1. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: An Ontology for Context-Aware Pervasive Computing Environments Harry Chen, Tim Finin, Anupam Joshi UMBC IJCAI 2003 - ODS

2. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Computing Evolution …

4. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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.

5. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Yesterday: Gadget Rules Cool toys… Too bad they can’t talk to each other…

6. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Today: Communication Rules Sync. Download. Done. Configuration? Too much work…

7. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Tomorrow: Services Will Rule Thank God! Pervasive Computing is here.

8. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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 …

9. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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.

10. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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)

11. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: OWL in Context-Aware Systems

12. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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.

13. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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 can’t be directly acquired from the sensors Detect inconsistent knowledge that results from imperfect sensing

14. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Key Uses of OWL (2) Use OWL (RDF/XML) as the KR language for knowledge sharing Knowledge sharing => 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

15. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Context Broker Architecture

16. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Context Broker Architecture Semantic Web Pervasive Computing Software Agents CoBrA CoBrA not CORBA!

17. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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

18. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: A Bird’s Eye View of CoBrA

19. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: An EasyMeeting Scenario The broker detects Alice’s presence B    Policy says, “can share with any agents in the room” A B The broker builds the context model Web Alice “beams” her policy to the broker B Policy says, “inform my personal agent of my location” A B.. isLocatedIn..

20. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: An EasyMeeting Scenario Her agent informs the broker of her role and intentions + The broker tells her location to her agent A The projector agent wants to help Alice The projector agent asks slide show info. B The projector agent sets up the slides The broker informs the subscribed agents B

21. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: CoBrA Research Roadmap Jan 2003Mar 2003Jun 2003 F-OWL (v0.2)F-OWL (v0.3) EasyMeeting (v0.1) CoBrA-Ont (v0.1)CoBrA-Ont (v0.2) CoBrA-Ont (v0.3) Ontologies for modeling contexts (114 Classes, 124 Properties) A prototype of an intelligent meeting room built on CoBrA An OWL reasoner built on Flora-2 (F-logic) in XSB (Full RDF-S and OWL-Lite; some OWL-DL)

22. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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

23. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: The CoBrA Ontology (v0.2) Ontology for “Place” Ontology for “Agent” Ontology for “Agent’s Location Context” Ontology for “Agent’s Activity Context”

24. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Versions of the Ontology Our paper describes version 0.2 http://daml.umbc.edu/ontologies/cobra/0.2/cobra-ont 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…

25. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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

26. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Places in CoBrA Place CompoundPlace AtomicPlace Room Stairway Hallway Restroom ParkingLot Building Campus … …

27. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Places in CoBrA Place AtomicPlace Hallway ParkingLot AtomicPlaceNotInBuilding … RoomRestroomStairway AtomicPlaceInBuilding CompoundPlace

28. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Where is Harry? Premise (static knowledge): R210 rdf:type 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.

29. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Spotting Error in Sensors 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 during the same time interval. Conclusion: There is an error in the knowledge base.

30. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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.

31. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: An Example of F-OWL animals:John a animals:Person. animals:Mark a animals:Person ; animals:hasFather animals:John. animals:hasFather rdfs:subPropertyOf animals:hasParent. animals:hasChild owl:inverseOf animals:hasParent. Premises Query Who is John’s child? What classes does John belong to? Who are the parents of Mark? F-OWL Query animals_John:Class [animals_hasChild -> X]. animals_Mark [animals_hasParent -> X].

32. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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/

33. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Work In Progress Adopting some censuses ontologies for modeling time and space (e.g., DAML spatial & temporal ontology, Region Connection Calculus (RCC), Allen’s 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

34. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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 of ~1 km radius The broker: isLocated(UMBC) => Yes! isLocated(RM223) => I don’t know!

35. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: 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))

36. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: CoBrA Blueprints Mocha P4 PC (19.8x16.1x6.2cm, 1250g) B Services Room Booker SOAP/OWL P Personal Agent (FIPA/JADE) FIPA-ACL/OWL (Semantic Web)

37. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Conclusions

38. :: :: 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.

39. :: Ebiquity Research Group :: CSEE :: UMBC :: :: :: Questions? Harry Chen http://umbc.edu/~hchen4/ Email: harry.chen@umbc.eduharry.chen@umbc.edu eBiquity.ORG - a pervasive computing news portal http://ebiquity.org/