Mapping Data Sets to Concepts using Machine Learning and a
Knowledge based Approach
Andreas Bunte
1
, Peng Li
1
and Oliver Niggemann
1,2
1
Institute for Industrial IT, Langenbruch 6, 32657 Lemgo, Germany
2
Fraunhofer IOSB-INA, Langenbruch 6, 32657 Lemgo, Germany
Keywords:
Clustering, Ontology, Knowledge, Reasoning, Classification, Concept Learning.
Abstract:
Machine learning techniques have a huge potential to take some tasks of humans, e.g. anomaly detection or
predictive maintenance, and thus support operators of cyber physical systems (CPSs). One challenge is to
communicate algorithms results to machines or humans, because they are on a sub-symbolical level and thus
hard to interpret. To simplify the communication and thereby the usage of the results, they have to be transfer-
red to a symbolic representation. Today, the transformation is typically static which does not satisfy the needs
for fast changing CPSs and prohibit the usage of the full machine learning potential. This work introduces a
knowledge based approach of an automatic mapping between the sub-symbolic results of algorithms and their
symbolic representation. Clustering is used to detect groups of similar data points which are interpreted as
concepts. The information of clusters are extracted and further classified with the help of an ontology which
infers the current operational state. Data from wind turbines is used to evaluate the approach. The achieved
results are promising, the system can identify its operational state without an explicit mapping.
1 INTRODUCTION
Machine learning techniques are getting more and
more common in industries. They are able to de-
rive information out of data which can be used for
tasks, such as anomaly detection, predictive mainte-
nance and optimization. A major limitation for all
these tasks is the sub-symbolic representation of the
algorithms’ results, so there is no meaning which can
be assigned to the data set. So, mapping to meaning-
ful results has to be performed manually, which has
to be done for every single type of machine, since the
mapping is not generic. This is not feasible for fast
changing cyber physical system (CPS). The results
have to be represented on a symbolical level, which
enable an easy exchange of information without ma-
nual adaption. Symbolical information are represen-
ted through concepts, which share a common under-
standing of a specific thing. But how can the machine
learning results be transferred to concepts?
To answer the question, a formal definition of
concepts is introduced, according to (Cimiano et al.,
2005). A concept c is a name for the aggregation of
things or objects which share a specific list of com-
mon attributes. The assignment of a thing or an object
to a concept can be defined as follows. A thing t ∈ T ,
where T is the set of all things, belongs to a concept
c ∈ C, where C is the set of all concepts, if and only
if all attributes of c are fulfilled by t. So, to automa-
tically assign data sets to concepts, the attributes of
the data sets have to be identified and concepts have
to be defined. For example, if the concept creature is
defined with the attribute has legs, then humans are
assigned to the concept, because they share the attri-
bute. But the definition is not good, because animals
such as fishes or snakes do not have the attribute and
thus they are not assigned to the concept.
There are some works which try to learn concepts
directly from the data and thus do not need prior kno-
wledge. For example Lake (Lake, 2014) analyzes
how to learn concepts from very small sets of trai-
ning data. Lake uses visual concepts for his work, but
it should be transferable to different input data. So,
concepts can be learned, but they are independent of
each other, e.g. without a hierarchical order, which
would be useful for communication e.g. by using su-
perordinate terms. This approach is presented in Fi-
gure 1, where two sets of data are aggregated to two
concepts.
Since the concepts should be used for an informa-
tion exchange, it seems not suitable to learn concepts
in each devices without prior knowledge. Especially,
430
Bunte, A., Li, P. and Niggemann, O.
Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach.
DOI: 10.5220/0006590204300437
In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 2, pages 430-437
ISBN: 978-989-758-275-2
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
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Figure 1: Concepts can be learned, but they are independent
of each other.
the relations are important to interpret the concepts
correctly, additionally, they cover knowledge that can
be used to infer new facts. Concept learning approa-
ches as in Figure 1 do not learn relations, since their
learning is difficult and error-prone (more details are
given in section 2). Instead, defined concepts can be
used to map data sets to these concepts, as shown in
Figure 2. It requires a definition of concepts, but en-
sures a comprehensible and constant classification to
concepts. Nevertheless, it is an abstraction where va-
lues of a continuous space are classified to discrete
values, so there will be a loss of information.
