A Cognition-inspired Knowledge Representation Approach for
Knowledge-based Interpretation Systems
Joel Luis Carbonera and Mara Abel
Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Keywords:
Knowledge Representation, Reasoning, Ontology, Petroleum Geology.
Abstract:
We propose a hybrid approach for knowledge representation that combines classic representations (such as
rules and ontologies) with cognitively plausible representations, such as prototypes and exemplars. The re-
sulting framework can be used for developing knowledge-based systems that combine knowledge-driven and
data-driven techniques. We also present how this approach can be used for developing an application for
interpretation of depositional processes in Petroleum Geology.
1 INTRODUCTION
Nowadays, knowledge-based systems can be viewed
as a mature technology, which have been applied
for supporting the decision making in a wide range
of scenarios. In general, knowledge-based systems
are characterized by a top-down approach, where
the relevant knowledge is explicitly represented in a
computer-processable way and the problem-solving
process performed by the system is supported by this
knowledge. Recently, ontologies have been applied
for representing the domain knowledge in knowledge-
based systems. Since an ontology specifies in a for-
mal and explicit way the shared conceptualization in
a given domain (Studer et al., 1998), its adoption al-
lows the knowledge reusing and promotes the seman-
tic interoperability among systems.
Knowledge-based systems can be applied when
there is no data available for applying bottom-up
approaches, such as those based on machine learn-
ing techniques. Besides that, in general, the results
achieved by knowledge-based systems are reliable,
since they are obtained by applying knowledge in
which the practitioners of the domain rely on. How-
ever, for developing these systems it is necessary to
elicit the relevant knowledge from reliable knowledge
sources (domain experts, domain literature, etc). This
is one of the weak points of adopting this approach,
since the knowledge acquisition is a notoriously diffi-
cult task, involving costly and error-prone processes
that, in general, depends on the availability of do-
main experts. Besides that, sometimes, the elicited
knowledge is not enough to cover all the situations
with which the application should deal.
In this position paper, we claim that these weak-
nesses of the knowledge-based systems can be mit-
igated by providing to the system the capability of
extracting useful knowledge from the available data;
that is, from previously solved instances available to
the system. The resulting approach can be viewed
as a combination of knowledge-driven (top-down)
and data-driven (bottom-up) approaches. Hybrid ap-
proaches like this were already proposed in the liter-
ature (Li and Love, 1999). However, in this paper,
we go a step further, by proposing a hybrid cognition-
inspired knowledge representation approach for sup-
porting this kind of system.
Within the cognitive sciences there is an ongoing
debate concerning how the knowledge is represented
in the human mind. According to (Murphy, 2002),
there are three main theories in this debate. The clas-
sical theory assumes that each concept is represented
by a set of features that are shared by all the entities
that are abstracted by the concept. In this way, this set
of features can be viewed as the necessary and suf-
ficient conditions for a given entity to be considered
an instance of a given concept. In this theory, con-
cepts are viewed as rules for classifying objects based
on features. The prototype theory, on the other hand,
states that concepts are represented through a typi-
cal instance, which has the typical features of the in-
stances of the represented concept. Finally, the exem-
plar theory assumes that each concept is represented
by a set of exemplars of it, which are explicitly rep-
resented in the memory. These exemplars are enti-
ties that were previously experienced by the agent. In
644
Carbonera J. and Abel M..
A Cognition-inspired Knowledge Representation Approach for Knowledge-based Interpretation Systems.
DOI: 10.5220/0005467106440649
In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 644-649
ISBN: 978-989-758-096-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
theories based on prototypes or exemplars, the cat-
egorization of a given entity is performed according
to its similarity with prototypes or exemplars; the in-
stance is categorized by the category that has a pro-
totype (or exemplar) that is more similar to it. There
are some works, such as (Fiorini et al., 2014), which
apply these alternative theories in computer applica-
tions.
Our main proposal in this position paper is to
combine classic representations (such as ontologies
and rules), prototypes and exemplars in a knowledge
representation framework for supporting knowledge-
based systems. The resulting framework combines
the strengths of the knowledge-driven (top-down) and
data-driven (bottom-up) approaches, overcoming the
weaknesses of both. In our framework, part of the
knowledge is represented classically, using ontologies
and rules, and part of the knowledge is represented
as prototypes and exemplars, which can be extracted
from the data that is processed by the system. Thus,
our approach can provide the reliance of the expert
knowledge when it can be applied, however, it also
is able to provide solutions for cases that cannot be
covered by this knowledge, but that can be estimated
from the available data. In this approach, the result-
ing system can perform problem-solving processes
by combining rule-based reasoning with similarity-
based reasoning. The rule-based reasoning can be
used first for reducing the search space and, if a suit-
able solution is not found, a similarity-based reason-
ing component can be triggered for finding the suit-
able solution, by comparing the instance that is being
analyzed with the available prototypes and exemplars.
