ESWC2011 Summer School: Front-end to the Semantic Web

“interface is the message”

on the path to a usable & personal Semantic Web

Lora Aroyo
VU University Amsterdam
@laroyo

Wednesday, June 1, 2011 1

o utline

front-end to semantics: how do we interact with SemWeb Apps?

personalization: what do we need to adapt to users?

example applications: what good & bad is out there?

evaluation: why is continuous evaluation so important?


why interfaces?

invisible computers

multitude of interaction modes

context-sensitive apps

networked devices: bridges between virtual & physical worlds

GUI become central

constantly increasing competition


take ho me message

combine content semantics with user context

integrate seamlessly physical & web worlds

identify relevance to user to rank & select information to present

continuous feedback cycle: to and from user

you need to deal with GUI on configuration level

perform continuous user testing

use real world data


“interface is the message”

Aaron Koblin: Artfully visualizing our humanity, TED Talk, 2011


FRONT-END TO SEMANTICS
how do we interact with the SemWeb Apps?


do SemWeb apps really d iffer?


semantics: what’s special?

explicit semantics (often from open sources, e.g. LOD) used
for system decisions and results

use facetted presentation, searching and browsing of
information

use typically classifications, typologies or other structures of
concepts

integrate data from different sources

aggregate data


credits: Dan Brickley


RDF data


interaction w ith semantics


http://twitpic.com/il1w/full ©
BBC
MMVIII


http://www.bbc.co.uk/programmes/b00c06n2.rdf


converting vocabularies


PERSONALIZATION
what do we need to adapt to us?


the user matters

when we consider interaction & interfaces, then the user plays a
key role

for good interface design, a good characterization of the user
is needed

first, some concept from theory and literature


user proﬁle

Definition: A ‘user profile’ is a data structure that represents a
characterization of a user (u) at a particular moment of time (t)

So, a user profile represents what (from a given (system)
perspective) there is to know about a user.
The data in a user profile can be explicitly given by the user or
have been derived.


user characteris tics

Personal data
Friend and relations
Experience
System access
Browsing history
Knowledge (learning)
Device data
Location data
Preferences


user mo del

Definition: The ‘user model’ contains the definitions and rules
for the interpretation of observations about the user and about
the translation of that interpretation into the characteristics in a
user profile.

So, a user model is the recipe for obtaining and interpreting user
profiles.


user mo deling

Definition: ‘user modeling’ is the process of creating user
profiles following the definitions and rules of the user model.
This includes the derivation of new user profile characteristics
from observations about the user and the old user profile based
on the user model.

So, user modeling is the process of representing the user.


stereotyping

Stereotyping is one example of user modeling.

A user is considered to be part of a group of similar people, the
stereotype.

Question: What could be stereotypes for conference participants
(when we design the conference website)?


user-adaptive system

Definition: A ‘user-adaptive system’ is a system that adapts itself to a
specific user.

Often, a user-adaptive system (or adaptive system, in short) uses user
profiles to base its adaptation on.
So, designing an adaptive system implies designing the user modeling.


user adaptation

User-adaptation is often used for personalization, i.e. making a
system appear to function in a personalized way.

Question: What user profile characteristics would be useful in
personalizing the conference’s registration site?
Question: How would you obtain those characteristics?


examples: user adaptation

Device-dependence
Accessibility (disabilities)
Location-dependence
Adaptive workflow

Question: Can you give concrete examples for interface adaptation,
both the adaptation effect as the prior user modeling necessary?


adaptive hyperme d ia

Well-studied example of adaptation is ‘adaptive hypermedia’: a
hypertext’s content and navigation are then adapted to the user’s
browsing of the hypertext.


DESIGNING INTERFACES


d ialog principles [Grice]

Be cooperative
Be informative
Be truthful
Be relevant
Be perspicuous (be clear)


UI principles [Shnei der mann]

Strive for consistency
Enable frequent users to use shortcuts
Offer informative feedback
Design dialog to yield closure
Offer simple error handling
Permit easy reversal of actions
Support internal locus of control
Reduce short-term memory load


usability heuristics [Nielsen]

Visibility of system status
Match between system and real world
User control and freedom
Consistency and standards
Error prevention
Recognition rather than recall
Flexibility and efficiency of use
Aesthetic and minimalist design
Help users recognize, diagnose and recover from errors
Help and documentation


all abo ut the user’s perspective

modeling the user: what are user’s preferences, interests, history,
activities, etc.

modeling the user’s context: e.g. location, time, device

which of all the data available is relevant
for this user in this context

also called context-aware


user’s context d is tribute d

switching between one context and another

doing things not only for him/herself, e.g. buying present for a
girlfriend


PERSONALIZED INTERACTION
s


interaction mo des

search, e.g. keyword, faceted

browse, story lines, narratives through collections

annotations of multimedia, e.g. (collaborative) tagging, professional
annotation of text, images and video, tagging games

explanations, hints, user feedback, e.g. explanation of
recommendation results, explanation of autocompletion suggestions


typical examples

recommendation systems, e.g. movies, music, art

user statistics and analysis, e.g. user usage data, profile, group
profiles, etc.

social networking


reco m mender systems

Definition: A ‘recommender system’ is a system that recommends to
a user, based on her individual interests, items that the user could find
interesting.

