A Smart System for Haptic Quality Control
Introducing an Ontological Representation of Sensory Perception Knowledge
Bruno Albert
1,2,3
, Cecilia Zanni-Merk
1
, Franc¸ois de Bertrand de Beuvron
1
, Jean-Luc Maire
2
,
Maurice Pillet
2
, Julien Charrier
3
and Christophe Knecht
3
1
ICube Laboratory SDC Team, INSA de Strasbourg, 300 bd S
´
ebastien Brant, 67400 Illkirch, France
2
SYMME Laboratory, Universit
´
e Savoie Mont-Blanc, 7 Chemin de Bellevue, 74940 Annecy-Le-Vieux, France
3
INEVA, 14 rue du Girlenhirsch, 67400 Illkirch, France
Keywords:
Sensory Perception, Haptics, Smart System, Semantic Analysis, Quality Control, Perceived Quality.
Abstract:
Perceived quality has become an important factor in the choice of products by customers. The human per-
ception process involves complex phenomena at a physical and psychological level that enable us to sense the
world and extract information about it. Because of the qualitative way humans represent and communicate
sensations, the field of sensory perceptions makes extensive use of semantics. The use of knowledge-based
systems in the field of perceived quality is hence natural. This project focuses on haptics in quality control
in industry. In particular, the aim is to develop a smart system which will enable to make decisions about
the haptic quality of a product. This paper introduces the framework used for the development of this smart
system, based on the KREM model. An ontological structure is proposed in order to represent knowledge re-
lated to the measure of sensory perceptions in general, and of haptic ones in particular. The proposed domain
ontologies about haptic control, that were elicited using semantic analysis, are aligned with the SSN ontology.
1 INTRODUCTION
Humans perceive the world and particularly objects in
the world through their different senses, which allow
them to not only understand, but also make their own
opinion and judgment about these objects. Consider-
ing this fact, perceived quality has naturally become a
major factor of the choice of products by customers.
In an industrial context, controlling perceived qual-
ity is often limited to controlling the visual aspect of
products, which in some cases is enough, but in most
cases does not fully correspond to the perception a
customer can have when interacting with a product.
In particular, the action of touching the product usu-
ally comes right after a first visual observation.
Touch involves complex physical and psycholog-
ical phenomena which lead to very precise but also
very subjective haptic perceptions. Haptic percep-
tions are a combination of tactile and kinesthetic per-
ceptions (Lederman and Klatzky, 2009). Tactile sen-
sations are obtained thanks to sensory receptors local-
ized in the skin. Kinesthetic sensations are obtained
thanks to receptors localized in muscles, tendons and
joints. As a simplification, considering contacts with
a product, tactile sensations can be understood as the
sensations obtained locally on the surface of the prod-
uct and kinesthetic sensations as the ones obtained
more globally over the product.
Therefore, on the one hand the control of hap-
tic sensations involves the comprehension of the pro-
cess of creation of haptic perceptions, at physical and
psychological levels. On the other hand, it involves
the formalisation of this knowledge in order to ex-
tract control protocols and make this knowledge us-
able by an automated system. In this context, knowl-
edge based systems are especially suitable.
The KREM framework is presented in section two
for the development of a Smart System for haptic
quality control. This framework highlights the use
of four main components (knowledge, rules, experi-
ence, meta-knowledge) to take into account the speci-
ficities of the system to be developed. In particular,
this paper details the Knowledge component. An on-
tological structure is introduced in section three. It
presents the different domains of knowledge involved
and the corresponding ontologies, as well as the upper
level ontology used to structure the concepts. A do-
main ontology structuring haptic knowledge is then
presented in section four, with the integration of the
proposed formalization of the haptic sensations.
Albert, B., Zanni-Merk, C., Beuvron, F., Maire, J-L., Pillet, M., Charrier, J. and Knecht, C.
A Smart System for Haptic Quality Control - Introducing an Ontological Representation of Sensory Perception Knowledge.
DOI: 10.5220/0006048300210030
In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 2: KEOD, pages 21-30
ISBN: 978-989-758-203-5
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
21
2 A FRAMEWORK FOR THE
DEVELOPMENT OF A SMART
SYSTEM FOR HAPTIC
QUALITY CONTROL
One framework used in order to develop a smart sys-
tem is the KREM model (Zanni-Merk, 2015), that has
been successfully used in several different domains
(Zanni-Merk et al., 2015; Gartiser et al., 2014).