In our project, our main focus of interest are
knowledge-based systems for data interpretation, in
the domain of Petroleum Geology. In this paper,
we discuss how to use our approach for developing
a knowledge-based system for visual interpretation
of depositional processes. This task is a resource-
consuming job, which relies intensively on the visual
knowledge of geologists, and that is considered a cru-
cial step in petroleum exploration.
2 SEDIMENTARY
STRATIGRAPHY
Since we will discuss an application for the Sedimen-
tary Stratigraphy field, in this Section, we will present
an overview of the domain. Sedimentary Stratigraphy
is a sub-field of Geology that studies the sedimentary
terrains in the surface or subsurface of the Earth, in
order to determine the geological history of their for-
mation. The main objects of study and description
are Body of Rock, Well Core, Outcrop, Sedimentary
Facies, Sedimentary Structures and Depositional Pro-
cesses. A body of rock can be a well core, which is
a cylinder of rock extracted from the subsurface by
means of drilling; or an outcrop, which is a body of
rock exposed in the surface. A sedimentary facies is a
region in a body of rock, visually distinguishable from
adjacent regions. Each sedimentary facies is a di-
rect product of a depositional process. A sedimentary
structure is the external visual aspect of some internal
spatial arrangement of the rock grains. Finally, de-
positional processes are events that involve the com-
plex interaction of natural forces and sediments, and
which are responsible for the formation of sedimen-
tary rocks. Figure 1 presents an example of a well
core, emphasizing two distinct sedimentary facies.
Figure 1: a well core, emphasizing two distinct sedimentary
facies (adapted from (Lorenzatti et al., 2009)).
In the task of visual interpretation of depositional
processes, the geologists visually inspect sedimentary
facies (in well cores or outcrops) and interpret which
was the corresponding depositional process that was
responsible by the formation of this facies.
3 OUR APPROACH
In this section, we will present our approach and how
it can be applied in a knowledge-based system for vi-
sual interpretation of depositional processes.
In our approach, we assume the availability of
static knowledge about the domain, represented as a
domain ontology. In our application, we adopted a do-
main ontology for Sedimentary Stratigraphy. Figure
2 represents an excerpt of our domain ontology, pre-
senting the core domain entities that were explained
in Section 2. Figure 3 represents an excerpt of the
taxonomy of depositional processes.
This domain ontology is used in our application
for providing a formal basis for describing facts about
the domain and for articulating the inferential knowl-
edge (rules), which are used for performing the in-
terpretation. Our system takes as inputs descriptions
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*
1
1
1
1
*
Chemical and diagenetic structure
Depositional structure
Sedimentary faciesBody of rock
Sedimentary structure
Biogenic structure
Deformation structure
Well core Depositional processOutcrop
hasSedimentaryStructure
generatedBy
hasSedimentaryFacies
Figure 2: Representation of the core domain entities of the
ontology. This excerpt omits the attributes of the entities
and the other sub-types of sedimentary structure.
of bodies of rock, which are made according the on-
tology. When the user requires the execution of the
interpretation process, the application analyses each
sedimentary facies of the body of rock, interpreting
the respective depositional process that have gener-
ated it.
Regarding the visual interpretation task performed
by our system, following (Carbonera et al., 2015; Car-
bonera et al., 2013; Carbonera et al., 2011), we also
consider that it is inferentially characterized by the
application of rules in the form of
observation interpretation (1)
, where observation is a set of statements that de-
scribe the features that should be perceived in some
observable entities for supporting the interpretation,
and interpretation is a set of statements about inter-
pretable entities. In our application, the observable
entities are sedimentary facies and sedimentary struc-
tures and the interpretable entities are depositional
processes. In this way, the observation comprises
statements about features that should be perceived in
a given instance f of sedimentary facies for inferring
the interpretation, which specifies what is the spe-
cific type of depositional process (see Figure 3) that
was responsible by generating f . Following, it is pre-
sented an example of rule used in our application:
f , s, p SedimentaryFacies( f ) hasSorting( f , PoorlySorted)
hasLithology( f , Sandstone) TroughCrossStratification(s)
hasSedimentaryStructure(f , s) DepositionalProcess(p)
generatedBy( f , p) 3dDuneMigration(p)
(2)
This rule represents that a sedimentary facies f , which
is poorly sorted, whose lithology is sandstone and that
has a sedimentary structure s, which is a Trough-cross
Stratification (a sub-type of sedimentary structure);
was generated by a depositional process p, which is
a 3D dune migration (a sub-type of depositional pro-
cess).
