COMET: An Ontology Extraction Tool based on a Hybrid

Modularization Approach

Bernabé Batchakui

, Emile Tawamba

and Roger Nkambou

National Advanced School of Engineering, University of Yaoundé 1, Yaoundé, Cameroon

University Institute of Technology, University of Douala, Douala, Cameroon

Department of Computing, University of Quebec at Montreal, Montreal, Canada

Keywords: Structural Modularization, Semantic Modularization, Segmentation Algorithm, Stuckenschmidt Approach,

Cuenca Grau Approach, Wu and Palmer Measurement, OWL Module Extractor, Ontology Segmentation.

Abstract: The design of ontologies is a non-trivial task that can simply be reduced to the reuse of one or more existing

ontologies. However, since an expert in knowledge engineering would only need a part of the ontology to

perform a specific task, obtaining this partition will require the modularization of ontologies. This article

proposes a tool named COMET, based on hybrid modularization, composed of existing structural and

semantic modularization techniques, that, from an ontology and a list of input terms, generates, according to

an integrated segmentation algorithm, a module which in fact is a segment consisting only of concepts deemed

relevant. The segmentation algorithm is based on two parameters which are hierarchical deep and semantic

threshold.

1 INTRODUCTION

The development of Web technologies has brought

about an increased interest in the research for

knowledge sharing and integration in a distributed

environment. The Semantic Web tends not only to

render information accessible but also readable and

usable through data processing applications. The

latter provides the tools which permit a better

organization of information through extracting,

sharing and reusing the specific knowledge in a

domain. In this perspective of sharing and

interoperability, the proliferation and availability of

ontologies are crucial. It goes without saying that

ontologies have become an inevitable pattern for the

representation and reasoning on domain knowledge.

Though essential to the system’s management on

knowledge basis, it does not refute the fact that the

conception, the reuse and integration of ontologies

remain complex tasks. In the present situation, the

authors quickly realize that modularization will be an

effective approach which will make it possible to

provide answers to a good number of problems of

expert knowledge. The authors will examine the

https://orcid.org/0000-0002-5287-4207

https://orcid.org/0000-0003-1486-9922

modularization as a process during which, a set of

pertinent concepts of a given domain is identified.

Modularisation of ontologies is an important field of

research with many recent publications such as

(Leclair and al, 2019; De Giacomo and al, 2018; Xue

and Tang, 2017; Khan and Keet, 2015; Algergawy

and al., 2016; Babalou and al, 2016; Movaghati and

Barforoush, 2016). Each of these works addresses

modularisation either on the structural or on the

semantic view. The notion of modularization is based

on the principle of «divide and rule» generally

applied in software engineering which is about

developing an application whose structure depends

essentially on autonomous components easily

conceivable and reusable. The module thus represents

a component of software which executes a precise

task and interacts with others. In ontological

engineering, modularization can be perceived in two

ways. Firstly, it can be seen as a process leading to

the decomposition of a large ontology into

ontological modules of small sizes. Secondly, it can

equally be perceived as a stage in the ontology

construction process which is done through the

conception of a set of ontological modules

Batchakui, B., Tawamba, E. and Nkambou, R.

COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach.

DOI: 10.5220/0010688300003064

In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD, pages 74-83

ISBN: 978-989-758-533-3; ISSN: 2184-3228

independent from one another. Modularization

therefore is summarized in selecting concepts and

pertinent relationships based on the task and the

application for which the modeling of an ontology is

decided. To do this, the researchers may:

 Reuse all the ontology in entirety. All the

concepts and relationships of domain ontology

will be included in the ontology which is

conceived. This results in a great weighing on

the ontology application which will be as big as

if the domain ontology is large. In such a case,

the authors would discover concepts and

definitions which are not necessarily relevant

to the task at hand.

 Build a new ontology. This will represent a

fastidious work. The conception of an ontology

remains a complex task in the sense that it is

expected of the expert to define all the concepts

and relationships necessarily.