Conventionally, a smart system is composed of a
fact base and a rule base, on which various types of
reasoning can be made. But the observation of the
drawbacks of this classic architecture (the difficul-
ties in eliciting expert knowledge, mainly because ex-
perts operate tacit knowledge, and basically, the non-
completeness of this elicitation (Milton, 2008)) led to
the proposal of this model, based on the use of seman-
tic technologies.
Semantic technologies use methods from auto-
matic language processing, machine learning and
knowledge representation to build the ontologies and
the rules that will enable its implementation. Se-
mantic technologies are also intended to create new
meaningful relationships, and therefore new knowl-
edge, based on information of different natures and
forms. Enriching documents with meta-data or cre-
ating specific linguistic or terminological standards
are examples of the possibilities offered by seman-
tic technologies to facilitate decision making through
effective knowledge management.
But decision-making, to be effective, must re-
sult from reasoning and analysis on this knowledge
and must also take into account the experience and
expertise of decision-makers. Naturally, the cap-
italization of experience appeared as a possibility
of improvement of the architecture, in the form of
specific knowledge structures and reasoning mecha-
nisms, such as SOEKS (set of experience knowledge
structure) (San
´
ın and Szczerbicki, 2009) and CBR
(case based reasoning)(Aamodt and Plaza, 1994).
Finally, the use of meta-knowledge to lead the ex-
ecution of our knowledge-based systems became a
need. Meta-knowledge is knowledge about the do-
main knowledge, the rules or the experience. It can be
in the form of context, culture or protocols that steer
the use of that knowledge. Context is any information
that characterizes a situation related to the interac-
tion between human beings, applications and the sur-
rounding environment (Dey et al., 2001) and is iden-
tified as belonging to four types: identity, location,
status, time. Context is typically the location, identity
and state of people, groups, and computational and
physical objects. Time is information that helps to
Figure 1: The KREM architecture with its four interrelated
components: Knowledge, Rules, Experience and Meta-
knowledge.
recognize a situation using historical data. The Cul-
ture aspect of meta-knowledge intends to reflect the
different ways decisions are made in different cultures
while Protocols include typically the ways the other
pieces of knowledge are used to accomplish the task
we are facing to (for example, diagnosis); or strategies
for problem solving or heuristics. Meta-knowledge
may be closely related to experience knowledge.
To take these ideas into account, the KREM model
has four interacting components that can be declined
by project or application domain. The re-use of com-
ponents is, of course, encouraged. The KREM com-
ponents are (Figure 1):
The Knowledge component that contains the do-
main knowledge to operate, by means of different
domain ontologies.
The Rules component that allows different types
of reasoning (monotone, spatial, temporal, fuzzy,
or other) depending on the application.
The Experience component that allows the capi-
talization and re-use of prior knowledge.
The Meta-knowledge component, including
knowledge about the other three bricks that
depends on the problem.
The way the domain knowledge is formalized will
shape the way the rules are expressed. Experience
will complete the available knowledge and rules. Fi-
nally, meta-knowledge will directly interact with the
rules and the experience to indicate which rules (com-
ing from experience or from the initial rule set) need
to be launched according to the context of the problem
to solve.
A modular architecture, such as KREM, is one
of the main architectural design pattern for large and
complex systems. In this pattern, each module or
component has a specific functionality providing sep-
aration of concerns that, in turn, support re-use or
replacement (i.e. changes in a single module would
not affect the others, permitting the continuous oper-
ation of the system). Moreover the communication
KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development
22
between modules needs to be based on well defined
interfaces to provide low coupling.
Formalising and structuring Knowledge are the
first steps of the development of the Smart System.
The following sections are hence focused on the
Knowledge component, which will eventually be in-
tegrated to a larger KREM architecture and therefore
coupled with the other modules previously presented.
3 TOWARDS AN ONTOLOGICAL
STRUCTURE FOR HAPTIC
QUALITY CONTROL
The use of formal models, such as ontologies, is es-
sential for the development of a smart system. How-
ever, very few studies have proposed ontologies di-
rectly related to the description of human perceptions,
or more specifically to the perceived quality. One rare
example of a similar domain ontology has been pro-
posed by (Myrgioti et al., 2013), but this study fo-
cuses on software development for haptic interfaces.
This section presents the proposed ontological struc-
ture for the measuring of sensory perceptions and its
particularization towards haptic quality control.
