=Paper=
{{Paper
|id=None
|storemode=property
|title=Ontologies for
Quantified Self: a Semantic Approach
|pdfUrl=https://ceur-ws.org/Vol-1210/LQS_01.pdf
|volume=Vol-1210
|dblpUrl=https://dblp.org/rec/conf/ht/CenaLRDM14
}}
==Ontologies for
Quantified Self: a Semantic Approach==
https://ceur-ws.org/Vol-1210/LQS_01.pdf
Ontologies for Quantified Self: a semantic approach
Federica Cena, Silvia Likavec, Alessandro Marcengo
Telecom Italia – Research and Prototyping
Amon Rapp, Martina Deplano Department
University of Turin
Via Reis Romoli 274, Torino, Italy
Corso Svizzera 185, Torino, Italy
alessandro.marcengo@telecomitalia.it
{cena,likavec,rapp,deplano}@di.unito.it
ABSTRACT places that the users have been to, things they have seen, how they
The spreading of devices and applications that allow people to sleep, how active they are, etc., creating a constant stream of data
collect personal information opens new opportunities for user that can reveal many aspects of their lives.
modeling (UM). In this new scenario UM together with personal However, today all these data are scattered in autonomous silos
informatics (PI) can offer a new way for self-monitoring that can and not integrated. UM techniques have the potential of
provide the users with a sophisticated mirror of their behavior, aggregating and correlating data not only coming from web
attitudes and habits and their consequences on their life, on the browsing but also provided by all these PI systems. A UM
environment and on contexts in which they live in. These new enriched with a plethora of personal data (behavioral,
forms of self-reflection and self-knowledge can trigger and psychological, physical and environmental), related to different
motivate the behavior change. In this paper we describe the first aspects of a person’s daily life, will be able to provide the user
step in this direction, focusing on opportunities offered by with a “mirror” of herself, a sophisticated representation of
semantic web ontologies for data integration and reasoning over interests, habits, activities in her life, in a novel way that is not yet
data for recommendation purposes. achieved by any of the personal informatics tools available today
[2]. This can support a new complex form of self-awareness and
Categories and Subject Descriptors self-knowledge, which could foster behavior change processes
[3], promoting more sustainable or healthier behavior,
H.5.m. Information interfaces and presentation (e.g., HCI):
discouraging bad habits, sustaining therapeutic improvement and
Miscellaneous
managing chronic diseases.
In this new scenario UM together with PI can offer a new way for
General Terms self-monitoring people’s own behavior, where self-monitoring
Languages.
refers to an assessment strategy to increase a person's awareness
of targeted behavior [4], in order to promote behavior change [5].
Keywords UM and PI can provide users with a sophisticated mirror of their
Ontologies, User model, Personal informatics, Quantified Self. behavior, attitudes and habits, highlighting their consequences on
their life, on the environment and on contexts in which they live
1. INTRODUCTION in, promoting a new form of self-reflection and self-knowledge
Personalized systems are used to meet individual preferences and that can trigger and motivate the behavior change.
needs of each specific user, thus tailoring the system response to Our goal is to design a sophisticated UM-based PI system which
these particular requirements. Personalized systems extrapolate can:
users’ interests and preferences from explicit user ratings and
from the observation of user behavior on the web: the system's i) gather heterogeneous types of user data (from PI systems'
assumptions about the user based on these observations are stored sensors, from social web activities, from user’s browsing
in a User Model (UM) [1]. A user model is the repository of behavior) and integrate them in an enhanced UM;
personal information that has the potential to drive personalization ii) reason on the gathered data in order to find aggregations and
and learning. The UM contains different types of information: correlations among data;
from user demographic data to domain-specific preferences data iii) provide users with recommendations and meaningful UM
(interest, knowledge…). visualizations to support self-awareness and self-knowledge.
On the other hand, Personal Informatics (PI), also known as The paper is structured as follows. We first present our solutions
Quantified Self (QS), is a school of thought which aims to use the and then we focus on semantic modeling of the domain in order
increasingly popular invisible technology means for acquiring and to allow data integration and reasoning.
collecting data on different aspects of the daily lives of people.