 Reuse of part of the initial ontology. It is a

compromise between the two preceding

alternatives. In this case, the application

ontology will therefore represent a part or a

module of the domain ontology. There exist

two principal modularization techniques of

ontologies (Algergawy A. and al, 2016.). The

authors distinguish the techniques based on the

partitioning of ontology and the techniques

based on the extraction of ontological module.

The partitioning subdivides an ontology into a

set of sub structures; autonomous or dependent

on each other called partitions, meanwhile the

module extraction consists in extracting a sub

ontology based on a precise signature. Whether

it deals with the ontology partitioning or

module extraction, it is remarked that these

techniques are based on structural and semantic

approaches.

The structural extraction module approaches bring

the problem of modularization to the extraction of a

graph which in essence is a sub structure containing

pertinent concepts in relation to a previously fixed

signature. While the semantic approaches are based

essentially on the very usage of ontology, putting in

evidence the way concepts are linked one to the other.

That is called the semantic proximity. However, they

quickly realized that each of these approaches present

limitations for which they will delve in to provide

preliminary solutions in proposing a hybrid approach

name COMET. The latter draws from the better of the

two worlds in combining at the same time the structural

and semantic approaches so as to consolidate the

strengths of these.

The remaining parts of this paper are organized

into five sections as follows: the first section is the

state-of-the-art on existing approaches to

modularization techniques. The authors present the

particularities and limitations of these approaches by

demonstrating the need to develop a new approach.

The next section concerns the COMET

modularization approach. The third section is the

experimentations and results of COMET where

validation protocol is presented and the results of the

experimentations. The fifth section is concerned with

the analysis of the results. The conclusion of this

paper will be presented in the last section.

2 STATE OF THE ART

An ontology O is defined by the following formula

(Palmisano, Tamma, Payne, & Doran, 2009):

(Ax(O),

(

))

where Ax(O) represents the

set of axioms made of concepts, relationships and

functions (sub class, equivalence, instantiation,

etc…). Sig(O) is the signature of the O which

represents the set of entity names which are found in

the axioms. In other words, it refers to the O

vocabulary. The ontology modularization of O

permits the definition of a module M as:

M = (Ax(M), Sig(M))

where M is part of O, (Ax(𝑀))



⊆ (𝐴𝑥(𝑂))



and

Sig (M) ⊆ Sig (O), I is the interpretation. In fact, it is

a basis of the description semantic logic. It is

symbolize by I = (Δ



, ●



) where Δ



is the domain

interpretation and ●



the interpretation function.

There are two main approaches to modularizing

ontologies: approaches based on ontology

partitioning and approaches based on ontology

module extraction.

Partitioning is the process during which an

ontology O is fractioned into a set of M modules (not

necessarily disjoint) such that the union of

interpretation of all the partitions thus creates the

equivalence of the initial ontology interpretation O.

Formally, the ontology partitioning can be defined as

follows:

𝑀 = {𝑀



,𝑀



,.. , 𝑀



{

(

𝐴𝑥(𝑀



)



𝑈 𝐴𝑥(𝑀



)



𝑈…𝑈 𝐴𝑥

(

𝑀



)



} =

{Ax(𝑂))



the authors distinguish two principal partitioning

approaches:

 The approach of the Stuckenschmidt and al. is

based uniquely on the hierarchical structure of

classes (Stuckenschmidt & Klein, 2004). This

COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach

approach is founded on the hypothesis that the

dependence between the concepts could be

derived from the structure of the ontology. The

latter is represented by a weighted graph O = <

C, D, W > where the nodes (C) represent the

concepts and the crests (D) represent the

relationships between concepts, and the weight

(W) varies as a function of the dependence.

 The approach of the Cuenca Grau and al. on the

other hand is based on ε-connections (Σ) (Grau,

Parsia, Sirin, & AdityaKalyan, 2005). These

are used as the language of definition and

ontologies combination instantiation OWL-

DL. The partitions generated by Cuenca Grau

and al. are at the same time structurally (Σ ∼ O)

and semantically compatible (Σ ≈ O). The

structural compatibility has as objective to

guarantee that no entity or axiom should be

added, removed or modified during the

partitioning. The role of the semantic

compatibility is to guarantee the preservation

of the interpretation module.

