server: definitions of terms, descriptions of agents and meta-information about context available in a Space
  • Checks ontology consistency and provides maintenance
  • Semantic interoperability is performed through the common adoption of the same ontologies by all agents
  • Ontologies also help the developer to write inference rules or machine learning code in a generic way
other uses of ontologies in gaia
Other uses of ontologies in Gaia
  • Configuration management
    • New unknown entities may enter a Space
    • In earlier version: scripts & ad hoc configuration files
  • Semantic discovery with a FaCT Server
    • Semantic queries involve subsumption and classification of concepts
  • Context modeling
    • Context is modeled as predicates
    • e.g., temperature (room3,”-”,98F)
    • Ontologies describe the type and values of predicate arguments
  • Context-sensitive behavior
    • The developers can specify the behavior of the applications under certain contextual conditions through the supported ontologies.
the gaia infrastructure
The Gaia infrastructure

Gaia context infrastructure

The ontology infrastructure of Gaia

conon the context ontology
CONON: The context ontology
  • Extensible ontology comprised of:
    • Upper Level Ontology
    • Specific Ontology
  • Written in OWL
  • Enables DL reasoning (subsumption, consistency, instance checking, implicit context from explicit context) with OWL-Lite axioms
  • Enables First Order Logic reasoning (inference of higher level context) with user-defined rules
trust
Trust
  • SW entails a Web of Trust
  • PC requires ad-hoc soft-security models
  • Ontologies can model semantic networks of trusted entities and allow trust inference
  • Ontologies are used for the definition of (rule-based) Policy Languages
    • Rei, KAoS
trust inference
Trust inference
  • Directly connected nodes have known trust values
  • Trust for not directly connected nodes can be inferred with several algorithms:
    • Maximum and minimum capacity paths (~ the range of trust given by neighbors of X to Y)
    • Maximum and minimum length paths (~ how “far” is Y from X?)
    • Weighted average (~ recommended trust value for X to Y). It is a very complex algorithm!!! Why?
complexity of trust computation
Complexity of trust computation
  • Trust is affected by social, contextual and other ad hoc conditions
  • Example (on the subject of “AutoRepair”)
    • A distrusts B, B distrusts C => A trusts C?
      • A may want to trust C, because B distrusts C
      • If C cannot be trusted by B, A may distrust C even more
  • A complete solution: semantic descriptions of trusted entities and user-defined trust policies
foaf ontology
FOAF Ontology
  • Builds social networks
    • Individuals are described by name, e-mail, homepage, etc.
    • There are links between individuals
a trust ontology 1
A trust ontology (1)
  • Nine levels of trust (trustsHighly, distrustsSlightly, etc.)
  • Extending foaf:Person (1)

<Person rdf:ID="Joe">

<mbox rdf:resource="mailto:bob@example.com"/>

<trustsHighly rdf:resource="#Sue"/>

</Person>

a trust ontology 2
A trust ontology (2)
  • Extending foaf:Person (2)

<Person rdf:ID="Bob">

<mbox rdf:resource="mailto:joe@example.com"/>

<trustsHighlyRe>

<TrustsRegarding>

<trustsPerson rdf:resource="#Dan"/>

<trustsOnSubject rdf:resource="http://example.com/ont#Research"/>

</TrustsRegarding>

</trustsHighlyRe>

<distrustsAbsolutelyRe>

<TrustsRegarding>

<trustsPerson rdf:resource="#Dan"/>

<trustsOnSubject rdf:resource="http://example.com/ont#AutoRepair"/>

</TrustsRegarding>

</distrustsAbsolutelyRe>

</Person>

current and future work in p comp
Current and future work in P-comp
  • Semantic Web Services
  • Description Logics
  • Location modeling
  • Tools survey and experimentation
  • Meta-information for sensor data
  • Ontologies for medical applications
  • Any ideas???
location modeling 1
Location modeling (1)
  • Ontologies can map and interconnect different underlying spatial representations
  • This facilitates advanced reasoning and user-defined queries
  • A “location modeling team” is currently being formed to design and develop a system:
    • With human-centered, 3D indoor spatial representation
    • Which supports declarative and semantically-rich queries
    • Which supports mobile users and location prediction
    • Which seamlessly integrates different spatial representation approaches (set-based, graph-based, geometric)
location modeling 2
Location modeling (2)

This is actually a Domain Ontology

Queries

User Applications

(e.g., navigation)

Top-Level

Location Ontology

(Prediction-driven)

Events

Application

Ontology 1

Application

Ontology 2

Application

Ontology 3

Model Mapping

Engine 1

Model Mapping

Engine 2

Model Mapping

Engine 3

Explicit

Semantics

Oracle

Spatial

DOMINO

Location

Ontology

Repository

Different DB platforms, access terms, conceptual models

some open research issues
Some open research issues
  • Can they efficiently model sensor data?
  • Will the introduction of Probability elements improve their effectiveness? If yes, how can this be implemented?
  • Development of user-friendly tools and powerful & efficient reasoners
  • Automated ontology generation/extraction and easy ontology maintenance
further reading
Further reading
  • Ontological Engineering, Gómez-Pérez, Fernández-López, Corcho, 2004, Springer
  • Harry Chen et al., "SOUPA: Standard Ontology for Ubiquitous and Pervasive Applications", International Conference on Mobile and Ubiquitous Systems: Networking and Services, August 2004.
  • Harry Chen et al., "A Context Broker for Building Smart Meeting Rooms", Proceedings of the Knowledge Representation and Ontology for Autonomous Systems Symposium, 2004 AAAI Spring Symposium, March 2004.
  • Robert E. McGrath, Anand Ranganathan, Roy H. Campbell and M. Dennis Mickunas, Use of Ontologies in Pervasive Computing Environments
  • Xiao Hang Wang, et al., Ontology Based Context Modeling and Reasoning using OWL, Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004
  • Jennifer Golbeck, James Hendler, Trust Networks on the Semantic Web, WWW 2003
  • RDFWeb: FOAF: ‘the friend of a friend vocabulary’, http://rdfweb.org/foaf/