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Figure 2: Data sets are mapped to predefined concepts.
This work follows the second approach, which
maps data sets to prior defined concepts. The prior
defined relations enable a reasoning of new facts,
which is an additional benefit. Not only the map-
ping should be done automatically, it should be rea-
lized with unsupervised machine learning techniques.
The prior knowledge and the unsupervised machine
learning technique enable a handling of new situati-
ons. As an example, the operational states of a wind
turbine will be automatically determined. It has the
obvious advantage, that the states have to be defined
once in an abstract manner and can be further used to
automatically identify the states of different types of
machine.
The contribution of this paper is a novel approach
to assign data sets to a corresponding concept. This is
achieved by using prior defined concepts which ena-
ble the reasoning of new knowledge about the data
set. This approach bridges the gap between sub-
symbolic and symbolic results and thus enables ma-
chines to express results in an understandable manner.
The paper is structured as follows: An over-
view about the state of the art is given in section 2.
Section 3 introduces the approach in detail. The re-
sults are presented in section 4 by using a concrete
use-case. Section 5 summarizes the work.
2 STATE OF THE ART
In this section a literature review of ontology and con-
cept learning is given. Ontology learning is not the
main topic but relevant, because it is similar to this
work by adding instances to the ontology and classify
them. To the best of the authors knowledge, no litera-
ture could be found which maps data sets to concepts
and thus use an equivalent approach.
In principle, the learning of ontologies is possi-
ble, but it is almost used for lightweight ontologies
such as taxonomies. Hierachichcal clustering or de-
cision trees are typical methods, which are used for
it, because they can be translated directly into light-
weight ontologies. Nevertheless, existing approaches
often focus on linguistic properties such as (Suma
and Swamy, 2016) or (Ocampo-Guzman et al., 2009).
That means structured, semi-structured and unstruc-
tured texts are used to derive relations between words
by using similarity measures such as syntax, proper-
ties of word or the probability of occurrence (Drum-
ond and Girardi, 2008). Such approaches are not
transferable to CPS, since there are no text bases avai-
lable.
There is some work in learning more complex on-
tologies, but these approaches lack of accuracy for a
real world applications. For example, Zhu (Zhu et al.,
2013) used Bayesian networks to learn more complex
ontologies. But even if it is one of the best algorithms
that can be found in the literature, the F1 score is be-
tween 0.3 and 0.8. Lehmann (Lehmann and Voelker,
2014) concludes in his work that good quality onto-
logies need a close interaction with humans, so auto-
matic or semi-automatic generated ontologies have a
poor quality. This indicates the difficulty of ontology
learning.
Concept learning means that patterns in the data
should be learned and assigned to concepts. Most
of the approaches that can be found in the literature
use online methods, which require an interaction with
humans. For example, Araki (Araki et al., 2013)
developed a Multimodal Latent Dirichlet Allocation
(MLDA) algorithm for a robot which enables an on-
line learning. For the evaluation, 120 objects are clas-
sified in 24 categories. The algorithm learns a word
for every object and categorizes each object to a class,
but there is a human in the loop, which should be avoi-
ded in this work. Alibeigi (Alibeigi et al., 2017) in-
troduces a method for robots to learn and imitate mo-
tions, but there is also a human in the loop. Many
approaches are dealing with texts or natural language
and learn concepts out of it, such as (Ali et al., 2017)
or (Jia et al., 2017). Jia (Jia et al., 2017) uses spo-
ken texts for the identification of concepts. They used
an example to reserve a table in a restaurant, where
Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach
431
several information have to be exchanged and the sy-
stem asks for missing information. But even in this
small fixed application scenario, there is much poten-
tial to improve the system. A more detailed review
about the concept learning is given by Lake (Lake,
2014) or Mahmoodian (Mahmoodian et al., 2013).
The difference of the approaches in literature is
that we use a knowledge base. That has the advan-
tage, that the resulting concepts are known and thus
they can be used for communication, because ever-
ybody, who uses that knowledge base, has the same
understanding, e.g. of the concept error state. Ho-
wever, it requires a knowledge base which has to be
slightly adapted to new types of machines, but there
is not much knowledge required and some of the kno-
wledge can be reused.