They allow users to self-track a variety of data about their own
behavior: these data can be, on the one hand, user physical states 2. STATE OF THE ART
(such as glucose level in the blood), psychological states (such as Traditionally, User Models (UMs) [1,6] have the following
mood), behavior (such as movements), habits (such as food features: (i) they are restricted to a single application; (ii) data are
intake, sleep); on the other hand, they can be environmental derived from the web; (iii) they concern short periods of time.
parameters (such as CO2 content, temperature) and contextual With the advent of ubiquitous computing technologies we are able
information (such as people meeting) of the places passed through to track and store large amounts of various personal information,
by the users during their everyday life. Thus, with this technology, scattered among applications and not integrated [7] even though it
we have the capability to automatically record at large scale the
is possible to integrate them with semantic web techniques [8]. In this paper we focus on data integration and reasoning over data
This project will advance the UM state of the art in the following: (points i) and ii)) exploiting opportunities offered by semantic
web ontologies [23]. Another challenging issue, namely gathering
• the integration of data derived from everyday life, in
user data, is out of scope of this paper
addition to the data derived from the web;
• reasoning on that data to gain further correlations about
user behavior. 4. ONTOLOGIES FOR QUANTIFIED SELF
In order to be able to:
The opportunity is related to obtaining a Lifelong user model that
stores user information for a long period of time and is able to integrate heterogeneous data coming from different devices and
manage user interest change [9]. This project is a first step in this sources
direction. reason on these data in order to provide meaningful visualization
and recommendation
According to [10], an ontology can be seen as a ‘‘formal, explicit
specification of a shared conceptualization’’. With explicit we design and develop three ontologies, modeling the three main
specifications of domain objects and their properties, as well as concepts of the Quantified Self world: time, place and user
the relationships between them, ontologies serve as powerful activities. Vital parameters such as weight, blood pressure or
formalisms for knowledge representation, providing exact blood sugar content are also important parameters, but we omit
semantics for each statement and avoiding semantic ambiguities. them from the preset analysis, since they are used primarily by
For these reasons, ontologies are often used for semantic data medical experts and are hard to analyze by ordinary people.
integration and for resolving semantic conflicts, as in Time ontology. We want to model the time from a user point of
[11,12,13,14,15]. Also, the associated rigorous mechanisms allow view, distinguishing work days, weekends and holidays (religious
for different forms of reasoning (for example, to deduce implicit and civil ones), as well as dividing each day into meaningful slots
classes), as in [16,17]. (morning, afternoon, evening, night).
Measuring users' daily affective experiences is an important way Place ontology. Again we want to model the place from a user
to quantify their life. In [18], the authors measure users' emotions perspective, labeling the places where the user lives, works or
at various moments throughout the day. They asked the users to does the activities, dividing them into indoor (school, house, gym,
answer demographic and general satisfaction questions, to work, cinema, restaurant, etc.) and outdoor (park, street..). (See
construct a short diary of the previous day, and then to answer Figure 1: Place ontology.)
structured questions about each episode. In [19], the authors
investigate digital recordings of everyday activities, known as
visual lifelogging, and elaborate the selection of target activities
for semantic analysis. They investigate the selection of semantic
concepts for life logging which includes reasoning on semantic
networks using a density-based approach.
Motivating behavior change towards a more active lifestyle is a
psychological, social and technological challenge. Several
Personal Informatics Systems have been developed in order to try
to modify a behavior by means of self-monitoring, such as
Figure 1: Place ontology
[20,21,22]
Activities ontology. We tried to model all the user activities,
dividing them into two main categories: activities with place
3. A NOVEL SEMANTIC PI SYSTEM change (such as transportation or sports with place change) and
We design a novel enhanced PI system, integrated in people’s activities with no place change (such as sports with no place
everyday lives, able to gather data in a transparent way and to change, intellectual activity, physical work, resting activity or
build and maintain a sophisticated user model able to aggregate feelings). Each of these classes has additional subclasses to better
data and provide meaningful visualization and personalized describe the performed activity, but we omit them from the
recommendations to the user for promoting behavior change. To picture for better clarity. For example, sports with place change
reach this goal, we need the following components: has as its subclasses running, cycling, kayaking or downhill
i) data integration of different user data for building a skiing, to name just a few. The design of this ontology was
sophisticated model of user behavior, habits, needs and motivated by the categorization of activities in “Moves”
preferences coming from different sources (web and real life application (https://www.moves-app.com). (See Figure 2:
behavior) Activities ontology.) For lack of space, we included feelings into
“Activities ontology”. We actually intend to have an additional
ii) advanced forms of reasoning on user data for correlating
different aspects of user daily behavior “Wellbeing and emotions ontology” to model user’s emotional
state and wellbeing, taking inspiration from [24].
iii) personalized feedback for triggering behavior change in the
users:
• recommendations triggered by the correlation of
different types of data (e.g., recommendations in
accordance with user behavior, attitudes and habits in
the UM)
• meaningful visualization of data for raising awareness
and motivating people in changing their behavior.
for modeling the core aspects of user behavior which would help
overcome these problems.
This work is still at its early stage. We aim at experimentally
evaluating our proposal by means of user tests to see short and
long term effects of recommendations and visualizations on user
behavior, as well as the acceptability of the solution.
6. ACKNOWLEDGMENTS
Our gratitude goes to Telecom Italia for their support.
Figure 2: Activities ontology 7. REFERENCES
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