ISWC 2012Semantic Reasoning in Context-Aware Assistive Environments             to Support Ageing with Dementia         Th...
OUR COMMUNITY‣   Ambient Assisted Living (AAL)‣   Deployment in Peacehaven nursing home (Singapore)                       ...
OUR COMMUNITYCHALLENGE‣   Limited scope of usability of existing solutions‣   Generic service framework to drive adoption ...
OUTLINE‣   Semantic technologies in the AAL use-case‣   First steps‣   Finding the appropriate reasoner      -   requireme...
SEMTECH CONTRIBUTION FOR AAL‣   Yet another Internet of Things + Semantic Web mix...‣   Some peculiarities      1. Modelin...
SEMTECH CONTRIBUTION FOR AAL1. MODELING ASSISTANCE IN SMART SPACE[KB skeleton]                                            ...
SEMTECH CONTRIBUTION FOR AAL1. MODELING ASSISTANCE IN SMART SPACE[KB skeleton]                                            ...
SEMTECH CONTRIBUTION FOR AAL‣   Yet another Internet of Things + Semantic Web mix...‣   Some peculiarities      1. Modelin...
SEMTECH CONTRIBUTION FOR AAL2. ENTITIES INTEGRATION: DISCOVERY & PLUG’N’PLAY                    End-user                  ...
SEMTECH CONTRIBUTION FOR AAL‣   Yet another Internet of Things + Semantic Web mix...‣   Some peculiarities      1. Modelin...
SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA              Context Producer             ...
SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA              Context Producer             ...
SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA              Context Producer             ...
SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA              Context Producer             ...
SEMTECH CONTRIBUTION FOR AAL‣   Yet another Internet of Things + Semantic Web mix...‣   Some peculiarities      1. Modelin...
SEMTECH CONTRIBUTION FOR AAL4. INTELLIGENCE: SEMANTIC INFERENCE‣   Imperative approach      -   Proof of concept in 2010  ...
SEMTECH CONTRIBUTION FOR AAL4. INTELLIGENCE: SEMANTIC INFERENCE‣   Imperative approach‣   Declarative approach     -   sep...
FIRST STEPS‣   OSGi - Java - Jena framework      -   fully featured APIs      -   poor integrated inference engine      - ...
THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣   Retractability of knowledge      -   contextual information is dynamic ...
THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣   Retractability of knowledge      -   contextual information is dynamic ...
THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣   Processing efficiency      -   pseudo-real-time constraints      -   hig...
THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣   Processing efficiency      -   pseudo-real-time constraints      -   hig...
THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣   Processing efficiency      -   pseudo-real-time constraints      -   hig...
THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON                             Jena             Pellet          RacerPro     ...
THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON                             Jena             Pellet          RacerPro     ...
THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON                             Jena             Pellet          RacerPro     ...
THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON                             Jena             Pellet          RacerPro     ...
THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON                             Jena             Pellet          RacerPro     ...
THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON                             Jena             Pellet          RacerPro     ...
THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON                             Jena             Pellet          RacerPro     ...
UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE                                                    ...
UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE      bundle auto-generation                        ...
UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE      bundle auto-generation                        ...
UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE      bundle auto-generation                        ...
UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE      bundle auto-generation                        ...
UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE         bundle auto-generation   1. profiles of user...
DEPLOYMENT RESULTSACCURACY & TIMELINESS‣   Accuracy of event recognition        Atomic events          34 times/day       ...
DEPLOYMENT RESULTSADAPTABILITY‣   Time to adapt the reasoning to a new deployment specificities                           i...
FUTURE WORK‣   Smart space composer      -   on site configuration by linked data generation‣   More powerful matching of c...
DISCUSSIONThibaut Tiberghien                  Image & Pervasive Access Lab               Doctoral Student     Internationa...
Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia
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Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia

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Robust solutions for ambient assisted living are numerous, yet predominantly specific in their scope of usability. In this paper, we de- scribe the potential contribution of semantic web technologies to building more versatile solutions — a step towards adaptable context-aware en- gines and simplified deployments. Our conception and deployment work in hindsight, we highlight some implementation challenges and require- ments for semantic web tools that would help to ease the development of context-aware services and thus generalize real-life deployment of se- mantically driven assistive technologies. We also compare available tools with regard to these requirements and validate our choices by providing some results from a real-life deployment.

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Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia

  1. 1. ISWC 2012Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia Thibaut Tiberghien, Mounir Mokhtari, Hamdi Aloulou, Jit Biswas Boston, MA, 15 Nov. 2012 www.ipal.cnrs.fr
  2. 2. OUR COMMUNITY‣ Ambient Assisted Living (AAL)‣ Deployment in Peacehaven nursing home (Singapore) iPhone/Android for nurse (3G) IPTV (Wi-Fi) Shower Bed Cupboard Nursing console (Wi-Fi) Shake sensor Passive infrared Speaker (Bluetooth) RFID bracelet on resident Pressure sensor Tiny debian machine ZigBee gateway RFID reader Wi-Fi router over ZigBee 2
  3. 3. OUR COMMUNITYCHALLENGE‣ Limited scope of usability of existing solutions‣ Generic service framework to drive adoption - interoperable services - reduce cost by sharing hardware & software resources - independent modules on top of Linked Data - atomic end-user services 3
  4. 4. OUTLINE‣ Semantic technologies in the AAL use-case‣ First steps‣ Finding the appropriate reasoner - requirements for AAL - pragmatic comparison‣ UbiSmart service platform, an integrated solution‣ Deployment results‣ Future work 4
  5. 5. SEMTECH CONTRIBUTION FOR AAL‣ Yet another Internet of Things + Semantic Web mix...‣ Some peculiarities 1. Modeling assistance in smart space 2. Entities integration: discovery & plug’n’play 3. Modules collaboration: shared model, linked data 4. Intelligence: semantic inference 5
  6. 6. SEMTECH CONTRIBUTION FOR AAL1. MODELING ASSISTANCE IN SMART SPACE[KB skeleton] runningFor usedBy Service Person helpsWith name repeat onDevice name id snoozeTime hasContext timeSent Context stage Device name needHands name hasAckService handheld Caregiver Resident hasSolvingContext busy stageForAlert Reminder Notification deployedIn watchesAfter Activity ackHandled Deviance escalateTo acknowledgement Location solved Legend Class data property object property subClassOf 6
  7. 7. SEMTECH CONTRIBUTION FOR AAL1. MODELING ASSISTANCE IN SMART SPACE[KB skeleton] runningFor usedBy Service Person helpsWith name repeat onDevice name id snoozeTime hasContext timeSent Context stage Device name needHands name hasAckService handheld Caregiver Resident hasSolvingContext busy stageForAlert Reminder Notification deployedIn watchesAfter Activity ackHandled Deviance escalateTo acknowledgement Location solved Legend Class data property 8 Service s, Resident r, Location l, Device dc, Activity a, Deviance da object property (r hasContext da) ^ (s helpsWith da) ) (s runningFor r) (s runningFor r) ^ (r locatedIn l) ^ (dc deployedIn l) ) (s onDevice dc) subClassOf (r hasContext a) ^ (a needHands true) ^ (dc handheld true) ) (dc fitted false) 6
  8. 8. SEMTECH CONTRIBUTION FOR AAL‣ Yet another Internet of Things + Semantic Web mix...‣ Some peculiarities 1. Modeling assistance in smart space 2. Entities integration: discovery & plug’n’play 3. Modules collaboration: shared model, linked data 4. Intelligence: semantic inference 7
