Computing with Perceptions for the Linguistic Description of Complex
Phenomena through the Analysis of Time Series Data
A. Ramos-Soto, A. Bugar
´
ın and S. Barro
Research Center on Information Technologies (CiTIUS), University of Santiago de Compostela,
R
´
ua de Jenaro de la Fuente Dom
´
ınguez, s/n, 15782, Santiago de Compostela, Spain
1 RESEARCH PROBLEM
Nowadays, new technologies allow acquiring and
archiving vast volumes of data about time-evolving
phenomena in many crucial areas such as economy,
science, and industrial processes. Examples in econ-
omy include the evolution of every kind of econom-
ical indicators at local or global levels, like stock
funds, electricity/gas/water consumption, price of ba-
sic products, etc. In science, the amount of infor-
mation collected by researchers is overwhelming and
ever growing, including astronomical observations by
radio telescopes, space probes, etc. and data collected
from experiments in diverse scientific fields.
In order to be useful, this data must be exploited
and explained in an understandable way, reporting
facts, advice or commands to be performed that use
the available background knowledge about the phe-
nomenon under study. These objectives can only be
achieved by using natural language, especially if the
final information is to be provided by a non-expert.
This is clear for example in the case of financial news-
papers and scientific publications, in which data is not
simply made accessible or summarized as graphics
and tables, but arguments and conclusions need to be
explained using natural language.
However, there is a lack of tools and means for
processing and interpreting all this data using com-
puters. Any organization of data as provided by a
computer, either in a numerical, categorical, and/or
graphical form, is just a tool that can be employed by
human experts to produce an explanation in natural
language.
Understandable linguistic descriptions of phe-
nomena are provided by human experts, while com-
puters just provide flexibility in storing and accessing
data. In fact, it is becoming easier and easier to col-
lect data, but providing a human being with expertise
on a certain domain remains difficult and expensive.
This situation clearly poses a problem, since the ra-
tio data/human experts is growing dramatically as a
consequence. In summary, there is a clear need for
computational systems able to produce automatically
linguistic descriptions of data about phenomena.
More specifically, the task of generating easily un-
derstandable information for people using human lan-
guage has been addressed by two fields which, inde-
pendently until now, have researched the processes
this task involves: the natural language generation
(Reiter and Dale, 2000) and the linguistic descriptions
of data (Zadeh, 1996).
The natural language generation field focuses its
efforts on automatically obtaining texts, with the pur-
pose of them being as much as possible indistinguish-
able from the ones created by humans. The linguistic
descriptions of data field, which originates in the soft
computing domain, provides summaries or descrip-
tions from data sets using linguistic concepts which
deal with the imprecision and ambiguity of language
through the use of fuzzy sets.
In this context, we propose in this Ph.D. to re-
search on the linguistic descriptions of data field, cov-
ering a group of soft computing-based concepts and
techniques, such as linguistic variables, fuzzy opera-
tors and quantification methods. For instance, using
this kind of solutions, we can obtain quantified sen-
tences such as “most of the students are good” or A
few days with high humidity the temperature is low”.
In fact, most of the approaches for building lin-
guistic descriptions described in the literature make
use of the concept of “quantified sentence”. In this
sense, the linguistic description approaches make use
of two different types of quantified sentences: type
I (“Q of X are A”), as in “several dogs are brown”,
and type II (“Q of D are A”), as in “a few young re-
searchers have published relevant papers”, where X is
a finite crisp set, Q is a linguistic quantifier and A, D
are fuzzy properties defined over X (Delgado et al.,
2014).
Despite its formal nature and its orientation to-
wards providing meaningful information from data,
as of today the linguistic descriptions of data field
has to face several problems as a novel research do-
main. First, the sole use of quantified sentences is
3
Ramos-Soto A., Bugarín A. and Barro S..
Computing with Perceptions for the Linguistic Description of Complex Phenomena through the Analysis of Time Series Data.
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
usually not enough to address real life linguistic de-
scription problems. In this sense several approaches
have studied the extension of quantified sentences to
deal with time series data or to provide reasoned de-
scriptions (see next Section), but their full potential is
still unexplored. Secondly, linguistic descriptions do
not directly provide textual descriptions ready for hu-
man consumption, but rather a set of several linguis-
tic properties which can be assigned to a certain con-
cept or entity. Consequently, in order to use linguis-
tic descriptions in real contexts we do not only need
expressiveness, but also a way of translating this ex-
pressiveness into tangible solutions for actual human
users. Another issue related to the previous problem
is that linguistic descriptions approaches have been
proposed from both a formal and practical point of
view, but applications or systems that make use of
these techniques in real life environments are almost
nonexistent.
