1. :: eBiquity Research Group :: CSEE :: UMBC :: :: :: A Context Broker for Building Smart Meeting Rooms Harry Chen, Tim Finin, Anupam Joshi Univ. of Maryland, Baltimore County AAAI Spring Symposium 2004

2. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Outline Introduction Issues in building context-aware systems How Semantic Web languages can help Background The Semantic Web vision and ontologies Context Broker Architecture (CoBrA) Approach, design, and prototypes Ongoing work & concluding remarks

3. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Computing Evolution …

4. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Pervasive Computing Thank God! PerCom is here…

5. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Intelligence is the Key Sync. Download. Done. Configuration? Too much work…

6. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Context-Aware Systems Context-awareness is a key aspect of the intelligent pervasive computing systems Systems that can anticipate users’ needs and act in advance by “understanding” their context A system that knows I am the speaker A system that knows you are the audiences A system that knows we are in a conference …

7. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: What’s Context? 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.

8. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Related Work Since the early 90’s, people have been interested in building context-aware systems Olivetti: Call forwarding & teleporting systems … Xerox PARC: Active map, PARC Tab … Georgia Tech.: Context toolkit, cyberguide … MIT: Office assistant, location-aware information delivery, intelligent room … UC Berkley: Context Fabric UIUC: Gaia HP Labs: Cooltown, CoolAgents … …

9. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: The Shortcomings of the Previous Systems Lacking an adequate representation for context modeling and reasoning Individual agents are responsible for managing their own context knowledge Users often have no control over the information that is acquired by the sensors

10. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Research Issues Context Modeling & Reasoning How to represent context, so that it can be processed and reasoned by the computers Knowledge Maintenance & Sharing How to maintain consistent context knowledge and share that information with other systems User Privacy Protection How to let users to control the sharing and the use of their contextual information that is acquired by the hidden sensors

11. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Our Research Contributions CoBrA: a broker-centric agent architecture for supporting pervasive context-aware systems Using SW languages to define ontologies for context modeling and reasoning Using logic inference to interpret context and to detect and resolve inconsistent knowledge Allowing users to defined policies to control the use of their contextual information

12. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Other Contributions EasyMeeting: a smart meeting room prototype that exploits CoBrA Providing relevant services and information to meeting participants based on their situational needs Allowing users to control the use and the sharing their location and social context

13. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Semantic Web & Ontologies

14. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: About the Semantic Web An extension to the present World Wide Web. The focus is on enabling computing machines to be able to reason about web information in addition to display web information. NOTE: displaying information does not necessarily require “deep” understanding of the information. NOTE: in order to reason about information often requires “deep” understanding of the information.

15. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: The Current Web Resource: Identified by URI’s Untyped Links: “href”, “src” … non-descriptive Users: Exciting world - semantics of resource, however, gleaned from content Machine: Very little information available - significance of the links only evident from the context around the anchor (adopted from Eric Miller’s presentation http://www.w3.org/2004/Talks/0120-semweb-umich/)

16. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: The Semantic Web (adopted from Eric Miller’s presentation http://www.w3.org/2004/Talks/0120-semweb-umich/) Resource: Globally identified by URI’s or locally scoped (blank) Extensible Relational Links: Identified by URI’s Extensible Relational Users: Even more exciting world, richer user experience Machine: More processable information is available (Data Web)

17. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: The Semantic Web Layer Cake we are here “The Semantic Web will globalize KR, just as the WWW globalize hypertext” -- Tim Berners-Lee

18. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Semantic Web Ontologies Formally, an ontology is an explicit specification of a conceptualization. For the developers, building ontologies is about defining shared vocabularies and associated semantic relations SonyEricsson T68i is a type of cellphone All SonyEricsson T68i supports Bluetooth Harry has a SonyEricsson T68i device => Harry’s cellphone supports Bluetooth.

19. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: KR languages for defining ontologies W3C Recommendations RDF/RDFS -- represents information as N-Triples (subject, predicate, object); supports basic class-subclass & properties. OWL (Web Ontology Language) -- adds more vocab. for describing classes and properties, cardinality, equality, XML datatypes, enumerations etc. http://www.w3.org/2001/sw/ Semantic Web Languages

20. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: OWL? Ontologies? But where? Pervasive computing is great!

21. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: How does OWL Help? ontology language service description lang. context model Interop language meta lang (policy) XSLT/XML friendly { PerCom } OWL provided a uniformed language which met many needs in developing a complex pervasive computing system.

22. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Context Broker Architecture (CoBrA)

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

24. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: A Bird’s Eye View of CoBrA

25. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Key Features of CoBrA Using OWL to define ontologies for context modeling and reasoning COBRA-ONT, SOUPA -- Standard Ontology for Ubiquitous & Pervasive Applications Taking a rule based approach to interpret and reason about context Jena + Jess, Theorist (assumption-based reasoning) Using a policy language and engine to control the sharing of user context Rei -- a policy language that exploits denotic concepts & speech acts (UMBC)

26. :: :: :: 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..

27. :: :: :: 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

28. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Research Work in CoBrA CoBrA Ontologies Context Reasoner Privacy Protection EasyMeeting Prototype

29. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: The CoBrA Ontology (v0.4) http://daml.umbc.edu/ontologies/cobra/0.4/

30. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Example 1: Location Inference Goal: reason about a person’s location using the available sensing information. => Step 1: define a domain spatial ontology

31. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: A Simple UMBC Ontology

32. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Location Inference Assume the broker is told that Harry is located in RM-201A

33. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Location Inference A: the used spatial relations are “rdfs:subProeprtyOf” the “inRegion” property B: “inRegion” is of type “Transitive Property” Based on A & B => …

34. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Location Inference

35. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Example 2: Spotting a Sensor Error 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 at the same time. Conclusion: There is an error in the knowledge base.

36. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Context Reasoner Jena OWL/RDFS Reasoner KB (MySQL) JESS Rule Engine Sensing Information Context Knowledge Context Broker

37. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: EasyMeeting Prototype #1 Room ECS201 Broker JADE BT Sensor JADE The URL of Harry’s Policy (FIPA+N3) Context information (FIPA + OWL-XML) HTTP Server Harry’s Policy MySQL N-Triple + Jena + RDQL CWM Tomcat Server N-Triple + Jena + RDQL

38. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: EasyMeeting Prototype #2

39. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Work in Progress

40. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Things that I’m working on… Enhancing the broker’s reasoning Implementing a policy-based privacy protection mechanism Building an Eclipse Plug-in for monitoring the “brain” of the broker Working with other researchers to define a shared ontology for supporting PerCom applications.

41. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Enhancing the Reasoner; Adding Privacy Protection Using an assumption-based reasoner (called Theorist) to support default and abductive reasoning Tries to explain the observed sensing information by making hypotheses (abduction), and then predicts users’ future actions (defaults) Using the Rei policy language & engine to support privacy protection

42. :: :: :: 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 > ~1 km radius The broker: isLocated(US) => Yes! isLocated(Maryland) => Yes! isLocated(BaltimoreCounty) => Yes! isLocated(UMBC) => Yes! isLocated(ITE-RM-201A) => I don’t know…

43. :: :: :: 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))

44. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: CoBrA Eclipse Viewer (CEV) For exploring the knowledge and user policies that are stored in the Context Broker; for monitoring the broker’s reasoning process. Inspired by the Java Spider application http://www.javaspider.org

45. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Building a Standard Ontology for Supporting PerCom Apps. Standard Ontology for Ubiquitous and Pervasive Application (SOUPA) Semantic Web in UbiComp SIG http://pervasive.semanticweb.org/ The bigger goal of SW-UbiComp SIG Bring together SW+PerCom researchers Exploring the use of ontologies in PerCom

46. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Conclusions

47. :: :: Semantic Web for PerCom Semantic Web languages & ontologies can facilitate knowledge sharing, context reasoning, and user privacy protection in a PerCom environment CoBrA is a new pervasive context- aware architecture that exploits the Semantic Web technologies

48. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: Questions? CoBrA (ontologies, CEV, source code) http://cobra.umbc.edu SW-UbiComp SIG http://pervasive.semanticweb.org/ PerCom news & development http://www.ebiquity.org/ Harry Chen Google “Harry Chen”