  9. 9. SEMTECH CONTRIBUTION FOR AAL2. ENTITIES INTEGRATION: DISCOVERY & PLUG’N’PLAY End-user SOA software representation Services of plug&play agents Reasoning Engine (Context Awareness) OSGi container plug & play Sensors Devices Actuators 8
  10. 10. SEMTECH CONTRIBUTION FOR AAL‣ Yet another Internet of Things + Semantic Web mix...‣ Some peculiarities 1. Modeling assistance in smart space 2. Entities integration: discovery & plug’n’play 3. Modules collaboration: shared model, linked data 4. Intelligence: semantic inference 9
  11. 11. SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA Context Producer Sensor Knowledge Base [KB] Service Module based on Euler Delivery UI Parser User Interface Plasticity Context Module Acquisition Stream Module Interaction Handler Context Synthetiser Context Understanding Context ss Module Awarene Service Selection Module Service Service Service Service ment Service Manage Context Consumer 10
  12. 12. SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA Context Producer Sensor Knowledge Base [KB] Service Module based on Euler Delivery UI Parser User Interface Plasticity Context Module Acquisition Stream Module Interaction Handler Context Synthetiser Context Understanding Context ss Module Awarene Service Selection Module Service Service Service Service ment Service Manage Context Consumer 10
  13. 13. SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA Context Producer Sensor Knowledge Base [KB] Service Module based on Euler Delivery UI Parser User Interface Plasticity Context Module Acquisition Stream Module Interaction Handler Context Synthetiser Context Understanding Context ss Module Awarene Service Selection Module Service Service Service Service ment Service Manage Context Consumer 10
  14. 14. SEMTECH CONTRIBUTION FOR AAL3. MODULES COLLABORATION: SHARED MODEL, LINKED DATA Context Producer Sensor Knowledge Base [KB] Service Module based on Euler Delivery UI Parser User Interface Plasticity Context Module Acquisition Stream Module Interaction Handler Context Synthetiser Context Understanding Context ss Module Awarene Service Selection Module Service Service Service Service ment Service Manage Context Consumer 10
  15. 15. SEMTECH CONTRIBUTION FOR AAL‣ Yet another Internet of Things + Semantic Web mix...‣ Some peculiarities 1. Modeling assistance in smart space 2. Entities integration: discovery & plug’n’play 3. Modules collaboration: shared model, linked data 4. Intelligence: semantic inference 11
  16. 16. SEMTECH CONTRIBUTION FOR AAL4. INTELLIGENCE: SEMANTIC INFERENCE‣ Imperative approach - Proof of concept in 2010 - stable but not scalable “decision support rules - no iteration possible are not easily accessible to knowledge engineers for maintenance; new rules require programming resources to‣ Declarative approach implement rules and custom data fetches; deployments are not reusable across multiple systems.” 11 Goldberg, H. S., Vashevko, M., Postilnik, A., Smith, K., Plaks, N., & Blumenfeld, B. M. (2006). Evaluation of a commercial rule engine asa basis for a clinical decision support service. In AMIA Annual Symposium Proceedings (Vol. 2006, p. 294). American MedicalInformatics Association. 12
  17. 17. SEMTECH CONTRIBUTION FOR AAL4. INTELLIGENCE: SEMANTIC INFERENCE‣ Imperative approach‣ Declarative approach - separate knowledge & logic - learning curve & design phase - scalable, iteration possible - easier to maintain or reuse - better fitted for scenario-based deployments 13
  18. 18. FIRST STEPS‣ OSGi - Java - Jena framework - fully featured APIs - poor integrated inference engine - what alternatives?‣ Marko Luther et al. “choosing the appropriate combination of a reasoning engine, a communication interface and expressivity of the utilized ontology is an underestimated complex and time consuming task.” 22Luther, M., Liebig, T., Böhm, S., Noppens, O.: Who the Heck Is the Father of Bob? In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P.,Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 66–80. Springer,Heidelberg (2009) 14