In this context, we are developing this Ph.D. in
order to research the use of linguistic descriptions ad-
dressing several of the issues that this field currently
poses. In fact, we believe that by both providing
real solutions employing linguistic descriptions and
extending the current theoretical base to consider a
higher expressiveness, linguistic descriptions can be-
come a very useful tool as a more human-friendly al-
ternative to other widely used descriptive techniques,
such as statistics or data mining approaches.
2 OUTLINE OF OBJECTIVES
The objectives of this Ph.D. are both practical and the-
oretical. On one hand we intend to explore and ad-
dress linguistic description needs in real-life contexts.
On the other hand, based on the experience gathered
in the practical use cases, we intend to achieve a gen-
eral linguistic description model which can be used to
easily develop linguistic description solutions for any
application domain. More specifically, we will:
Study each application domain and create a cor-
pus of commonly used linguistic expressions.
Extend the computational theory of perceptions
(Zadeh, 2000) to include the representation needs
previously identified, so that new linguistic re-
sources can be contemplated, such as hypothesis,
causality, speculation, conclusions, as well as new
kind of quantifiers and operators at a syntactic-
semantic level, which allow to establish spatio-
temporal relationships among the occurrence of
events.
Research quality assessment criteria for linguistic
descriptions. These allow to determine the quality
degree of candidate linguistic descriptions in an
objective way.
Create search procedures which select the most
relevant descriptions according to the application
domain expert’s preferences. These procedures
may be heuristic (where, in an ad hoc way, the
expert knowledge is directly included as part of
the search procedure) or meta-heuristic (where an
optimization function including the expert knowl-
edge guides the search process), depending on the
problem characteristics. In the latter case, genetic
programming seems an appropriate strategy, since
it is oriented to learn grammar instances, which
can be used to structure and represent the target
linguistic descriptions.
Research validation methodologies to verify the
robustness of the linguistic description generation
procedures and the subjective quality of the lin-
guistic descriptions. Although the objective crite-
ria used in the search procedures allow to obtain
good quality descriptions, these have a limited va-
lidity in this context, due to the high number of
possible answers and the subjectivity in their as-
sessment (situation complexity, divergent expert
criteria, etc.).
Ultimately develop a generic linguistic descrip-
tion model which considers and formalizes all
the previous described aspects. This model will
be implemented as a software library which may
serve as the kernel engine of a linguistic descrip-
tion generator authoring tool.
3 STATE OF THE ART
The linguistic descriptions (or summaries) of data
(LDD) aim to obtain informative, brief and precise
descriptions from numeric datasets and cover a group
of soft computing-based concepts and techniques,
such as linguistic variables, fuzzy operators and quan-
tification methods. It is a novel field, whose so-
lutions provide information in the form of linguis-
tic terms. Specifically, although preliminar ideas ap-
peared early in the 1980s, it started to develop in the
second half of the 1990s, when the advances in the
field of fuzzy sets (namely computing with words and
computing with perceptions) provided new potential
applications, mostly oriented to data mining. Due to
its short career and its formal background, many ap-
proaches in this field are on the theoretical side, al-
though in some cases practical examples and real life
based problems have been addressed.
ICAART2015-DoctoralConsortium
4
3.1 Theoretical Work
(Yager, 1982) and (Yager et al., 1990) define the basic
linguistic summaries and serve as the starting point
in the characterization of linguistic descriptions, in-
troducing the concept of quantified sentences as data
summaries. Years later, (Zadeh, 1996), (Zadeh, 2000)
and (Zadeh, 2001) introduce the concept of com-
puting with words (CWW) and computing with per-
ceptions (CWP) and highlight the potential of the
fuzzy logic to provide a methodology to this concept,
including examples which show how this approach
could be structured. More recently, (Kacprzyk and
Zadrozny, 2005), (Kacprzyk and Zadrozny, 2010) and
(Kacprzyk, 2010) introduce some ideas about a poten-
tial relationship between computing with words and
the natural language field, but do not explore them in
depth.
