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Semantic Knowledge and
Privacy in
the Physical Web
PRAJIT KUMAR DAS, ABHAY KASHYAP,
GURPREET SINGH, CYNTHIA MATUSZEK,
TIM FININ, ANUPAM JOSHI
UMBC ebiquity and IRAL Labs
Motivation
Our goal is to provide contextually
aware information, using the IoT,
that is privacy preserving and
ubiquitously helpful
Image courtesy Batman Wikia
CARLTON
Slide 2 of 44
IoT by Volume
Slide 3 of 44
IoT by
Domain
Slide 4 of 44
IoT by Privacy Concerns
Slide 5 of 44
Salient features
 CARLTON: A context-aware, NL question-answer BOT
 Context derived from the Physical Web (IoT)
 Under development, prototype system
 Simple NLP using tools like Stanford CoreNLP
 Mobile app and Kiosk for front-end
 ABAC privacy model, Privacy rules using SWRL
 Hierarchical context ontology
 Optional authentication for UMBC people
Slide 6 of 44
 Concretization of IoT
 Small, quick seamless
interactions with
physical objects and
locations with your
device
Physical web: What?
Slide 7 of 44
Physical web: What?
 Everything is a tap
away
Slide 8 of 44
Physical web: What?
 See what’s useful
around you
Slide 9 of 44
Physical web: What?
 Any object or place
can broadcast
content
Slide 10 of 44
Physical web: How?
Three main techniques
 Nearby Connections
Slide 11 of 44
Physical web: How?
Three main techniques
 Nearby Connections
 Nearby Notifications
Slide 12 of 44
Physical web: How?
Three main techniques
 Nearby Connections
 Nearby Notifications
 Nearby Messages
Slide 13 of 44
System Overview
Slide 14 of 44
System Overview
Slide 15 of 44
System Overview
Slide 16 of 44
Who is Tim
Finin?
System Overview
Slide 17 of 44
“Tim Finin”: Person Entity type
“Who”: WH query type
Text to Semi-Structured Text
Intent
Who is Tim
Finin?
System Overview
Slide 18 of 44
“Tim Finin”: Person Entity type
“Who”: WH query type
Text to Semi-Structured Text
Intent
SPARQL query generator
Context
Who is Tim
Finin?
System Overview
Slide 19 of 44
“Tim Finin”: Person Entity type
“Who”: WH query type
Text to Semi-Structured Text
Intent
SPARQL query generator
Context
Who is Tim
Finin?
Inference Engine
OntologyKnowledge base
System Overview
Slide 20 of 44
“Tim Finin”: Person Entity type
“Who”: WH query type
Text to Semi-Structured Text
Intent
SPARQL query generator
Context
Who is Tim
Finin?
Inference EngineResponse: JSON
{“text”: “He’s a
Professor in the
Computer Science
department!”}
OntologyKnowledge base
System Overview
Slide 21 of 44
Example query
Slide 22 of 44
Is this room
booked from
2PM-3PM?
Example query
Slide 23 of 44
User is a faculty and is in
front of Conf. room 1.
Is this room
booked from
2PM-3PM?
Example query
Slide 24 of 44
Conf. room 1 calendar has no
events during that time.
Is this room
booked from
2PM-3PM?
Example query
Slide 25 of 44
Is this room
booked from
2PM-3PM? No, would you like
me to book it from
2PM – 3PM?
Example query
Slide 26 of 44
Is this room
booked from
2PM-3PM? No, would you like
me to book it from
2PM – 3PM?
Yes, please!
Example query
Slide 27 of 44
Okay, the room
has been booked
in your name from
2PM – 3PM
Is this room
booked from
2PM-3PM? No, would you like
me to book it from
2PM – 3PM?
Yes, please!
Example query
Slide 28 of 44
Example query
Slide 29 of 44
Is Dr. Joshi here?
Example query
Slide 30 of 44
Is Dr. Joshi here?
User could mean Dr. A.