It is important to notice that, in our domain of
interest, it is necessary to provide to the system the
capability of providing interpretations in several lev-
els of generalization/specificity. This is important be-
cause sometimes there is not enough information in
Figure 3: Representation of an excerpt of the taxonomy of
depositional processes.
the sedimentary facies description for supporting the
interpretation of the specific type of depositional pro-
cess that have generated the facies. We provide this
capability to our application by including a taxonomy
(with several levels of generalization) of depositional
processes in the domain ontology and by including
the necessary rules for interpreting each different type
of depositional process specified by this taxonomy.
The ontology, the rule base and the rule-based
reasoning engine constitute the knowledge-driven
component of our system. In general, it can be very
effective in reducing the set of alternative possible in-
terpretations of a given facies. It can prune entire
branches of alternatives in the taxonomy of deposi-
tional processes. It can even reach specific interpreta-
tions (some leaf of the taxonomy, such as 3D dune
migration), if the description of sedimentary facies
is detailed enough. However, this is not always the
case. It is common the absence of important infor-
mation in facies descriptions, because some features
are not identifiable by the geologist in the object, or
because some identified features are omitted during
the description. Besides that, the inferential knowl-
edge can be incomplete, in a way that the set of rules
cannot cover all the possible cases that arise in real
scenarios. In these cases, the knowledge-driven com-
ponent can only provide a more general interpretation
(such as Subaqueous tractive current, for example).
For overcoming these weaknesses, we propose a
data-driven component that acts in conjunction with
the knowledge-driven component. This component
relies on a knowledge representation approach that is
inspired by the prototype theory and exemplar theory
of knowledge representation in the human mind. Fol-
lowing this idea, we assume that for each possible rel-
evant interpretable entity ie, the system has a proto-
type and a set of exemplars of the observations that
can support the interpretation of ie. In this compo-
nent, the visual interpretation is carried out through
similarity-based reasoning, where the instances that
should be interpreted are compared with the proto-
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types and exemplars for determining the suitable in-
terpretation. In addition, since the system should ex-
tract the prototypes and exemplars from the available
data (that can include instances that were previously
solved by the system); it includes a component for ex-
tracting the prototypes and exemplars: the knowledge
extractor.
Before presenting the details of the data-driven
component, let us consider a formal characterization
of some important notions.
DS = {r
1
, r
2
, ..., r
n
} is a data set, which
can be abstractly considered as a set of
n records. Each r
i
can be viewed as
a 2-tuple (observation, inter pretation); where
interpretations are articulated using concepts in
the domain ontology that are generically called
Interpretable entities, and observations are artic-
ulated using concepts in the domain ontology that
are generically called observable entities.
O
DS
= {o
1
, o
2
, ..., o
n
} is the set of the
observations of the records in DS, in a way
that o
i
O
DS
is the observation of the record
r
i
DS. There ir one observation for each record.
ro: DS O
DS
is a function that maps a given
record r
i
DS to its observation o
i
O
DS
.
IE = {ie
1
, ie
2
, ...ie
m
} is a set of m interpretable
entities, where each ie
i
is a concept provided by
the domain ontology. The elements of IE are or-
ganized in a taxonomy, which is a partially or-
dered set (IE, ), where is the subsumption re-
lation held between elements of IE.
P = {p
1
, p
2
, ..., p
m
} is a set of m prototypes of
observations, where each p
i
represents the typi-
cal observation whose interpretation is specified
by the interpretable entity ie
i
.
E = {es
1
, es
2
, ..., es
m
} is a set whose elements
are sets of exemplars, such that, each es
i
=
{e
1
, e
2
, ..., e
k
} is a set of k exemplars of observa-
tions whose interpretation is specified by the in-
terpretable entity ie
i
.
prototype : IE P is a function that maps a given
interpretable entity ie
i
IE to the prototype p
i
P, which represents the typical observation hose
interpretation is specified by ie
i
IE.
exemplars: IE E is a function that maps a
given interpretable entity ie
i
IE to the set es
i
of exemplars, such that es
i
E, which represents
the set of k exemplars of observations whose in-
terpretation is specified by the interpretable entity
ie
i
.
records: IE 2
DS
is a function that maps a given
interpretable entity ie
i
IE to the set DS
ie
DS
of records in DS, which represents the set of
records in DS whose interpretation is specified by
the interpretable entity ie
i
.
obs : IE 2
O
DS
is a function that maps a given
interpretable entity ie
i
IE to a set O
ei
O
DS
,
which represents the set of observations related to
the records specified by records(ei), where o
i
O
ei
, r
i
records(ei), o
i
= ro(r
i
).
