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

2

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

3

4

Ontology of a Building: SIT Ontology

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

5

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

9

User View: Adaptive Locator

his desk

12

13

14

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

17

Postgrad

General public

Personalised Context Ontology

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

18

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

19

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

20

Participants

Used a Web interface and two monitors

The task completion was assessed by logged informationand observation

21

Experiment tasks and questions

Locator systems

Experimental Procedure

Start-up and Familiarisation Tasks

23

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

24

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

Acknowledgements

Thank all the experiment participants for their time and participation.

Smart Services CRC (formerly Smart Internet Technology CRC) partially funded this project.

Thank you!