  19. 19. THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣ Retractability of knowledge - contextual information is dynamic & temporal - ease to retract information: asserted & inferred 15
  20. 20. THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣ Retractability of knowledge - contextual information is dynamic & temporal - ease to retract information: asserted & inferred - opposition to RDF monotonicity assumption - Solutions: - higher complexity in reasoner’s language - no live state reasoner 15
  21. 21. THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣ Processing efficiency - pseudo-real-time constraints - highly dynamic data 16
  22. 22. THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣ Processing efficiency - pseudo-real-time constraints - highly dynamic data‣ Scalability - size of monitored space? room, building, smart city... - keep linked data - beware of the reasoner’s complexity 16
  23. 23. THE APPROPRIATE REASONER?REQUIREMENTS GATHERING‣ Processing efficiency - pseudo-real-time constraints - highly dynamic data‣ Scalability - size of monitored space? room, building, smart city... - keep linked data - beware of the reasoner’s complexity‣ Quality of Information 16
  24. 24. THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON Jena Pellet RacerPro Euler Consistency incomplete ✓ ✓ ✓ Rule format own SWRL own, powerful N3Logic Retract info ✓ ✕ ✓ ✕ Ease of use average (manual) easy complex easy 100 783ms 442ms ~ 310ms 4ms Speed 1000 29,330ms 38,836ms ~ 44,166ms 40ms 10 000 out of memory 436ms Scalability ✕ ~ ✕ ✓ Size (download) 22.3Mb 24.3Mb 60.3Mb 12.9Mb Licensing free, open-source free, open-source $, closed-source free, open-source 17
  25. 25. THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON Jena Pellet RacerPro Euler Consistency incomplete ✓ ✓ ✓ Rule format own SWRL own, powerful N3Logic Retract info ✓ ✕ ✓ ✕ Ease of use average (manual) easy complex easy 100 783ms 442ms ~ 310ms 4ms Speed 1000 29,330ms 38,836ms ~ 44,166ms 40ms 10 000 out of memory 436ms Scalability ✕ ~ ✕ ✓ Size (download) 22.3Mb 24.3Mb 60.3Mb 12.9Mb Licensing free, open-source free, open-source $, closed-source free, open-source 17
  26. 26. THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON Jena Pellet RacerPro Euler Consistency incomplete ✓ ✓ ✓ Rule format own SWRL own, powerful N3Logic Retract info ✓ ✕ ✓ ✕ Ease of use average (manual) easy complex easy 100 783ms 442ms ~ 310ms 4ms Speed 1000 29,330ms 38,836ms ~ 44,166ms 40ms 10 000 out of memory 436ms Scalability ✕ ~ ✕ ✓ Size (download) 22.3Mb 24.3Mb 60.3Mb 12.9Mb Licensing free, open-source free, open-source $, closed-source free, open-source 17
  27. 27. THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON Jena Pellet RacerPro Euler Consistency incomplete ✓ ✓ ✓ Rule format own SWRL own, powerful N3Logic Retract info ✓ ✕ ✓ ✕ Ease of use average (manual) easy complex easy 100 783ms 442ms ~ 310ms 4ms Speed 1000 29,330ms 38,836ms ~ 44,166ms 40ms 10 000 out of memory 436ms Scalability ✕ ~ ✕ ✓ Size (download) 22.3Mb 24.3Mb 60.3Mb 12.9Mb Licensing free, open-source free, open-source $, closed-source free, open-source 17
  28. 28. THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON Jena Pellet RacerPro Euler Consistency incomplete ✓ ✓ ✓ Rule format own SWRL own, powerful N3Logic Retract info ✓ ✓ ✕ ✓ ✓ ✕ Ease of use average (manual) easy complex easy 100 783ms 442ms ~ 310ms 4ms Speed 1000 29,330ms 38,836ms ~ 44,166ms 40ms 10 000 out of memory 436ms Scalability ✕ ~ ✕ ✓ Size (download) 22.3Mb 24.3Mb 60.3Mb 12.9Mb Licensing free, open-source free, open-source $, closed-source free, open-source 17