3 APPROACH
The idea is to map data sets to previously defi-
ned concepts as presented in Figure 2. This work
uses clustering as a well known machine learning
technique. Concepts are defined within ontologies.
Ontologies are suitable, because they enable a formal
concept description, support reasoning and there are
tools available which ensure an easy usage. The
aim is to assign data to concepts and thus bridge the
gap between sub-symbolic and symbolic layer. This
is achieved by five steps (see Figure 3), which are
described in more detail in this section:
(i) Extraction of data about the clusters;
(ii) Data pre-processing;
(iii) Data discretisation;
(iv) Instance creation for the current state;
(v) Reasoning of the final operational state;
Step (i) is closely related to the used machine le-
arning technique. Clustering is used in this approach,
but it is also possible with other classification algo-
rithms. The so called mapping unit consists of step
(ii) and (iii). The steps (iv) and (v) performed in the
knowledge base, which is an ontology in this appro-
ach. The mapping unit must be able to query the kno-
wledge base, to get knowledge about the signals.
3.1 Data Extraction
For more complex CPS, the system behavior might
consist of multiple operational states that depend on
different factors, e.g. work environments or opera-
tions of the systems. For example, a wind turbine
has various operational states, namely idle, part load,
full load or error state. Therefore, data from all sen-
sors and actuators is acquired during the operational
phase. When cluster analysis is performed on a data
set, multiple clusters can be recognized. Each cluster
corresponds to a particular operational state of a given
CPS.
Typically, clustering provides data about the posi-
tion of the data point or about the cluster where the
data point is assigned to. This is suitable for appli-
cations such as anomaly detection, but for this use
case detailed information about the clusters is neces-
sary. This first approach just focuses on clusters and
does not consider single outlier. In this step, the in-
formation that describes the clusters sub-symbolically
will be extracted in the following manner. The na-
mes of the variables in the data sets will be passed
to the mapping unit, so that the semantic information
of each cluster can be retained as much as possible.
All possibly relevant data is captured, in order to des-
cribe the clusters as good as possible with statistical
values. Therefore, clusters are described in all dimen-
sions (dimensions are equal to the number of input
signals), so the maximum, minimum and mean value
of all signals are extracted as well as the variance of
each cluster. These data is provided to the mapping
unit, which does the further processing.
3.2 Data Preprocessing
As known from every data mining application, the
data preprocessing is an important step. Since there
are different scales of variables in the CPS, their im-
pact has to be adjusted. To compare the influence
of these variables on the mapping task, each signal
will firstly be normalized in the range of [0, 1] overall
clusters.
The task is to identify one representative value for
each signal of each cluster. Therefore, all the informa-
tion (minimum, mean, maximum and variance) has
to be processed to one significant value. It is sugge-
sted that the mean value provides a good representa-
tion, if the variance of the signal is not to high. If
there is a huge variance (in extreme it could cover
the whole range between 0 and 1), it is difficult to
identify one characteristic value for the signal, thus
the signal is suggested as not characteristic. The not
characteristic values are determined as all other va-
lues, but they are marked with a minus. Depending on
the needs, the threshold should be adapted. Generally
speaking: If there are many dimension and few states,
then the boundary to set a dimension as not charac-
teristic should be lower. However, the presented ap-
proach focuses on determining operational states. For
purpose of configuration it might be interesting which
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
432
Figure 3: The mapping is done in five steps.
signals have a huge variance, or for optimization the
minimum and maximum values might be more inte-
resting. In such cases, the data preprocessing has to
be adapted according to the needs. Nevertheless, the
output of the step is a list where each of the dimensi-
ons have a dedicated value.
3.3 Data Discretization
In this section it is described how the discretization of
the cluster information is performed. The discretiza-
tion bases on signal values determined in the previous
step. Every signal value s ∈ S, where S is the set of
all signals, is assigned to a category y ∈ Y , where Y
is the set of all categories, using the function f . Six
categories Y are used to represent the ordinale scale,
named no (α), low (β), mean (γ), high (δ), very high
(ε) and not characteristic (ζ). The number of cate-
gories should be chosen according to the application,
but for many applications six categories seems to be
suitable.