Joshi or Dr. K. Joshi.
Example query
Slide 31 of 44
Is Dr. Joshi here?
But user is in front of Dr. A.
Joshi’s office.
Example query
Slide 32 of 44
Is Dr. Joshi here?
User is an advisee of Dr. A.
Joshi
Example query
Slide 33 of 44
Is Dr. Joshi here?
Dr. Joshi is in a
meeting till
3PM
Example query
Slide 34 of 44
Example query
Slide 35 of 44
Example query
Where is Dr.
Finin’s office?
Slide 36 of 44
Example query
User is in CSEE building
Where is Dr.
Finin’s office?
Slide 37 of 44
Example query User is unknown to system
Where is Dr.
Finin’s office?
Slide 38 of 44
Example query
Where is Dr.
Finin’s office?
Please see CSEE
front desk for
required
information
Slide 39 of 44
Example1.
@prefix crltn:<https://www.ebiquity.org/ontologies/carlton/0.1>.
@prefix swrlb:<http://www.w3.org/2003/11/swrlb>.
crltn: student(?requester)∧
(
crltn: supervises(“Xavier”,?requester)∨
(crltn: affiliatedWith(?requester,?labName)∧crltn: leads(“Xavier”,?labName))
)∧
crltn: hasCurrentLocation(?requester,?aBldgLocation)∧
crltn: room(?aBldgLocation)∧crltn: sitsIn(“Xavier”,?aBldgLocation)∧
crltn: currentTime(?currTime)∧swrlb: Exists(?anEvent)∧crltn: speakingAt(“Xavier”,?anEvent)∧
(
(crltn: startTime(?anEvent,?eventStartTime)∧swrlb:
greaterThan(?eventStartTime,?currTime))∨
(crltn: endTime(?anEvent,?eventEndTime)∧swrlb: greaterThan(?currTime,?eventEndTime))
)∧crltn: hasCurrentLocation(“Xavier”,?aLocation)∧crltn: Location(?aLocation)∧
crltn: requestLocation(“Xavier”)
⇒
shareLocation(?aLocation)
Policy Example
Slide 40 of 44
supervises(“Xavier”,?requester)
OR
(
affiliatedWith(?requester,?labName)
AND
leads(“Xavier”,?labName)
)
Policy Example
Slide 41 of 44
hasCurrentLocation(?requester,?aBldgLocation)
AND
room(?aBldgLocation)
AND
sitsIn(“Xavier”,?aBldgLocation)
=>
shareLocation(?aLocation)
Policy Example
Slide 42 of 44
Future work
 Prototype system constantly adding conversations
 Beacons on robots
 Reason over robots near you
 How robots respond to instructions?
 “Can you take me to Prof. Matuszek now?”
 “Show me the way to the ITE 346 conference
room”
Slide 43 of 44
Summary
 We presented CARLTON
 A context-aware, NL question-answer BOT
 Context derived from the Physical Web (IoT)
 Semantic web technologies used to preserve data privacy
Thanks to NSF for the travel grant!
and
Thanks to Google for the gift of beacons!
Slide 44 of 44

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Semantic Knowledge and Privacy in the Physical Web

Hinweis der Redaktion

  1. We present Carlton, a context-aware, natural language, question-answer BOT that responds to ‘Helpdesk’ styled questions using Semantic Web technologies It leverages context derived from the Physical Web to protect privacy of organization’s and organizational entity’s data, by executing context-sensitive SWRL rules Carlton is a conversational Bot that uses the Internet of Things to respond to queries tying in information about the environment
  2. Roughly half of all consumers highly uncomfrotable with companies using and selling their data in physical spaces Altimeter is part of Prophet, a consultancy company that helps clients find better ways to grow. Altimeter, is a research and consulting firm that helps companies understand – and act on – digital disruption. Ratings of users in a range of 1-5, 1 being extremely uncomfortable and 2 being uncomfortable about the situation Source: Consumer perceptions of the Internet of Things based on 2062 respondents
  3. Carlton has a memory and remembers the question asked before the current one
  4. Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device. Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away. PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30 A city rent-a-bike service could enable users to sign up on the spot See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you. SpotCall HumaneSociety A dog collar could allow passerby to call a service to find the owner Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless. A bus that could alert users of its next stop A home appliance could offer an interactive tutorial. An industrial robot could display diagnostic information. A mall that could offer a map.