The extraction of the ontology module is a process

during which an M module covering a specific

signature is extracted from an ontology O, as in

Sig(M) ⊆ Sig(O). M is the pertinent part of O which

covers the entire elements defined by Sig (M). M

module is an ontology of its own, as such, other

modules can be extracted from it. Formally, the

module extraction can be defined as follows:

extracting

(O, Sig(M)) ⟶ {𝑀|(𝐴𝑥

(

𝑀

)



⊆ (𝐴𝑥

(

𝑂

)



}

The most evident way to visualize an ontology is

to represent it in a graph where the peaks consist of

concepts and individuals and the crests are the

relationships (hierarchies and semantics) (Dupont,

Callut, Dooms, Monette & Deville, 2006). The

problem of module ontology extraction will then be

brought to the extraction of a sub graph containing the

most pertinent concepts in relation to the list of terms

entered by the user. There is the necessity to evaluate

the pertinence of concepts one from the other. The

authors can refer to the notion of distance in the sense

that the most pertinent will be the closest to the

starting concepts in a certain predefined range by the

user, and for which the inter-concepts distance could

be evaluated by the calculation of the shortest path of

Dijkstra. This approach will be realizable, most

assuredly since there exist a Java library called

JUNG, which enables the transformation of an

ontology into a graph.

The advantages of this structural approach are

many. Firstly, there already exist algorithms effective

in extraction of sub graphs. Supposing that the

ontology from which the module to be extracted is of

a satisfactory quality, this approach does not re-

structure the ontology in the sense that the extracted

module conserves the same internal structure as the

original ontology. And should the graph be any least

dense, the complexity of the algorithm is reduced. On

the contrary, JUNG considers by default that all the

crests of the graph are equivalent. On this basis, it

becomes impossible to correctly evaluate the nodes

and arcs, even as in an ontology, the relationships

between different concepts are never equivalent. More

so, this method ignores the concepts distance from the

beginning concepts but which can also be pertinent.

The second way of seeing an ontology is to

consider it through its utility which is the

representation of knowledge. In other words, when it

comes to the definition of concepts which are useful

in relation to the others, in order to understand the

domain which the ontology describes, the authors

speak of semantic domain. In making a total

abstraction of the structure of ontology, the authors

shall be taking concepts two by two in order to see if

they are semantically close (Jiang & Conrath, 1997).

Different from the structural approach, the authors

make allusion here to a semantic distance which will

allow us evaluate the semantic proximity of ontology

concepts in relation to those corresponding to the

terms entered by the user.

The advantages of the semantic approach are the

pertinence and precision, because, what constitutes

the principal strength of an ontology remains its

semantics (Ghosh, Abdulrab, Naja & Khalil, 2017b).

Suffice for a concept to be linked to a bad concept for

the quality of ontology to be altered, and so, can led

us to an ontology which does not tie anymore with

reality. The semantic approach permits us to put

ontology from start and to test the semantic similarity

of all the sets of concepts of ontology. Meanwhile,

seeing that the authors work with large ontologies, to

calculate the semantic distance of all the sets of

concepts seems very much utopic in terms of the

algorithmic complexity and the execution time. It

therefore becomes primordial to turn to other

alternatives which will permit us to still consider the

ontology structure instead of completely ignoring it.

3 COMET MODULARIZATION

APPROACH

In order to cope with the current limitations of

existing modularization approaches, the authors

KEOD 2021 - 13th International Conference on Knowledge Engineering and Ontology Development

propose the hybrid COMET approach that integrates

both the structure and semantics of the ontology. The

modularization process with COMET is done in 2

steps: the segmentation of the ontology and the

extraction of the module.