The user data in the system database is stored as
a set of instances of the domain ontology. However,
for the purposes of the data-driven component, this
database can be abstractly viewed as the dataset DS,
where observations (including individual exemplars
of observations in the set E, and typical observations
in the set P) can be viewed as vectors of features of
length l, called observation vectors. The vectorial
representation is preferable for understanding how to
perform similarity judgments. Figure 4 presents the
process of representing instances of our domain ontol-
ogy as vectors of features. Notice that, in our applica-
tion, observations consist basically in descriptions of
instances of sedimentary facies (our observable enti-
ties), and interpretations are basically types of depo-
sitional processes (our interpretable entities). Thus,
in the resulting observation vectors, each attribute of
Sedimentary Facies is represented as a position of the
resulting vector of features. Besides that, the result-
ing vector has a special position for representing the
type of the Sedimentary Structure of the Sedimentary
Facies.
Figure 4: Representation of the process of converting the
instance of Sedimentary facies (represented according the
domain ontology) in a vector of features.
The extraction of prototypes of observations from
data is performed by the knowledge extractor (in the
knowledge-driven component). This component ex-
tracts a prototype of the observations whose interpre-
tation is some interpretable entity ie (obs(ie)). It ba-
sically analyses the observations in obs(ie) and deter-
mines the typical value of each one of the l attributes
of the observations. If the attribute is numeric, the
typical value can be the average; if the attribute is
nominal, the typical value can be the most frequent.
The extraction of a set of exemplars of obser-
ACognition-inspiredKnowledgeRepresentationApproachforKnowledge-basedInterpretationSystems
647
vations from data is also performed by the knowl-
edge extractor, in the knowledge-driven compo-
nent. Considering a given ie IE, the exemplars
in exemplars(ie) will be used by the system as ref-
erences for interpreting a new observation o
new
, by
performing judgments of the similarity between o
new
and each considered exemplar in exemplars(ie). In
our approach, we assume that it is not desirable to
consider all records in obs(ei) as exemplars for rep-
resenting the observations in obs(ei), since the com-
putational cost of the interpretation process is pro-
portional to the number of exemplars that are se-
lected for representing the observations. For reduc-
ing the computational overhead of the interpretation
process, in our approach we consider that the num-
ber of exemplars related to each interpretable en-
tity ei
i
IE is defined as a percentage ep (defined
by the user) of the total number of observations in
obs(ei
i
). This raises the problem of how to select
which observations in obs(ei
i
) will be consider as
the exemplars in exemplars(ei
i
). We select three
main criteria that an observation o
e
obs(ei
i
) should
meet for being included in exemplars(ei
i
), consider-
ing an interpretable entity ie: (i) o
e
should have a
high degree of dissimilarity with the prototype given
by prototype(ie); (ii) o
e
should have a high degree
of similarity with a big number of observations in
obs(ei
i
); and (iii) o
e
should have a high degree of
dissimilarity with each exemplar already included in
exemplars(ei
i
). This set of criteria was developed for
ensuring that the set of exemplars in exemplars(ei
i
)
will cover in a reasonable way the spectrum of vari-
ability of the observations in obs(ei
i
). That is, our
goal is to preserve in exemplars(ei
i
) some uncom-
mon observations, which are not well represented by
prototype(ie), but that represent the variability of the
observations. In our approach, we apply these crite-
ria, by including in exemplars(ei
i
) the h first observa-
tions from obs(ei
i
) that maximize their exemplariness
index. The exemplariness index is computed using the
notion of density of a given observation (represented
in the form of an observation vector). Regarding some
interpretable entity ie, the density of some observa-
tion o
i
obs(ie), is computed by the function density,
such that, density(o
i
) =
1
|obs(ie)|
|obs(ie)|
j=1
d(o
i
, o
j
);
where d is some dissimilarity (or distance) function (a
function that measures the dissimilarity between two
entities). Considering this, the set exemplars(ei
i
) of
some interpretable entity ie, with h exemplars, can be
computed by the Algorithm 1.
Notice that the Algorithm 1 basically selects from
obs(ie), the observations that maximize the exemplar-
iness index, which is the sum of: (i) distance (or dis-
similarity) of the observation from the prototype(ie);
Algorithm 1: extractExemplars.