3.1 Upper-level Conceptual Model
The first step in the construction of a conceptual do-
main representation is the identification of the general
ontological structure in which the domain ontology
can be included. In particular, the use of a high-level
ontology is essential in order to form the ”skeleton” of
the structure. There are multiple upper-level ontolo-
gies, but we will focus here on the Semantic Sensor
Network (SSN) ontology (Compton et al., 2012), sup-
ported by the W3C
1
, that has been identified as par-
ticularly relevant considering the context of the study
and the opportunities of further development. Comp-
ton et al. (2012) introduced the SSN ontology in order
to describe sensors and observations. Besides the per-
spectives of future development of the present study
around the system of sensors, the SSN ontology pro-
poses a way to conceptualize the links between prop-
erties, sensors and observation. In addition, SSN is
a core ontology that is based on the well-known top-
level DUL ontology (DOLCE+DnS Ultralite, 2010).
Figure 2 is a reduced version of the SSN ontol-
ogy including the stimulus-sensor-observation pattern
proposed by (Compton et al., 2012), focused only on
some of the entities relevant to this study. This rep-
resentation involves the different concepts of inter-
1
World Wide Web Consortium (http://www.w3.org/)
Figure 2: Extract of the SSN ontology (Compton et al.,
2012).
est, regarding the aim of knowledge integration about
haptic perception, as well as future automation of the
process. An alignment between this extract of the
SSN ontology and the proposed domain ontology is
presented below.
3.2 Global Ontological Structure
Because of the diversity of domains involved in the
control of the haptic quality of products, an ontolog-
ical structure is proposed in Figure 3. It aims at or-
ganizing knowledge that composes each domain into
different domain ontologies, which can be aligned to
the upper-level ontology, i.e. the SSN ontology here.
This ontological structure is presented as a classical
ontology hierarchy (Roussey et al., 2011) with a top-
level ontology, a core ontology, a general ontology, a
task ontology and multiple domains ontologies. The
domains involved here are: the sensors, the objects
of study (products to be controlled), the sensory per-
ceptions (and haptic perceptions in particular) and the
context. In addition, the control process is represented
as a task ontology.
The haptic quality control will then make use
of the elements of each of the domain ontologies,
in a flexible and adapted way following the context
of application and industrial constraints (formalised
throughout the Context ontology). The following sec-
tion focuses on the Haptic Perception ontology which
gathers knowledge about haptics and enables a di-
rect correspondence with human perceptions. The
other domain ontologies involved in this structure are
part of the development process for the formalization
of haptic quality control, but they will not be exten-
sively detailed in this paper. Here is a brief descrip-
tion of these ontologies : the Sensor ontology gath-
ers knowledge about industrial sensors which are rel-
evant for the measuring of sensations. The Object
of Study ontology contains information about the
A Smart System for Haptic Quality Control - Introducing an Ontological Representation of Sensory Perception Knowledge
23
products or the samples on which the quality control
is performed. The Control Process ontology aims at
gathering the tasks and protocols necessary in order
to perform the quality control. Moreover, the Context
and Control Process ontologies are mainly part of the
Meta-knowledge components.
In addition, the proposed ontological structure has
been made general enough in order to foresee possible
utilization with other kinds of senses, for instance vi-
sion, hearing or taste, as shown with dotted line boxes
in Figure 3.
4 HAPTIC DOMAIN ONTOLOGY
This section presents our first developments of a do-
main ontology integrating haptic perception knowl-
edge and its alignment with Compton’s core ontology.
In order to develop this ontology it was necessary to
start with the gathering of knowledge about this do-
main, by understanding the main concepts involved
as well as structuring and formalizing the vocabulary
involved. The proposed haptic perception ontology is
then detailed and an example is presented.
4.1 Formalization of Knowledge about
the Haptic Domain
The sense of touch has been widely studied at a bio-
logical level in order to understand how it works. A
summary of the perception process is presented here-
after. Considering the description of sensations, only
few studies have proposed to analyse and select de-
scriptors. Moreover, these studies usually focused on
specific types of application and material. A more
general and generic approach is proposed here. It
intends to formalize and structure knowledge about
haptics by gathering relevant vocabulary and relations
from multiple sources, such as sensory analysis stud-
ies as well as vocabulary databases, and extracting
sensation classes.
4.1.1 Perception Process
The process of creation of a tactile perception starts
with touch, which can be defined as the stimulation
of the skin by thermal, mechanical, chemical or elec-
trical stimuli. Sensory systems, and sensory receptors
in particular, activate as soon as a stimulus is detected.
