Ambient Intelligence through Ontologies

1. Ambient Intelligence through Ontologies Vassileios Tsetsos b.tsetsos@di.uoa.gr P-comp Research Group http://p-comp.di.uoa.gr

2. 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

3. What is an ontology? • Taxonomy, classification, vocabulary, logical theory, … • Concepts/classes, relations, properties/slots, instances/objects, restrictions/constraints, axioms, rules

4. 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!

5. 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

6. 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)

7. Some examples • IEEE SUO • RDF(S) KR

8. 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 • …

9. 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 • …

10. 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

11. Some “PC+Ontologies” projects • CoBrA • SOUPA • Gaia • Other

12. 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

13. CoBrA (2) • Context Broker: maintains a model of the present context and shares this model of context knowledge with other agents, services and devices.

14. 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

15. CoBrA Place Ontology

16. SOUPA (1) • Standard Ontology for Ubiquitous and Pervasive Applications (SOUPA) • eBiquity @ UMBC, http://pervasive.semanticweb.org • Written in OWL

17. SOUPA (2)

18. Gaia (1) • A PC infrastructure for smart spaces • CORBA-based middleware for the management of Spaces • Ontologies written in DAML+OIL

19. 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

20. 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.

21. The Gaia infrastructure Gaia context infrastructure The ontology infrastructure of Gaia

22. 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

23. 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

24. 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?

25. 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

26. FOAF Ontology • Builds social networks • Individuals are described by name, e-mail, homepage, etc. • There are links between individuals

27. 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>

28. 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>

29. 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???

30. 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)

31. 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

32. 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

33. 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/