From the ideas and concepts proposed in the pre-
vious contributions, the construction of a linguistic
description framework which can be applied to any
kind of description problem in any domain is perhaps
one of the biggest challenges in this field, but it is
still far from being achieved. In this sense, the Gran-
ular Linguistic Model of a Phenomenon (Menendez-
Gonzalez and Trivino, 2011), which has been used
as a solution for several practical cases in diverse
domains (Menendez-Gonzalez and Trivino, 2011),
(Alvarez-Alvarez and Trivino, 2013), (Eciolaza and
Trivino, 2011), is the nearest approach there is to an
all-in-one framework. It is based on a set of inter-
connected nodes named perception mappings (PM),
which receive a set of computational perceptions (CP)
as input. Each PM applies an aggregation function to
the input CP (for example minimum, maximum, aver-
age or even fuzzy rules) and generates a new CP as a
result which can be reused as input to other PM. Fig-
ure 1 shows an example of a GLMP model which de-
termines and describes types of climate from temper-
ature and humidity input data. A are linguistic label
partitions and W vector of fuzzy fulfillment degrees
associated to each label. R
1
...R
n
is a set of fuzzy rules.
1PM
temperature
1PM
humidity
2PM_climate
26 º
86 %
A = (cold,normal,hot)
W = (0,0.3,0.7)
A = (low,normal, high)
W = (0,0.2,0.8)
R1: If temperature is
hot and humidity is
high then climate is
tropical
....
Rn: If temperature is
hot and humidity is
low then climate is
desertic
Currently we have a
tropical weather (truth
degree: 0.7)
Figure 1: Example of a GLMP model which determines the
type of climate from temperature and humidity data inputs.
Other recent contributions explore the use of dif-
ferent quantifiers and develop evaluation criteria for
quantified sentences. For example, (Diaz-Hermida
and Bugarin, 2011) with several theoretical aspects
such as the use of semi-fuzzy quantifiers to model
quantified sentences and the description of some
generic methods for pattern detection. Furthermore,
(D
´
ıaz-Hermida et al., 2011), (Castillo-Ortega et al.,
2012), (Wilbik and Keller, 2012) and (Menendez and
Trivino, 2012) define several and mostly coincident
evaluation criteria, such as the data coverage percent-
age or the sentence fuzzy fulfillment degree. Oth-
ers were inspired by the conversational maxims in
the field of human communication (Gamut, 1991), in-
cluding the relevance or the length of the description.
In fact, when referring to criteria, it can be stated that
there is a solid consensus about which characteristics
of a linguistic description can be useful in the task of
evaluating and ranking candidate descriptions in an
objective way.
3.2 Use Cases and Practical
Contributions
In (Castillo-Ortega et al., 2011a), the concept of lin-
guistic summary applied to temporal data series is
given, which must fulfill the brevity, precision and
data coverage criteria. A few algorithms to obtain
linguistic summaries are presented. The given exam-
ple is made on data about the patient inflow in med-
ical centers, from which summaries such as “Most
of the days with cold weather” or “patient inflow is
low or very low” are obtained. This use case was
also explored in (Castillo-Ortega et al., 2011b) using
a genetic algorithm approach instead of the standard
heuristic algorithms used to generate the linguistic de-
scriptions.
In (Kobayashi and Okumura, 2009), a specific
application oriented to the economic domain is de-
scribed. Nikkei data time series are used, from which
several pattern profiles which take into account the
curvature and trend of the data series are detected to
produce summaries about the evolution of the market
in a given date. These descriptions were compared
with news reports about that evolution. The obtained
descriptions are composed of simple sentences such
as “At the end of the session the prices decreased”.
(Kacprzyk and Wilbik, 2009) orients the use of
linguistic descriptions to temporal series comparison,
with the objective of helping human decision taking
in an effective way, in this case related to economic
investments. The kind of sentences obtained include
variation patterns, such as Among all y, most are con-
stant”, Among all medium y, most are constant” or
Among all moderate y, most are medium and con-
ComputingwithPerceptionsfortheLinguisticDescriptionofComplexPhenomenathroughtheAnalysisofTimeSeries
Data
5
stant”.
In (van der Heide and Trivino, 2009), the prob-
lem of generating linguistic descriptions for domestic
electric consumption is addressed. This work high-
lights the potential that linguistic descriptions have
for an electricity company in order to provide cus-
tomers with customized information above mere nu-
merical data. In this case, intuitive descriptions are
given, such as About two thirds of the days the con-
sumption in the mornings is lower than the consump-
tion in the afternoons”, “Most of the days the con-
sumption in the mornings is lower than the consump-
tion in the evenings” or “About two thirds of the days
the consumption in the middays is lower than the con-
sumption in the evenings”.