Segmentation is the first phase of the extraction

process. It is based on the assumption that when a

concept is pertinent, then its sub concepts are equally

pertinent (Stuckenschmidt & Schlicht, 2006)( Ghosh,

Abdulrab, Naja & Khalil, 2017a). The segmentation

phase therefore consists in determining the limits of

the module. To do this, the user must first of all enter

a list of terms, then define a semantic threshold and a

hierarchical depth. Once all these parameters have

been defined, the algorithm will first of all search for

the concepts corresponding to the list of terms and

will save them in a formerly defined stack: this is the

initialization. This stack containing the starting

concepts is the initial core of the module. The purpose

of this approach is to extend the base over time as the

semantically close concepts are discovered. Thus for

each concept, it is verified that it possesses semantic

relationships stemming from the object type. In the

case where there is, then for each of these

relationships, the concept to which it is semantically

linked will be searched and the distance between

these two concepts will be evaluated. In a scenario

where this distance is greater than or equal to the fixed

semantic threshold, the new concept thus discovered

will be added to the stack containing the list of

pertinent concepts. Also, depending on the user's

defined hierarchical depth, the offspring of the

concept judged pertinent will equally be added to the

stack. In the event where the semantic distance is less

than the threshold and cannot find a semantically

linked concept to the pertinent concept already

present in the stack, the algorithm will call upon the

hierarchy of ontology to verify if the initial concept

has sub concepts. If sub concepts exist, then for each

of them, the algorithm will start searching again for

semantic relationships. And, if there exists one, it will

evaluate the semantic inter-concept distances in

relation to the threshold. In a case where there exists

no semantic relationships or if the threshold is still not

attained, the algorithm will search even further in the

hierarchy until it finds a concept with satisfactory

semantic relationships. The calculation of the

semantic distance is done using Wu and Palmer's

distance, which is a practical and intuitive measure

based on the length of the path between two concepts

of the same hierarchy. It calculates the distance

separating two concepts in the hierarchy according to

their position relative to the root (Wu & Palmer,

1994). It is obvious that two concepts found at the

same depth in the hierarchy will have a higher

similarity than concepts at different levels of the

hierarchy. The measurement of Wu and Palmer is

defined as follows:

𝑆𝑖𝑛(𝐶



+ 𝐶



2 + dept(C)

𝑑𝑒𝑝𝑡ℎ



(

𝐶



)

+𝑑𝑒𝑝𝑡ℎ



(𝐶



)

where c, the most accurate common subsuming,

depth(c) is the length of the path between c and the

root of the hierarchy, 𝑑𝑒𝑝𝑡ℎ



(𝑐



) is the number of

arcs between 𝑐



and the root passing through c. This

measure ranges between 0 and 1. The segmentation

algorithm is presented by the following codes.

Algoritm COMET_Segmentation

Require: Ontology O

Require: domain : OntClass

Ensure: relevantClasses : Vector<OntClass>

1: procedure computeRelevantClasses(domain)

2: relations ← getOutgoingObjectProperties(O, domain)

3: if (relations.size() > 0) then

4: for each op

∈

relations do

5: range ← op.getRange()

6: if range 6= 0 then

7: if SemanticDistance(domain, range) > threshold then

8: if relevantClasses.contains(range) then

9: continue

10: else

11: relevantClasses.add(range)

12: end if

13: else

14: if domain.hasSubclass() then

15: relevantSubClasses ← domain.listSubClasses()

16: for each c

∈

relevantSubClasses do

17: computeRelevantClasses(c)

18: end for

19: end if

20: end if

21: else

22: relevantClasses.remove(range)

23: end if

24: end for

25: else

26: if domain.hasSubclass() then

27: relevantSubClasses ← domain.listSubClasses()

28: for each c

∈

relevantSubClasses do

29: computeRelevantClasses(c)

30: end for

31: end if

32: end if

33: return relevantClasses

34: end procedure

After segmentation, the second phase of the

modularization process follows, it is the extraction of

the sub ontology that has been identified and stored

in memory. To do this, the authors proceed with the

pruning of the graph (tree) representing the ontology

in looking through its entirety in order to delete non-

pertinent concepts. It is important to indicate that the

authors work with two sets of data, one containing all

COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach

the relevant concepts and the other containing all the

concepts judged irrelevant. When a concept is deleted

from the ontology, all the semantic and hierarchical

relationships that index it are equally deleted. Once

pruning is completed, the new ontology will be

exported into a file. Pruning is by far the most suitable

technique for the extraction phase, in that it would

have been clearly more complex to save both the

relevant concepts and their differing properties in data

structures (list type) and uniquely from these, to

generate the ontological module.