  29. 29. THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON Jena Pellet RacerPro Euler Consistency incomplete ✓ ✓ ✓ Rule format own SWRL own, powerful N3Logic Retract info ✓ ✓ ✕ ✓ ✓ ✕ Ease of use average (manual) easy complex easy 100 783ms 442ms ~ 310ms 4ms Speed 1000 29,330ms 38,836ms ~ 44,166ms 40ms 10 000 out of memory 436ms Scalability ✕ ~ ✕ ✓ Size (download) 22.3Mb 24.3Mb 60.3Mb 12.9Mb Licensing free, open-source free, open-source $, closed-source free, open-source 17
  30. 30. THE APPROPRIATE REASONER?A PRAGMATIC COMPARISON Jena Pellet RacerPro Euler Consistency incomplete ✓ ✓ ✓ Rule format own SWRL own, powerful N3Logic Retract info ✓ ✓ ✕ ✓ ✓ ✕ Ease of use average (manual) easy complex easy 100 783ms 442ms ~ 310ms 4ms Speed 1000 29,330ms 38,836ms ~ 44,166ms 40ms 10 000 out of memory 436ms Scalability ✕ ~ ✕ ✓ Size (download) 22.3Mb 24.3Mb 60.3Mb 12.9Mb Licensing free, open-source free, open-source $, closed-source free, open-source 17
  31. 31. UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE Sensors Devices ZigBee Wi-Fi/3G/BT HomeControlService Euler (API) DeviceManager ServiceControl ReminderService WSMS Apache Felix container 18
  32. 32. UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE bundle auto-generation Sensors Devices ZigBee Wi-Fi/3G/BT HomeControlService Euler (API) DeviceManager ServiceControl ReminderService WSMS ors ns Se Apache Felix container 18
  33. 33. UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE bundle auto-generation Sensors Devices ZigBee DPWS Wi-Fi/3G/BT EnvironmentDiscovery HomeControlService Euler (API) DeviceManager ServiceControl ReminderService WSMS ors ns Se Apache Felix container 18
  34. 34. UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE bundle auto-generation Sensors Devices ZigBee DPWS Wi-Fi/3G/BT EnvironmentDiscovery HomeControlService Euler (API) DeviceManager ServiceControl ReminderService WSMS ors es ns vic Se De Apache Felix container 18
  35. 35. UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE bundle auto-generation Sensors Devices ZigBee DPWS Wi-Fi/3G/BT EnvironmentDiscovery HomeControlService Euler (API) DeviceManager ServiceControl ReminderService WSMS rules.n3 & query.n3 ors es ns vic environment.n3 Se De skeleton.n3 input.n3 Apache Felix container 18
  36. 36. UBISMART SERVICE PLATFORMINTEGRATION OF A REASONER IN OUR ARCHITECTURE bundle auto-generation 1. profiles of users, devices, sensors 2. context information, devices status 3. selected service and device 4. start/stop service on a device 5. services status Sensors Devices 6. service instantiation on device ZigBee DPWS Wi-Fi/3G/BT EnvironmentDiscovery HomeControlService Euler (API) DeviceManager ServiceControl ReminderService WSMS rules.n3 & query.n3 ors es ns vic environment.n3 Se De 3 skeleton.n3 1 input.n3 5 2 6 4 Apache Felix container 18
  37. 37. DEPLOYMENT RESULTSACCURACY & TIMELINESS‣ Accuracy of event recognition Atomic events 34 times/day 71% Complex events 7 times/day 70%‣ Timeliness Euler module Networking Misc. modules Total 1.226s 0.735s 0.752s 2.713s 19
  38. 38. DEPLOYMENT RESULTSADAPTABILITY‣ Time to adapt the reasoning to a new deployment specificities imperative platform semantic platform first implementation ~5 days a few months new deployment ~3 days a few hours 20
  39. 39. FUTURE WORK‣ Smart space composer - on site configuration by linked data generation‣ More powerful matching of context with services - needs parametric context description‣ Handling of quality of information - reasoning with uncertainty of contexts‣ Cohabitation of rule-base inference and AI algorithms - context classification from linked data 21
  40. 40. DISCUSSIONThibaut Tiberghien Image & Pervasive Access Lab Doctoral Student International joint research unit - UMI CNRS 2955 thibaut.tiberghien@ipal.cnrs.fr www.ipal.cnrs.fr

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