Some signals do not use the whole range between
0 and 1 which leads to a wrong discretization, because
the categories are ordered through the whole range be-
tween 0 and 1. If the signal just sligthly change, e.g.
between 0.8 and 1, values would only alternate bet-
ween two categories. For example, the power factor
should be higher than 0.9 and a value near 0 would
immediately lead to a blackout, so the category mean
should be significantly bigger than a value of 0.5. One
could suggest that it is fixed by the normalization, if
the value just varies between 0.8 and 1, but it is mos-
tly not the case for long data records, caused by e.g. a
disconnect of the grid, a blackout or measurement er-
ror. To prevent such wrong classifications, every sig-
nal has a parameter to choose the mean value µ. This
value µ can be defined in the knowledge base which
influences the discretization of signals. If the parame-
ter is not defined, µ is set to 0.5 as default value. But
also the variance of some signals can be smaller, e.g.
again the power factor varies between 0.8 and 1, but 1
should be very high and 0.8 should be low. Therefore
another parameter λ can be set, which scales the range
down. Again, this is used to cover the whole range of
categories and does not end up in a single class over
the whole range. If there is no parameter defined, λ
is set to 1. So, to perform this task, an access to the
knowledge base is required, to query the values µ and
λ for each signal.
It could happen that a signal which is mandatory
for the classification of operational states has a high
variance and is thus classified as not characteristic.
This would cause some trouble, because without such
signals, it is not possible to classify the state. To pre-
vent this, an additional parameter relevance can be
defined for every signal in the knowledge base.
1
The
relevance can be manually set to a value between 0
and 1, where 1 means that the signal is mandatory for
the cluster description and 0 indicate no relevance of
the signal (which will probably occur rarely in practi-
cal application). So, all values with a relevance higher
than 0.5 are changed to a positive value. Afterwards,
the borders for the values are defined as follows:
f (s) =
α if 0 ≤ s ≤
λ
10
β if
λ
10
< s ≤ µ −
(1−µ)
3·λ
γ if µ −
(1−µ)
3·λ
< s ≤ µ +
(1−µ)
3·λ
δ if µ +
(1−µ)
3·λ
< s ≤ 1 −
(1−µ)
3·λ
ε if 1 −
(1−µ)
3·λ
< s ≤ 1
ζ if 0 > s
(1)
The formula 1 assigns each signal of a cluster to
a discrete class. This enables to name properties of
clusters, such as ”The wind speed in cluster 3 is high.”
1
The signals that are needed for the state classification
could also be determined by the knowledge base with a sim-
ple query, but the relevance is also used for a unique and
meaningful naming of the cluster, as introduced in (Bunte
et al., 2017).
Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach
433
So, this can already be used for communication, but
it should be transformed to a more abstract level by
combining different characteristic combinations to an
operational state.
3.4 Create Individuals
Ontologies capture the prior knowledge, but they are
also used to capture the knowledge which came up
during the operation. They can be modeled with the
web ontology language (OWL). The ontology defines
individuals (instances) and concepts (classes) which
can be ordered hierarchically and restricted class defi-
nitions. Individuals are described through object and
data properties. Object properties are describing re-
lations between classes or between individuals. It is
possible to define relations between classes and in-
stances, but the ontology is getting undecidable with
it (Antoniou et al., 2003), which can influence the
state inferring. So these kinds of relations are for-
bidden for this approach, since the decidable profile
OWL-DL is used. Data properties are defining values
(strings, dates, floats,...) which are used, e.g. for the
relevance parameter.
The initial ontology has a class Cluster where an
instance for each cluster is created. The ontology con-
tains additional classes which describe the operational
states and individuals which represent all input sig-
nals of the machine learning. By creating an indivi-
dual for each cluster, all information should be stored.
The main information is the discretized input signal
of each cluster. So, all discrete categories (α...ζ) are
modeled as an object property and they are connected
to the particular input signals, which are modeled as
individuals. Additionally, all continuous values are
also stored as data properties to not lose the precise
information.