Examples: music, movies, people, restaurants
Types: collaborative (reason about similar users), content-based
(reason about similar items)
Problems: new users, new items, sparsity, gray sheep


reco m mender systems

movies & TV programs, e.g. Netflix, MovieLens, TiVo, personalized TV
guides

music, e.g. LastFM, Pandora, iTunes Genius

food & tourism, e.g. guides adapted to location, current time, preferences

news, e.g. Google reader, news filters

e-shopping, e.g. Amazon’s recommendations

advertisement, e.g. Facebook personalized ads

art, museums, e.g. personalized search, personalized museum guides


consi derations

Collection of activities/context/attention data

Derive interests from this data

Recommender-specific problems, e.g. cold start, over-specialization

Surface items of interest in the ‘long tail’

Cross-domain recommendations

Multi-person recommending

Granular control for users


user proﬁles & stats

overview of user preferences, e.g. settings, privacy

overview of user interests, e.g. ranking of interests, links to content

overview of user/group activities, e.g. per topics, per activity, per
date, over a period, overall

comparative views between users, e.g. LastFM, livingSocial movies
user similarity, Twitter similar users to you

different views/visualization over the same set of user data


social networking

professional networks & events, e.g. LinkedIn, Mendeley

people, organizations, e.g. Facebook, MySpace

Twitter

social bookmarking, e.g. Delicious, StumbleUpon, Diggit

GetGlue

Books, e.g. LibabryThing


EXAMPLE APPLICATIONS
Interfaces & Personalization on SemWeb


the big guys


The Recommendation and Like plugins let users share any content
they like back to their profile.


The Activity Feed plugin shows users what their friends are doing on
your site through likes and comments.


activity streams

http://xmlns.notu.be/aair/


weig hte d interest

http://xmlns.notu.be/wi


EXAMPLE 1
what do Gerrit Dou and Rembrandt have in common?

http://www.chip-project.org


enriched Rijksmuseum collection


mili<a teacher
of:
Ferdinand
Bol

teacher
of:
Nicolaes
Maes

self-‐portrait

teacher
of:
Gerrit
Dou
style:
Baroque

place:
Amsterdam,

1625
to
1650


goal & central role of UM


personalized experience
Personalized
Web
Access Online
Tour
Wizard Personalized
Mobile
Tour

Interactive tours
Semantic Search
Interactive user modeling
On-the-ﬂy adaptation
Museum tour maps
Recommendations of
artworks & art topics Synchronized user
Historic timeline
proﬁle


semantic recommendations


personalized tours


Interactive Museum Guide

h"p://chip-‐project.org


Interactive Museum Guide


event-based browsing


dynamic adaptation
For each artwork in the museum:

Related works

Include in the tour ( & recalculate the map/tour)

Indicate relevance in terms of e.g. personal interest, position, recommended by friends, by Rijks, on view

Rate to indicate interest

At any point of the tour:

Include/exclude artworks

Adjust tour length

Change navigation in and outside of the tour

Save for other tours


EXAMPLE 2
professionals vs. lay users on Web 2.0

semantic annotation of Rijksmuseum prints
http://e-culture.multimedian.nl/pk/annotate?
semantic tagging: http://waisda.nl


Autocompletion with multiple
vocabularies

http://slashfacet.semanticweb.org/wordnet/search
http://slashfacet.semanticweb.org/autocomplete/demos/


EXAMPLE 3
semantic television

http://notube.tv


watching TV in a group

for more details check out our blog at http://notube.tv


Environment Age
Interact with the second 15 - 35 years old
screen as a group
Friend interaction at home Type of Activities
Watching as a group quiz and betting games
change camera view
Synchronization information regarding the
TV & Second Screen content of the program
between second screens textual captions
between second screens &
TV show content provider Type of Program
Sports


observations



second screen & TV
functionalities
shared virtual space synchronization with second
voice dubbing screen
subtitles “overlay” on top of the main
related information TV-picture
quizzes censoring
voting & betting different camera views
scene-grab & share group alerts
social interaction
live-chat
parental advisory
uncensored version
different camera views


CONTINUOUS EVALUATION


CHIP users

Target users’ characteristics

small groups with 2-4 persons and a male taking the leading role
(67%)

middle-aged people in 30-60 years old (75%)

higher-educated (62%)

no prior knowledge about the Rijksmuseum collection (62%)

visit the museum for education (98%)


contextual analysis

Context
ual obse
rvations
Define familiarity with the
domain

s Define familiarity with
iew collections/vocabularies
ter v
r in
Use
Va Identify use cases
lid
ate
Identify navigation patterns
sks
Model user’s ta Identify requirements for
user groups


do main exploration


usability testing


results


http://www.cs.vu.nl/intertain/


take ho me message

combine content semantics with user context

integrate seamlessly physical & web worlds

identify relevance to user to rank & select information to present

continuous feedback cycle: to and from user

you need to deal with GUI on configuration level

perform continuous user testing

use real world data


ESWC2011 Summer School: Front-end to the Semantic Web

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