They transform the energy received through the stim-
uli into electrical energy by a change of neuronal elec-
trical potential (transduction). Encoded information
is then processed by the nervous system in order to
produce sensations. Sensory systems located in the
nervous system interpret these sensations by compar-
ison to memories and known sensations. Perceptions
are the results of this process. A schematic view of the
tactile perception process is provided in Figure 4 (De
Boissieu, 2010) and De Rossi and Scilingo (2006).
In principle - and considering similar external
conditions such as temperature, humidity, level of
tiredness, etc. - a sensation is identical, or near-
identical, for everybody. However, a perception dif-
fers from one person to another, and depends on ex-
perience and/or culture.
4.1.2 Usual Haptic Sensation Descriptors
Humans communicate about sensations using words.
The field of sensory perceptions makes hence great
use of semantics. In particular regarding haptic sen-
sations, more than 200 descriptors could be listed, as
a result of the search performed across the literature.
Some examples are provided below, along with the
specificity of these descriptors.
Descriptors found in the literature are usually re-
lated to specific types of product and material. They
also depend on the language and culture of the con-
trollers. Sensotact (Crochemore et al., 2003) is a
reference method introduced by Renault in order to
describe the tactile perceptions of vehicle interiors.
It employs ten descriptors distributed following the
exploration mode (hardness, responsiveness, mem-
ory effect, sticky, fibrous, relief, scratchy, blocking,
slippery and thermal). Considering textile products
only, Issa et al. (2005) proposed six invariant de-
scriptors common to French and English languages
(flexible/rigid, falling, thin/thick, soft, creasable, re-
sponsive). In the same field of application, Pi-
card et al. (2003) found ve pairs of descriptors
(soft/rough, thin/thick, mellow/hard, smooth/rough,
pleasant/harsh), from a set of twenty-four and Sola
(2007) listed fourteen descriptors. Considering paper
sheets, Summers et al. (2007) reduced it to two de-
scriptors (rough and stiff). In the field of packaging,
Dumenil-Lefebvre (2006) suggested two groups of
descriptors, respectively for the tactile description of
ground-glass (sticky, rough, granular, slippery, cool,
greasy) and a multi-material group, including plas-
tic, cardboard, etc. (adherent, sticky, supple, elas-
tic, markable, rough, granular, slippery, scratchable,
cool).
The descriptors listed in the different studies are
hence very different from one product to another, as
well as from one type of material to another. While
some descriptors are common across different mate-
rials (for instance, soft that is used similarly for wood,
fabric, leather, ceramic), some others are very specific
to one type of material. For example, descriptors like
KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development
24
Figure 3: General ontological structure and domain ontologies of haptic quality control.
Figure 4: The human tactile perception process. Inspired
from De Boissieu (2010) and De Rossi and Scilingo (2006).
furry, fuzzy, fluffy, etc. are mainly used for the de-
scription of fabric.
Translations from one language to another can
also make these lists change, because the correspon-
dence between the meanings of translated words is
not always complete, or can be expressed with sev-
eral different words, following the context. For ex-
ample, frais in French could be translated into fresh
or cool. Some words also do not have any transla-
tion, which is the case of bouchard
´
e in French which
comes from a tool: the boucharde, which is originally
used to print marks on concrete (Sola, 2007). Differ-
ences between cultures, often represented by the dif-
ference in language, can sometimes induce a differ-
ence in the meaning of the same words, for example
a sensation of cold might not be perceived the same
way by people living in cold or hot areas.
4.1.3 Formalizing Haptic Sensations
Considering the diversity of the vocabulary used in
order to describe haptic sensations, there is a strong
need for the formalisation of the way these sensations
are described. For instance, studies performed in the
context of visual quality control (Baudet, 2012; Maire
et al., 2013) demonstrated the feasibility of reducing
the list of descriptors used and therefore formalizing
visual aspect anomalies.
The proposed method makes use of the seman-
tic characteristics of the descriptors in order to ex-
tract generic categories of haptic sensations. In par-
ticular, a classification of the usual descriptors was
performed. First, semantic relations between descrip-
tors were used in order to group them. Synonym and
antonym links were drawn from semantic databases
like Wordnet
2
and the Thesaurus
3
. Then, a graphical
2
WordNet: https://wordnet.princeton.edu/
3
Thesaurus: http://www.thesaurus.com/
A Smart System for Haptic Quality Control - Introducing an Ontological Representation of Sensory Perception Knowledge
25
tool
4
and the OpenOrd method (Martin et al., 2011)
were used in order to gather elements with strong re-
lations and spread the ones with weak relations. The
result is shown in figure 5. The OpenOrd method in-
volves the computation of the distance between nodes
(descriptors), by minimizing a formula containing at-
tractive and repulsive terms. This grouping enabled to
extract categories from the meaning of the descriptors
contained in these main groups.