Finally, all information of the clusters is stored as
individuals of the class Cluster in the ontology. This
is just a different representation, but ontologies pro-
vide reasoning capabilities, which enable to find lo-
gical conclusions based on the formal descriptions,
which is done in the next step.
3.5 State Inferring
The reasoning requires prior knowledge to infer new
facts, e.g. to infer the operational state. To enable this,
all possible states have to be defined formally in the
knowledge base, as well as a description of signal ty-
pes, e.g. power or temperature. The main challenge is
to model the knowledge in a way that allows inferen-
ces for all possible combinations to exactly one state.
It requires some experience and an understanding of
available modeling constructs, but then it is feasible
and does not take a long time. The section 4 shows
exemplary how a state definition looks like. If it has
to be adapted to another type of machine, some kno-
wledge can be reused. Signal types hold generally,
so just the state definition has to be adapted and the
reusable knowledge depends on the similarity of the
machine types.
The inferring itself is done by a reasoner, which is
used to infer knowledge based on the formal descripti-
ons. Among other things, the reasoner checks all indi-
viduals and identifies class assignments for them. Ba-
sed on the object properties the reasoner checks which
cluster fits to which class. The new type assignments
are made according to the description and this repre-
sents the current operational state. These states can be
used for communication between machines, but also
for the communication with humans. The concepts
defined in the ontology fit to the humans’ understan-
ding, so if there is an error state, it can be shown to
humans and they would understand the current situa-
tion.
4 RESULTS
The results for a concrete use-case are presented in
this section. Clusters detected in the wind power plant
data should be automatically determined to an opera-
tional state. The data set consists of 11 continuous
signals with a time resolution of 10 minutes. Over
230,000 data points generate six clusters, but most of
the data is represented in two clusters, (see the clus-
tering of Figure 3). To provide a more detailed under-
standing of the approach, the results of all five steps
are presented for this example.
In the first step, data about the maximum, mean,
minimum value and variance of every signal in every
cluster center is extracted. It is represented as follows:
Cluster1 windSpeed 12.0 5.2 0.0 3.1 rotorSpeed 16.9
9.7 0.0 5.94...
Cluster2 windSpeed 16.1 10.7 5.7 4.2 rotorSpeed 18.0
17.3 16.6 0.72...
...
In the second step, the data is preprocessed, so at
first it is normalized. The further processing can be
adapted to the specific use case. In this use case, the
variance in most of the clusters is low, such that the
mean values are suitable to describe the cluster. The
following data is provided to the discretization step:
Cluster1 windSpeed 0.20 rotorSpeed 0.54 ...
Cluster2 windSpeed 0.41 rotorSpeed 0.96 ...
...
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
434
Figure 4: All signals of the plant have to be modeled as
individual, before.
The step data discretization transforms all values
into a category between no and very high or to not
characteristic. As described, the parameters λ and µ
can be used to adapt the categorization. This is done
e.g. for the signal rotorSpeed, because even at low
wind speeds the rotor has a fast drive. µ was set to
0.8. The parameter relevance is not needed for this
use case. To increase the readability, every signal can
have a name that is more understandable or common
in the community. In this example, the signal wind-
Speed has the colloquial name WIND which is stored
in the individual that represent the input signal wind-
Speed. The resulting representation is as follows:
Cluster1 windSpeed LOW WIND rotorSpeed LOW
ROTORSPEED...
Cluster2 windSpeed MEDIUM WIND rotorSpeed
VERYHIGH ROTORSPEED...
The fourth step is to integrate the information
from above to the ontology. All signals of the plant
must be represented by an individual in the ontology,
as shown in Figure 4. Additionally, the categories
have to be represented by an object property, as shown
in Figure 5. Both are preliminaries, so this is done du-
ring the configuration beforehand. Since this is done,
the information can be transferred to an individual,
which is an automatic process. For every cluster, a
new individual of the generic type cluster is created
and the name is generated by the word cluster and a
consecutive numbering. All eleven signals are combi-
ned to one individual by using the categories as object
property which connects it with the individual that re-
present the signal. This is presented in Figure 6. Ad-
Figure 5: The categories are modeled as object properties.
