Ambient Intelligence through Ontologies
Vassileios Tsetsos [email protected]
P-comp Research Grouphttp://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)
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 ontology?
Taxonomy, classification, vocabulary, logical theory, … Concepts/classes, relations, properties/slots,
instances/objects, restrictions/constraints, axioms, rules
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)
Upper Level OntologiesDescribe very general concepts. SUO (IEEE Standard Upper Ontology)
KR OntologiesRepresentation primitives => Semantically-
described grammars of ontology languages. OKBC, OWL KR, RDF Schema KR
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)
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)
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
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)
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) Context Broker:
maintains a model of the present context and shares this model of context knowledge with other agents, services and devices.
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)
Standard Ontology for Ubiquitous and Pervasive Applications (SOUPA)eBiquity @ UMBC,
http://pervasive.semanticweb.orgWritten in OWL
Gaia (1)
A PC infrastructure for smart spaces CORBA-based middleware for the
management of Spaces Ontologies written in DAML+OIL
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
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.
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
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
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
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
Builds social networks Individuals are described by name, e-mail, homepage, etc. There are links between individuals
A trust ontology (1)
Nine levels of trust (trustsHighly, distrustsSlightly, etc.)
Extending foaf:Person (1)<Person rdf:ID="Joe">
<mbox rdf:resource="mailto:[email protected]"/>
<trustsHighly rdf:resource="#Sue"/>
</Person>
A trust ontology (2)
Extending foaf:Person (2)<Person rdf:ID="Bob">
<mbox rdf:resource="mailto:[email protected]"/><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
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)
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)Top-Level
Location Ontology
ApplicationOntology 1
ApplicationOntology 2
ApplicationOntology 3
Oracle Spatial
DOMINO LocationOntology
Repository
Model Mapping Engine 1
Model MappingEngine 2
Model Mapping Engine 3
ExplicitSemantics
User Applications(e.g., navigation)
QueriesThis is actually a Domain Ontology
(Prediction-driven) Events
Different DB platforms,
access terms, conceptual
models
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 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/
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