Descriptors were also classified following three
semantic axes proposed by Sola (2007), which focus
on the origins and meaning of the descriptors. These
semantic axes (source, effect and physical property)
highlight the semantic foundations of the descriptors
and the links with the characteristics of a surface. The
source axis refers to a perception (sensation comple-
mented with knowledge and experience), e.g. oily
refers to oil. One needs to have the knowledge of
what oil is to understand what an oily sensation is.
The effect axis refers to sensations, which involve a
judgement from the evaluator. These sensations are
hence subjective, or even hedonic. Finally, the phys-
ical property axis refers to a non-hedonic sensation,
which can be directly measured. This classification
enabled to select descriptors which were relevant to
each category, and useful as an objective description
of specific levels of each category of sensation. Hedo-
nic elements (which form a separate group in figure 5)
were not selected, because of the strongly subjective
judgement they involve.
4.1.4 Elementary Haptic Sensations
As a result of the categorization previously presented,
a set of nine elementary haptic sensations is proposed.
These elementary sensations correspond to the groups
identified in the classification step - with one excep-
tion of a central group (including homogeneous and
heterogeneous) corresponding to general characteris-
tics of the other descriptors. These elementary sensa-
tions are hence designed to cover all haptic descrip-
tors listed and to be used in order to describe them
in a formalised manner. Moreover, considering that
these classes were constructed using descriptors from
all kinds of domains of application, they enable a
generic description of tactile sensations. In particu-
lar, the descriptors found in the literature, and pre-
sented above, can be described by at least one ele-
mentary sensation. Table 1 shows the list of nine el-
ementary sensations, organised following tactile sen-
sations and kinesthetic sensations. In this Figure, an
association of stimuli and exploratory movements is
4
Gephi: Open graph visualisation platform, url:
https://gephi.org/
Table 1: Proposed elementary sensations, and associated
stimuli and exploratory movements.
also proposed. It was constructed thanks by extract-
ing relations between stimuli and descriptors in the
following studies : (Lederman and Klatzky, 2009; De
Boissieu, 2010; Bensma
¨
ıa and Hollins, 2005; Jones
and Lederman, 2006; Crochemore et al., 2003).
4.2 Haptic Perception Ontology
The formalized knowledge about the haptic domain
was then used to design a domain ontology of haptic
perceptions. Figure 6 shows an extract of the pro-
posed haptic perception ontology. It was obtained us-
ing OWLGrEd (B
¯
arzdin¸
ˇ
s et al., 2010). A UML-like
notation is used, where boxes are OWL classes, thick
lines are hierarchical relations and thin lines are re-
strictions, labeled with object properties. Only a sub-
set of the concept and relation restrictions are dis-
played for the sake of clarity, and in order to show
a specific example of the characterisation of the de-
scriptor Slippery. The complete ontology includes all
the elements of Table 1 as well as the full list of de-
scriptors.
The main concepts of the more general sensory
perception ontology are: Exploration, Stimulus, Sen-
sation and Descriptor. Exploration describes the way
the stimulus is generated. Receptor refers to hu-
man sensory receptors which detects stimuli. Stim-
ulus is the physical phenomenon that induces sen-
sations. Sensation is a formal description of human
sensations. It gives insights on Descriptor which in-
tegrates the vocabulary usually used to communicate
about perceptions. In addition, individual stimuli are
characterized by individual elements of PhysicalPa-
rameter. These elements are a decomposition of the
physical properties that characterise stimuli. They are
intended to be as low level as possible in order to pro-
vide elements for the control process as well as to es-
KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development
26
Figure 5: Extraction of categories from usual haptic descriptors using semantic classification methods.
tablish future relations with sensors.
Regarding haptic perceptions in particular, the
concept of Exploration integrates the elements nec-
essary for the stimuli to be generated. This includes
parameters of the movements and contacts to be per-
formed. The concept of Stimulus integrates the phys-
ical characteristics measured by human receptors,
i.e. for instance Pressure, Stretching, Persistence,
etc. The concept of Sensation integrates the pro-
posed formalisation of haptic sensations previously
presented. The elementary sensations are distributed
on the two classes TactileSensation and Kinesthetic-
Sensation. The usual descriptors are grouped under
the concept Descriptor and are related to correspond-
ing sensation categories.