  5. Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device. Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away. PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30 A city rent-a-bike service could enable users to sign up on the spot See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you. SpotCall HumaneSociety A dog collar could allow passerby to call a service to find the owner Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless. A bus that could alert users of its next stop A home appliance could offer an interactive tutorial. An industrial robot could display diagnostic information. A mall that could offer a map.
  6. Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device. Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away. PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30 A city rent-a-bike service could enable users to sign up on the spot See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you. SpotCall HumaneSociety A dog collar could allow passerby to call a service to find the owner Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless. A bus that could alert users of its next stop A home appliance could offer an interactive tutorial. An industrial robot could display diagnostic information. A mall that could offer a map.
  7. Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device. Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away. PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30 A city rent-a-bike service could enable users to sign up on the spot See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you. SpotCall HumaneSociety A dog collar could allow passerby to call a service to find the owner Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless. A bus that could alert users of its next stop A home appliance could offer an interactive tutorial. An industrial robot could display diagnostic information. A mall that could offer a map.
  8. Uses BLE and ultrasonic modem to get a URL to a phone! Everything else is standard web technology or standard computing technology. Nearby Messages: Provides Publish Subscribe methods relying on proximity Nearby Connections: Enables local network real-time connect and exchange Nearby Notifications: Android feature associating website or app to beacon
  9. Uses BLE and ultrasonic modem to get a URL to a phone! Everything else is standard web technology or standard computing technology. Nearby Messages: Provides Publish Subscribe methods relying on proximity Nearby Connections: Enables local network real-time connect and exchange Nearby Notifications: Android feature associating website or app to beacon
  10. Uses BLE and ultrasonic modem to get a URL to a phone! Everything else is standard web technology or standard computing technology. Nearby Messages: Provides Publish Subscribe methods relying on proximity Nearby Connections: Enables local network real-time connect and exchange Nearby Notifications: Android feature associating website or app to beacon
  11. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  12. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  13. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  14. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  15. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  16. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  17. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  18. Nearby Messages API provides data from beacons to kiosks and Android phones Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations Context discovery of user and requester antecedent ⇒ consequent Antecedents: Requester context, Entity metadata, Entity context
  19. User Query Target Requester Location Additional context Response Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM? Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking AppointmentRequest class addition ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  20. User Query Target Requester Location Additional context Response Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM? Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking AppointmentRequest class addition ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  21. User Query Target Requester Location Additional context Response Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM? Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking AppointmentRequest class addition ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  22. User Query Target Requester Location Additional context Response Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM? Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking AppointmentRequest class addition ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  23. User Query Target Requester Location Additional context Response Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM? Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking AppointmentRequest class addition ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  24. User Query Target Requester Location Additional context Response Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM? Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking AppointmentRequest class addition ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  25. User Query Target Requester Location Additional context Response Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM? Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking AppointmentRequest class addition ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  26. User Query Target Requester Location Additional context Response Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  27. User Query Target Requester Location Additional context Response Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  28. User Query Target Requester Location Additional context Response Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  29. User Query Target Requester Location Additional context Response Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  30. User Query Target Requester Location Additional context Response Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  31. User Query Target Requester Location Additional context Response Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  32. User Query Target Requester Location Additional context Response Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  33. User Query Target Requester Location Additional context Response Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  34. User Query Target Requester Location Additional context Response Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  35. User Query Target Requester Location Additional context Response Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  36. User Query Target Requester Location Additional context Response Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  37. Complete privacy reasoning isomorphic to Truth maintenance Truth maintenance - if information is being added in a forward manner how do you ensure that your knowledge already stored are still true -