Figure 6: Example of the individual cluster 1.
ditionally, the original values are stored as data pro-
perty. This is not needed, but can provide additional
information e.g. if the categories are reorganized.
In step five, the reasoner will infer the states by
assigning individuals to classes, the classes represent
operational states in this example. Table 1 shows the
definition of states, which are modeled during the
configuration. They only base on the two properties
wind speed and power, in this example. The ope-
rational states are not defined very strictly, e.g. no
wind and low power is defined as idle state, because
transitions between two categories are always critical.
Since two signals are classified, it is not known which
one switches first to another category, e.g. if wind and
power rise.
The class description of FullLoad is shown in
Figure 7. It is defined as subclass of Operational-
State and disjoint with the classes PartLoad, IdleState
and ErrorState, which indicates that every cluster can
have only one operational. But the important defini-
tion for the reasoning is the Equivalent To property,
which is defined regarding table 1 for FullLoad. So
all created individuals, which represent a cluster, will
be assigned to a class, depending on the attributes they
fulfill, by the reasoner. In this example Cluster4 has
all attributes of and thus it is assigned to the class. The
yellow background of individual (in Figure 7) indica-
tes that the class assignment was inferred automati-
cally.
Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach
435
Table 1: Definition of operational states.
Wind speed
Power
no low medium high very high
no Idle state Idle state Error state Error state Error state
low Idle state Part load Part load Error state Error state
medium Error state Part load Part load Full load Error state
high Error state Error state Part load Full load Full load
very high Error state Error state Error state Full load Full load
Figure 7: Description of the class full load including one inference.
The overall results are promising. All clusters are
classified correctly. Cluster 3 is correctly classified as
error state, because there is high wind, but no power.
So it is obviously for humans that it is an error state,
but here the machine also has determined it automa-
tically and thus shares the concepts with the humans.
Cluster 4 is detected as full load state, which is cor-
rect, with a mean value of 0.95 for power, the wind
power plant is nearly at its rated power. Cluster 1
is classified as idle state, which can be argued about,
since the mean value of 0.095 for power is not fully
idle, but it is the lowest value of all clusters and in-
cludes lots of idle times. All other clusters (cluster
2, 5 and 6) are correctly classified as part load. They
have different combinations of low/medium and po-
wer/wind, additionally the cos ϕ differs.
5 CONCLUSION
This paper introduces an approach for the mapping of
data sets to concepts. It maps sub-symbolic data to
symbolic information. This approach requires some
prior knowledge about signals and operational states.
The signal names are mandatory, three parameters are
optional and just needed for some signals, which are
mostly the same even in different types of machines,
such as cos phi, which has always the same charac-
teristic. This is additional expert knowledge that is
provided to the system.
In a first step a clustering algorithm is performed
to generate clusters from data points. These clusters
can be interpreted as concepts. The information about
the clusters is extracted and classified to one of six
categories, namely no, low, medium, high, very high
or not characteristic. This symbolic representation is
added to the ontology. Reasoning is performed in the
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
436
ontology as a last step. A reasoner assigns the clusters
to classes, which represent the operational state based
on its features. The approach was tested at a wind
power plant data set with six clusters. All clusters are
assigned correctly to the operational modes.
Therefore, the aim, to determine the operational
state without explicitly defining it for a use case, is
achieved. There are just generic definitions used,
which are suitable for similar applications. If the
application changes, it has to be adapted only once.
But since a classification is made of many continuous
signals, it can happen that really small changes lead
to another operational state, but this is quite normal
since it is an abstraction.
Further work can deal with the generic part of the
data preprocessing, since it has to be adapted manu-
ally regarding the use case. In particular the norma-
lization can cause some trouble, if there are uncom-
mon values, which deform the range of the values and
lead to wrong classification, which should be handled.
Furthermore, additional machine learning techniques
can be integrated and maybe combined to achieve a
better results.
ACKNOWLEDGEMENT
The work was supported by the German Federal
Ministry of Education and Research (BMBF) under
the projects ”Semantics4Automation” (funding code:
13FH020I3) and ”Provenance Analytics” (funding
code: 03PSIPT5B).
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