Figure 6 serves also as an example of the relations
involved in the description and characterization of
the descriptor Slippery. First, the performed formal-
ization of haptic knowledge provides information on
relations between the usual descriptors and the pro-
posed elementary sensations. Considering this spe-
cific example case, it is possible to establish that the
elementary Sensation of Grip uniquely describes the
Descriptor Slippery. Grip is sensed through the Stim-
ulus Stretching. This specific type of Stimulus is char-
acterized by the physical parameters TangentialDe-
formation and TangentialForce.
indent Thus, this representation provides all the ele-
ments necessary to link haptic sensations - and even
more specifically usual descriptors of these sensations
- to the physical properties of the stimuli that can be
measured in order to provide intensity values of hap-
A Smart System for Haptic Quality Control - Introducing an Ontological Representation of Sensory Perception Knowledge
27
Figure 6: Extract of the proposed Haptic Perception ontology.
tic sensations. Moreover, this ontology also provides
the related exploration parameters, as well as the sen-
sory receptors involved in the perception process (not
fully displayed in Figure 6).
4.3 Alignment to the SSN Core
Ontology
Considering the aim of applying haptic perception
knowledge to quality control, the higher level SSN on-
tology brings a formal structure to the proposed con-
cepts and will enable to reason about it in a more gen-
eral manner. Indeed, SSN is designed to provide a
formal ontological framework to represent the inter-
actions between sensors and properties, or more gen-
erally between a sensing system and features of in-
terest.The alignment to a part of the SSN ontology,
and more specifically to a part of the sensor-stimulus-
observation pattern, is relatively natural considering
the concepts proposed in the Haptic Perception (HP)
ontology. The following alignment is proposed :
HP : Sensation v SSN : Property
HP : PhysicalParameter v SSN : Property
HP : Descriptor v SSN : FeatureO f Interest
HP : Stimulus v SSN : Stimulus
HP : Exploration v SSN : Sensing
HP : Receptors v SSN : Sensor
In particular, in the SSN ontology, SSN:Property
is defined as an observable characteristic of real-
world entities (SSN:FeatureofInterest), which are not
directly observable. HP:Descriptor can be aligned to
SSN:FeatureofInterest, because it corresponds to the
way people usually communicate about sensations,
which is not directly observable. On the contrary,
HP:Sensation and HP:PhysicalParameter correspond
to observable characteristics of HP:Descriptor. They
can hence be aligned to SSN:Property.
5 CONCLUSIONS AND
PERSPECTIVES
This work sets the basis for the development of a
Smart System for haptic quality control. The main ar-
chitecture of the System was presented with the use of
the KREM model which provides a separation of the
modules involved in the development of this system,
while still enabling interaction between them. This al-
lows for more flexibility in the development, consid-
ering future applications of the system, in particular
regarding the integration of experience. The Knowl-
edge component of this architecture was explored and
detailed in this paper, with the proposition of a general
ontological structure adapted to the constraints of the
project.The high-level SSN ontology was used in or-
der to structure knowledge corresponding to the mul-
tiple domains involved in the development of a Smart
System for haptic quality control. The domain on-
tology integrating haptic knowledge was specifically
presented. The formalization of haptic knowledge
was first detailed with the proposition of generic el-
ementary haptic sensations. Haptic knowledge was
then conceptualised into the proposed Haptic Percep-
tion ontology and aligned with the SSN ontology.
The next steps of the development of a Smart Sys-
tem for haptic quality control include the develop-
KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development
28
ment of the Meta-knowledge and Rules modules of
the KREM architecture. In particular, the extraction
of control rules will be realized through the applica-
tion of the proposed formalization of haptic knowl-
edge on case studies. While expert knowledge en-
abled a validation of the semantic analysis, the in-
dustrial testing being performed will provide more
specific evaluation material. Moreover, the establish-
ment of the influence of the application context will
enable to select adapted rules. Furthermore, the pro-
posed system being intended to automate haptic qual-
ity control, knowledge about sensors and objects of
study will also be explored, as well as the relations
between data from the sensors and haptic sensations
which correspond to the problem of symbol anchor-
ing.
ACKNOWLEDGEMENTS
This work has been done within a thesis project
funded by the French technological research associ-
ation (ANRT) as well as the company INEVA
5
. This
work is the result of a collaboration between three
parties, which are all acknowledged here: the com-
pany INEVA, the INSA de Strasbourg (with the ICube
laboratory) and the University of Savoie Mont Blanc
(with the SYMME laboratory).
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