Based on the GLMP model, (Eciolaza and Triv-
ino, 2011) automatically produces linguistic descrip-
tions of driving activity from vehicle simulator data.
(Alvarez-Alvarez and Trivino, 2013) also employs
GLMP together with fuzzy finite state machines, to
create a basic linguistic model of the human gait and
to generate a human friendly linguistic description of
this phenomenon focused on the assessment of the
gait quality, including rules which allow to provide
explanations to the descriptions as in “28 days after
the knee lesion, the gait quality is very low because
the gait symmetry is low and the gait homogeneity is
low”. (Menendez-Gonzalez and Trivino, 2011) uses
this framework to create linguistic descriptions from
OLAP cubes in the energy consumption domain, such
as “Your behavior is inefficient, due to the high con-
sumption, the quite old devices and the low consump-
tion at the low charge period”.
4 METHODOLOGY
The methodology we are employing to develop this
Ph.D. is based on a cyclic hybrid bottom-up and top-
down approach. This approach consists in studying
practical application domains in order to gain knowl-
edge and experience on how to make the best use of
linguistic descriptions. From these practical cases, we
intend to identify and abstract (bottom-up) a generic
model which can gather the techniques employed in
these solutions, but which can be applied in other do-
mains as well (top-down).
The use of this model in other domains might not
provide enough potential to address the whole prob-
lem. In this case, the new linguistic description re-
quirements and concepts would be incorporated into
the model in order to extend its capabilities. Figure 2
shows a structural diagram of this methodology.
Since practical problems are the pillars of this
linguistic description
generic model
solutions for
specific problems
solutions for
specific problems
lead to
provides
extend
Figure 2: Schema of the Ph.D. development methodology.
methodology, we should also further extend this
methodology to deal on how to approach realistic lin-
guistic description needs. Thus, for each problem we
must:
1. Study a corpus, if available, of the descriptions
elaborated by the domain experts. This allows to
identify the structure of the target descriptions and
which sort of techniques must be used to create
them. If a set of examples is not available, then
the experts must define the target descriptions.
2. Design the linguistic description generation ap-
proach according to the requirements defined in
the previous stage. Determine which operators
and techniques must be used in order to extract
the relevant information from the source data.
3. Develop prototypes, ideally including a basic nat-
ural language generation system which translates
the linguistic descriptions into texts which can be
reviewed by the experts.
4. Perform a full validation process involving the ex-
perts once the results seem satisfactory in order to
ensure that the solution can be used in a real con-
text.
These tasks are based on the natural language gener-
ation research field, which has addressed many real
text generation necessities. Consequently, this way of
creating linguistic description solutions seems closer
to software engineering methodologies than to a re-
search process. However, the only way of proving
the usefulness of linguistic description techniques is
to apply them in practical cases, and this usually in-
volves the creation of software systems which must
deal with natural language generation, among several
other aspects.
ICAART2015-DoctoralConsortium
6
5 EXPECTED OUTCOME
We expect to address several linguistic description
problems in practical contexts, providing solutions to
real life description needs. These approaches will
help in the creation of the generic linguistic descrip-
tion model described in the previous section.
More specifically, the experience gathered in the
creation of practical linguistic description solutions
will allow us to provide a model which is based on
real contexts. Consequently, we would achieve a for-
mal model whose usefulness is proven by the practical
problems it is based on.
This model would provide solutions for new lin-
guistic description applications, which, in exchange,
could also provide new ideas and concepts for the ex-
tension of the original model.
6 STAGE OF THE RESEARCH
During the first stage of the Ph.D. we explored the ap-
plicability and performance of linguistic descriptions
in meteorology, an applied domain which presents
several interesting and relevant use cases.
Our first research approach addressed a common
task among meteorologists, which consists in the cre-
ation of weather reports about the climate behavior.
We studied monthly reports about temperatures in
Galicia (NW Spain) published by the Galician Mete-
orology Agency (MeteoGalicia) and elaborated a cor-
pus which served as a base to define the structure and
content of the target linguistic descriptions. As a re-
sult, we defined the concept of fuzzy nuance and pro-
posed an heuristic procedure which obtained similar
reports to the ones issued by the experts (as in Fig. 3).
Our results showed that the automatically generated
reports were consistent with the original ones.
