pervasive personalisation of location information: personalised context ontology william niu and...
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Pervasive Personalisation of Location Information: Personalised Context Ontology
William Niu and Judy KayIn Conference on Adaptive Hypermedia 200830 March 2009
What is an ontology?
Problems and motivation
User view: Adaptive Locator
Personalised Context Ontology (PECO)
User study
Results
Summary and conclusions
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Outline
What is an Ontology?
…an explicit specification of a conceptualization. [Gruber1993]
…a shared understanding of some domain of interest. [Uschold1996]
…an explicit specification of a conceptualisation that is shared within some domain of interest.
Plain English: concepts + relationships + common understanding
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Personalisation in pervasive computing environments is dynamic
Alice wants to find Bob for an urgent meeting
Alice wants to have ambient awareness
Same information may mean differently in different contexts
Room 125 is a common room
Room 125 is a coffee room for CHAI
Different contexts may require different representations
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Problems & Motivation
Need for personalised location descriptions
“Room 302”
“Mobile Personalisation Course Room”
“The room just outside level 3 west”
Problems & Motivation
Problems & Motivation
“The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” (Weiser 1991)
Need for explanation on the personalisation, esp in pervasive contexts
Bob knows Judy because…(they are colleagues)
Bob is familiar with the building because…(he has worked there for 2 years)
Need meaningful explanation
Previous Work
Valuable proposals of ontologies in PerCom, e.g.
SOUPA (Chen, Perich, Finin, Joshi 2004)
CONON (Gu, Wang, Pung, Zhang 2004)
UbisWorld Ontology (Heckmann 2005)
Our work
Middle Building Ontology (IUI 2007)
Conflict resolution (PERVASIVE 2008)
This paper explores PECO in delivering personalised and scrutable information in PerCom
Personalisation
Selection of relevant people
Personalised location label
Explanation of the personalisation
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User View: Adaptive Locator
PASTOR Framework
Building is-a FixedStructure
Room 300 is-part-of Level 3 West
SensorA detects Bob’s mobile
Sources
Domain-specific
Examples…
Building maps in SVG/XML:
Room 123 is-part-of Level 1 East
Sensors:
SensorA detects DeviceX
Email list:
Alice is-colleague-of Bob
Accretion and resolution approach
Personally meaningful place labels Alice => coffee room
Bob => recharging corner
…
Location telling:
Alice asks, “Where is Bob?”
Bob is at Judy’s office
Bob is at the common room, adjacent to the board room
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If Alice does not know where the common room is, but knows where the board
room is
Reasoning PECO with ONCOR
Hypotheses (about Adaptive Locator):
1. Personalisation is useful
2. Personalisation is correct
3. Understandable explanations
4. Users prefer the adaptive system
Comparing two systems
adaptive and non-adaptive
crossover, within-subject
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even with limited evidence sources and simple
ontological reasoning
User Study
8 participants
2 females and 6 males
2 staff members, 2 postgrads and 4 undergrads
All worked in the building
6 worked for 6+ months
1 for 4 months and 1 for 1 month
All computer scientists - but relevant population for the building
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Participants
Used a Web interface and two monitors
The task completion was assessed by logged informationand observation
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Experiment tasks and questions
Locator systems
Experimental Procedure
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Non-adaptive system Adaptive system
Where is William? (same)
Would you know William’s location from the description, “Desk 3W32”?
Would you know William’s location from the description, “his office”?
Who is on Level 4 East? (same)
Identify the people who you think should not have been displayed? Explain why not?
What is needed for the system’s reasoning of “his office”.
(ask users to see system explanations)
H1: Personalisation is useful
H2: Personalisation is correct
H3: Explanations are understandable
System tasks
8 questions in a 7-point Likert scale (1 is disagree)
Participants completed the tasks on both systems without significant time difference
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Post-study Questionnaire
Q1. The personalised system made a lot of mistakes in terms of selecting people of relevance Good correlation between users’ perception of and actual
system mistakes User A: “my need to find [hidden people] is less (often) than
those that did show up”
Q2. If the personalised system can correctly display the relevant people and hide the irrelevant ones, you would prefer to have that feature than not. 6.4 ± 0.2 (out of 7)
People liked less cluttered maps (4) and the ability to see hidden information (5)
Some (3) also wanted more control over the information: “I would probably prefer to click a ‘don’t show me this person again’…and initially show everyone.”
Q3. Personalised labels (e.g. “Bob's office”) are more helpful than room/desk numbers (e.g. “Desk 3W32”).
6.8 ± 0.2
One expressed that they felt it was easier to associate a place with something more meaningful (e.g. person, activity) than a number
Q4. If the personalised system can correctly display personalised location labels for the places you know about, you would prefer to have that feature. 6.6 ± 0.3
5 people explicitly expressed that they wanted both the place number and the personalised label to be displayed, e.g. Alice’s office (320)
Q5. Suppose you want to know Alice's location, but you have never been to her office. Without a map, you would prefer to see her location label as her office number.
5.6 ± 0.5
3 thought the personalised labels might still be more useful
Q6. The explanations generated by the system were understandable.
6.5 ± 0.2
3 people thought the explanation for hidden people should be elaborated more than “You do not appear to know Alice”
“The system says that I don't know him, but I don't know why. He seems to be sitting close to me (I can see him without getting up from my desk), he is in the same research group as me.”
2 people wanted the control to modify incorrect data
Q7. The explanations provided by the personalised system told you what you wanted to know.
6.4 ± 0.2
Similar response to last question:
4 wanted more information on hidden people
2 wanted more control over the data
Q8. It is important to have explanations for the personalisation.
6.6 ± 0.3
“…so that I know when things go wrong…But sometimes I don't care.”
Summary
~75% of accuracy in selecting people of relevance by inferring social networks with limited sources and simple ontological reasoning
Personalised interface was preferred, as long as hidden information is available via scrutiny
Personalised location labels (PLL) were preferred over room numbers, but most users wanted both
PLLs were sometimes still preferred, even when users did not know the location
Explanations were understandable and explained what they wanted to know
Restricted population in study
Conclusions
Ontological reasoning is promising in delivering personalised information in pervasive computing
A personal ontology can be used to generate understandable explanation of personalisation, a critical aspect in adaptive systems