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The Senior Companion: a Semantic Web Dialogue System
d.¿email@example.com Roberta Catizone
firstname.lastname@example.org WeiWei Cheng
1. APPLICATION DOMAIN
The Senior Companion (SC) is a fully implemented Win-
dows application intended for intermittent use by one user
only (a senior citizen) over potentially many years. The
thinking behind the SC is to make a device that will give its
owner comfort, company, entertainment, and some practical
functions. The SC will typically be installed at home, either
as an application on a personal computer, or on a dedicated
device (like a Chumby1) or an intelligent coﬀee table (like
Microsoft’s Surface). By means of multimodal input and
output, and a graphical interface, the SC provides its ‘owner’
with diﬀerent functionalities, which currently include:
•conversing with the user about his personal photos
•learning about the user, user’s family, and life history
•telling the user jokes
•reading the news (via RSS feed from the internet)
Chatting about photos is currently the SC’s main activity.
This initial direction was chosen on the assumption that
senior citizens enjoy browsing photos and being reminded of
events and people from their lives. The SC will currently
also tell its owner jokes and read the news, if requested.
2. AGENT TECHNIQUES
The goals of the SC are more vague than those of most
dialogue systems, which are often focused on task fulﬁlment.
The overarching goal of the SC is to be a friendly, entertain-
ing, and useful companion for its owner. One subgoal of this
overarching goal is to encourage the user to talk about his
life, while using personal photos as a prompt. Another sub-
goal is for the SC to learn the details of signiﬁcant events in
the user’s life, so as to be able to construct a story or time-
line of the user’s life, and to be a knowledge source for other
family members (grandchildren, for example). Another sub-
goal is for the SC to learn about the user’s personal photos
∗Aﬃliation of all authors is Department of Computer Sci-
ence, University of Sheﬃeld, S1 4DP, UK.
Cite as: The Senior Companion: a Semantic Web Dialogue System,
D G Field, R Catizone, W Cheng, A Dingli, S Worgan, L Ye, Y Wilks,
gent Systems (AAMAS 2009), Decker, Sichman, Sierra and Castel-
franchi (eds.), May, 10–15, 2009, Budapest, Hungary, pp. XXX-XXX.
2008, International Foundation for Autonomous Agents and
Multiagent Systems (www.ifaamas.org). All rights reserved.
and what they depict, so that the SC can retrieve photos on
the basis of what they depict, to enhance conversation.
The agency of the SC is embodied in its dialogue man-
ager (DM). The DM uses the stack architecture (after 
and COMIC2) to manage dialogue, employing a set of hand-
crafted augmented transition networks we call ‘Dialogue Ac-
tion Forms’ (DAFs). DAFs are individuated by conversa-
tional topic—when the conversation moves to a new topic,
a new DAF is pushed onto the stack. Although the DAFs
are hand-crafted, their design is informed by data from a
spoken dialogue corpus,3and we are making preparations
to add decision theory to the DAFs to enable the DM to
make probabilistic decisions based on current context.
At login the SC speaks to the user, welcoming him, and
begins a conversation. The SC asks the user questions, re-
members some content from the user’s answers, and makes
statements. Some statements are motivated by inferences
the SC has made, and some come from a chatbot. User ut-
terances are processed ﬁrst as input to an Automatic Speech
Recogniser (ASR). The DM sends the output of the ASR
to the Natural Language Understander (NLU). We are us-
ing an Information Extraction approach to NLU exploiting
GATE plug-ins. Named Entity Recognition is a main fea-
ture of the NLU. In many cases, the DM sends the NLU
a speciﬁcation of the type of information it is expecting to
be contained in the user response. For example, the DM
might tell the NLU that it is looking for a family relation-
ship in the user’s utterance content. The information types
the NLU can recognise are as follows:
•person relationships (family and other)
•prepositional phrases that describe locations
•time phrases not containing an explicit date
•occasions (e.g., weddings, funerals, birthdays)
If the DM does not receive from the NLU the information
it is expecting at that point, it will typically apologise and
repeat the question to the user up to three times, or it will
invoke the chatbot.
3Collected under Companions .
Cite as: The Senior Companion: a Semantic Web Dialogue System,
Debora Field, Roberta Catizone, WeiWei Cheng, Alexiei Dingli, Simon
Worgan, Lei Ye, Yorick Wilks, Proc. of 8th Int. Conf. on Autonomous
Agents and Multiagent Systems (AAMAS 2009), Decker, Sichman, Sierra
and Castelfranchi (eds.), May, 10–15, 2009, Budapest, Hungary, pp. 1383 – 1384
Copyright © 2009, International Foundation for Autonomous Agents and
Multiagent Systems (www.ifaamas.org), All rights reserved.