Input: An interpretable entity ie and a number h of exemplars
Output: A set exemplars
ie
of h observations representing the
exemplars of the observations whose interpretation is
specified by ie.
begin
exemplars
ie
;
for j 1 to h do
eIndex
max
;
obs
max
null;
foreach o
i
obs(ie) do
density density(o
i
);
d p d(o
i
, prototype(ie));
med 0;
if exemplars
ie
is not empty then
Compute the distance between o
i
and each exemplar e
already included in exemplars
ie
and assign to med the
distance of the nearest exemplar from o
i
;
/* eIndex is the exemplariness index */
eIndex = d p + density + med;
if eIndex > eIndex
max
then
eIndex
max
eIndex;
obs
max
o
i
;
exemplars
ie
exemplars
ie
obs
max
;
return exemplars
ie
;
(ii) the density of the observation, considering the set
obs(ie); and the distance (or dissimilarity) of the ob-
servation from its nearest exemplar, already included
in exemplars
ie
.
Once the system has the hybrid knowledge rep-
resentations (prototypes and exemplars), they can be
applied by the interpretation engine for performing
interpretations. This component takes as input a
new observation and provide its corresponding inter-
pretation. In our application, the observation is an
ontology-based description of a sedimentary facies,
and the interpretation is a specific type of depositional
process that was responsible by the formation of the
facies. Firstly, the interpretation engine applies rule-
based reasoning (using classical knowledge represen-
tations in the form of a domain ontology and a rule
base) for providing a first set of hypothesis. Notice
that the rule-based reasoning can infer more than one
interpretation for the same observation, depending on
the rules in the rule base. If the rule-based reason-
ing provides interpretations that are not specific (the
leaves of the taxonomy of interpretable entities in the
domain ontology), the similarity-based reasoning can
be used for determining more specific interpretations.
The visual interpretation engine implements the Al-
gorithm 2.
Notice that the Algorithm 2 uses the notion of ap-
plicability, which, intuitively measures the degree in
that a given interpretable entity ie can be applied as
an interpretation for a given observation obs. The ap-
plicability is computed by the Algorithm 3, using the
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648
Algorithm 2: visualInterpretation.
Input: An observation obs.
Output: A set int
set
of interpretable entities representing the
interpretations of obs.
begin
int
set
;
Perform the rule-based reasoning for interpreting obs (applying
the rules in the rule base), and include the interpretable entities of
the resulting interpretations in int
set
;
if the interpretable entities in int
set
are not specific then
hyp
set
;
foreach ie int
set
do
Find the leaves in the taxonomy of interpretable entities,
whose root is ie, and include them in hyp
set
;
int
set
;
MAX ;
foreach ie hyp
set
do
app applicability(ie, obs);
if app > MAX then
MAX app;
int
set
;
int
set
int
set
ie;
else if app = MAX then
int
set
int
set
ie;
return int
set
;
Algorithm 3: applicability.
Input: An interpretable entity ie and an observation obs.
Output: A value r R, which is the degree in that ie can be applied
as an interpretation for obs.
begin
app 0;
pSimilarity sim(obs, prototype(ie));
eSimilarity 0;
Calculate the similarity sim(obs, ex
i
) between obs and each
ex
i
exemplars(ie), and assign to eSimilarity the similarity value
of the most similar ex
i
;
app pSimilarity + eSimilarity;
return app;
prototypes and exemplars of the observations in
obs(ie).
Notice that the Algorithm 3 uses the function sim
for measuring the similarity. Intuitively, the similar-
ity is the inverse of the dissimilarity (or distance) be-
tween two observations. Thus, sim has values that
are inversely proportional to the values obtained by
the function d. Here, we assume that sim(o
i
, o
j
) =
exp(d(o
i
, o
j
)).
4 CONCLUSIONS AND FUTURE
WORKS
In this paper, we propose a cognition-inspired
knowledge representation framework for supporting
knowledge-based systems. The resulting framework
can combine the strengths of the knowledge-driven
(top-down) approaches, overcoming the weaknesses
of both. In future works, we intend to provide a de-
tailed account of the whole systems developed us-
ing this approach, and to present a validation of the
approach in real cases. In addition, we plan to in-
vestigate how the system can update its representa-
tions (prototypes and exemplars), when new data is
included in its database.
ACKNOWLEDGEMENTS
We gratefully thank to PRH PB-217 program (sup-
ported by Petrobras), for the financial support to this
work, and to Sandro Fiorini for comments and ideas.
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