Shortly after, we researched the application of
quantified sentences to create descriptions about the
cloud coverage state forecasting. Our approach aggre-
gated geographical time series data from numeric pre-
diction models to provide a global forecast for Gali-
cia. These automatically generated forecasts were
compared to the cloud coverage forecasts issued by
the meteorologists, showing that, although meteorol-
ogists use subjective experience and several informa-
tion sources to elaborate forecasts, the approach we
developed can be applied to provide linguistic de-
scriptions of geographical forecasts. An explanation
of the two previous use cases can be found in (Ramos-
Soto et al., 2013a), (Ramos-Soto et al., 2012a) and
(Ramos-Soto et al., 2012b).
Due to our involvement in the meteorology field
Meteorologist: “Temperatures were high for
October, due to the high temperatures which
were registered during the first fortnight”.
LDD approach: “The temperature was high
for October, with very high temperatures
during the first fortnight and very cold
temperatures during the fourth week”.
Figure 3: Example of a temperature report made by a mete-
orologist and an automatically generated report for the same
data source from (Ramos-Soto et al., 2013a).
and the experience gathered in our previous works we
created GALiWeather, an application which automat-
ically generates short-term forecast texts in Spanish
and Galician for every Galician municipality using a
linguistic description approach combined with a ba-
sic natural language generation method (see Fig. 4
for a textual forecast example). During this research
and development we have been supported by mete-
orologists from MeteoGalicia, who helped us in the
validation process of the application. A preliminary
study of the application can be found in (Ramos-Soto
et al., 2013b), while (Ramos-Soto et al., 2014a) and
(Ramos-Soto et al., 2014c) provide an exhaustive and
detailed technical explanation on the final version of
GALiWeather. As of today, GALiWeather has been
producing daily textual forecasts in MeteoGalicia’s
test servers for more than half a year. A public re-
lease of the automatically generated texts is expected
soon.
There will be clear skies at the beginning and towards the middle of the
term, although at the end they will be very cloudy. We expect precipitations
on Thursday morning. The temperatures will be normal for the minimums
and high for the maximums for this period of the year, with minimums in
notable increase and maximums without changes.
9th December,
Monday
9th December,
Monday
10th December,
Tues day
10th December,
Tues day
10th December,
Tues day
11th December,
Wednesday
11th December,
Wednesday
11th December,
Wednesday
12th December,
Thursday
12th December,
Thursday
12th December,
Thursday
Morn.
Aft.
Night
Morn.
Aft.
Night
Morn.
Aft.
Night
Morn.
Aft.
Night
Min: 1º Max: 14º
Min: 1º Max: 14º
Min: 5º Max: 16º
Min: 5º Max: 16º
Min: 5º Max: 16º
Min: 7º Max: 16º
Min: 7º Max: 16º
Min: 7º Max: 16º
Min: 11º Max: 15º
Min: 11º Max: 15º
Min: 11º Max: 15º
Figure 4: Example of a meteorological short-term forecast
obtained by GALiWeather.
While GALiWeather is not as sophisticated as
other forecast generators from a natural language gen-
eration point of view (such as those described in
(Goldberg et al., 1994), (Coch, 1998) or (Sripada
et al., 2003)), it focuses on the use of linguistic de-
scriptions in a real context, which provide meaningful
information for a wide public.
Currently, we find ourselves in the middle of the
Ph.D. development. We are considering new appli-
ComputingwithPerceptionsfortheLinguisticDescriptionofComplexPhenomenathroughtheAnalysisofTimeSeries
Data
7
cation domains in which linguistic descriptions may
prove useful, but are mainly focused on research-
ing a preliminary linguistic description generic model
based on our previous experience. We have also re-
cently explored the current state of the art in both nat-
ural language generation systems and linguistic de-
scriptions of data in order to ascertain the role that
generic LDD approaches (and thus our model) could
play integrated into NLG systems (Ramos-Soto et al.,
2014b).
We intend to provide a model which can be di-
rectly used in practical cases but can also be formally
described, thus maintaining both the theoretical and
practical aspects of the Ph.D. objectives. Our aim is
that this model can create linguistic descriptions from
heterogeneous data-sets, although at first we will fo-
cus on time series data. We expect to further extend
this model to support data with spatial components
and also to include new concepts and capabilities as
new practical problems arise.
Furthermore, we will also explore the genera-
tion of linguistic descriptions using meta-heuristic ap-
proaches. This is a task which has been scarcely ex-
plored (Castillo-Ortega et al., 2011b) and which may
prove useful in the sense of providing a general algo-
rithm for creating linguistic descriptions. This could
also be one of the possible extensions to our model.
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