AAMAS 2009 • 8th International Conference on Autonomous Agents and Multiagent Systems • 10–15 May, 2009 • Budapest, Hungary
em can recogn
he has changed his mind about something he has just said
(typically by using phrases like “Oh no” and “that’s not
right”). The system will then use a clariﬁcation routine to
ensure that it has understood the correct information.
Knowledge and reasoning
Information that the DM learns from a user is stored
as triples (binary predicates) in an RDF triplestore. The
knowledge base is essentially monotonic, with the exception
of the clariﬁcation routine just mentioned above, in which
the system replaces a fact it discovers is false with one it
now thinks is true.
The knowledge base also contains a small set of inference
rules describing family relationships. At the end of each user
utterance the DM calls a reasoner to infer new information.
The reasoner is called forwards to infer everything it can
about everything it knows. The newly inferred information
is then exploited by the DM in its subsequent utterances at
With regard to ascertaining the date when a particular
photo was taken, if the user uses a time phrase that is not an
explicit date (for example, “It was taken six years ago”), the
system invokes procedures for working out the approximate
date when the photo was taken.
As the conversation progresses, the system not only learns
about events and people in the user’s life, it also makes an
inventory of what is depicted in each photo, and it links the
photos and the things depicted in them to the facts being
stored in its knowledge base. In order to recognise that dis-
tinct objects are depicted in a photo, the system has to be
able to see the photo, at least in some limited sense. Cur-
rently the system is able to detect front-facing human faces
(using OpenCV4), but it cannot distinguish between faces,
and so cannot recognise people. We are, however, about
to replace this with a face recognition system developed by
Polar Rose.5We also intend to add the ability to recognise
other object types, such as monuments and landmarks.
The key innovation of the Senior Companion is the use
of Semantic Web technologies to build a dialogue system.
This approach was chosen in order to provide a seamless
join between a dialogue system and the internet, and there-
fore to maximise the potential for exploiting open access
knowledge when planning the content of a system’s conver-
sational utterances. Currently, the ways in which we exploit
the internet to enhance conversational abilities are:
•to enable easy access to one’s own or other people’s
personal digital photographs via Facebook.
•to look for tourist attractions near a place mentioned
by the user, so as to chat to the user about them.
•to invoke an online chatbot at appropriate points.
•to provide live, up-to-date news.
•to supply particular kinds of jokes.
The above are just the ﬁrst steps towards an internet-
driven dialogue system.
Other innovations under development include:
how to monitor and respond to user emotions during
conversation, and building that into the DM.
•using ML to derive dialogue structure from a corpus.
•using reasoning to guide the conversation towards the
system’s topics of greatest ignorance about the user
(which we call ‘grounding in the user’).
4. LIVE AND INTERACTIVE ASPECTS
The SC is a Windows application that runs on a personal
computer. To interact with the SC, a microphone is needed
for user input, and speakers for system output.
Before a user interacts with the SC some of his personal
photos must have been uploaded. They can either be stored
statically on the hard disk, or they can be uploaded from a
Facebook album via the internet.
The principal input modality is speech. The ﬁrst time
only that the user interacts with the SC, the SC leads him
through a ten-minute voice training session with the ASR.6
The user may also type his utterances into a text box,
which is provided mainly for error correction. For example,
if the ASR repeatedly misinterprets part of a user utterance,
and this leads to the DM not receiving anything useful from
the NLU, the user can type his utterance. (The user can see
from the interface when the ASR makes mistakes.)
A third input modality not fully exploited yet is touch.
The user can point on the touch-sensitive screen with an
electronic pen. He might typically do this when saying, for
example, “This is my sister”, while touching the image of
his sister on the screen. The DM knows the co-ordinates of
where the screen has been touched, and knows which areas
on a particular image depict faces. By aligning these, the
system will be able to exploit the user’s touch input in its
conversational utterances and inferences.
This work was funded by Companions , European Com-
mission Sixth Framework Programme Information Society
Technologies Integrated Project IST-34434.7
 H. Cunningham, D. Maynard, K. Bontcheva, and
V. Tablan. GATE: A framework and graphical
development environment for robust NLP tools and
applications, 2002. In Proc. 40th Anniversary Meeting
of the Association for Computational Linguistics
 O. Lemon, A. Bracey, A. Gruenstein, and S. Peters.
The WITAS multimodal dialogue system, 2003. In
Proc. Eu rospeech 20 03.
 Y. Wilks. Artiﬁcial companions, 2005. Interdisciplinary
Science Reviews , June, Vol. 30, pp. 145–152.
6The current ASR is Dragon Naturally Speaking.