Figure 1, illustrate the entire modularization

process from COMET, with input parameters: a list

of terms composed of a and b, a semantic threshold

fixed at 0.4, and a hierarchical depth of 1. At the start,

the authors have an ontology of 15 concepts and after

modularization, the authors obtain a module of 7

concepts. The authors note that the concepts d, g and

i are identified and added via hierarchical

relationships according to the depth fixed in relation

to a pertinent concept already present in the stack.

Figure 1: Modularization process with COMET.

4 EXPERIMENTATIONS AND

RESULTS OF COMET

A module is an ontology, and as such, the quality

assessment approaches of ontologies apply equally to

it. Although some evaluation techniques analyze the

structure of the ontology represented by the content

of the ontology, other techniques focus mainly on the

comparison of the content of the ontology itself,

either with another existing ontology whose quality

would have been judged satisfactory, or with another

alternative representation of the domain of

knowledge in question, such as a corpus of

documents. This comparison is made in order to

determine the extent to which this content modulates

all relevant aspects of the domain to be described.

Based on these principles and in order to finalize their

validation protocol, the authors will first of all define

their evaluation criteria and then establish the

procedure by which the different extracted modules

will be evaluated.

4.1 The Validation Protocol

The validation protocol is essentially based on a

comparative analysis of the modules obtained by

COMET to those generated by the OWL Module

Extractor1 and SegmentationApp tools. It is in fact a

Gold standard type evaluation having as reference the

ontology from which the different modules are

extracted (Dellschaft & Steffen, 2006). The aim of our

validation protocol is to evaluate both the lexico-

semantic aspect and the structural aspect of the

different modules. To do this, in addition to

OntoMetrics API, the authors also use the OntoEval

API to calculate the precision and recall of modules as

a function of those obtained by the reference ontology.

Each of these APIs take two parameters for input: the

reference ontology and the generated or calculated

ontology which, in this case is the extracted module.

The API evaluation process is therefore carried

out in three phases. In the first, the API evaluates the

source ontology according to Alani and al. This

evaluation consists first of all in calculating the

quality metrics of the source ontology, then in

assigning the maximum score which is 100% to each

returned metric result. This systemic attribution of a

maximum score is justified by the fact that our source

ontology is considered as an ontology of reference,

that is, an ontology of satisfactory quality.

In the second phase, the modules are evaluated

in turn using these same APIs. The metrics are

calculated for each module. The results obtained in

the calculations of the module's quality metrics are

compared with those of the reference ontology. Based

on the scores attributed to those of the reference

ontology, the scores of the metrics in the module are

calculated. In order to illustrate this approach of

evaluation, let us consider an ontology O and a

module M for which the density DEM is calculated.

At the end of the calculation of the density of the

initial ontology O, the authors obtain a result of

DEMO = 12.22; while the calculation of the density

of the module gives a result of DEMM = 5.21.

In making a connection between the densities of

the module to that of the reference ontology, the

authors obtain the score of the module density, which

in this case, is of 42.63%.

𝑆𝑐𝑜𝑟𝑒









(%)

KEOD 2021 - 13th International Conference on Knowledge Engineering and Ontology Development

The last phase of the evaluation consists simply of

comparing the metric scores of the modules generated

by COMET, OWL Module Extractor and

SegmentationApp amongst them in order to

determine which one is most relevant depending on

the metrics used.

To summarize the validation protocol, the authors

compare each module generated from the different

extraction tools with the source ontology to obtain

scores. Then, compare the scores of the naked

modules with each other in order to determine the

highest score. It is the modules with the best metric

scores or the best total score that allows the judgment

of the efficiency of an extraction tool.

It is expected that the module that will maximize

the characteristics of the reference ontology will end

up with the highest scores. The metric scores depend

on factors such as the number of concepts, the number

of properties and the taxonomy of the module or the

ontology of reference to name just a few. Table 1and

2 shows all ontologies modularized, the signature of

the module (the list of terms to be covered), the

extraction tools together with the size of ontologies

and the different modules extracted from them. As for

the tests, the authors have fixed the hierarchical depth

of COMET at 1 while the semantic threshold S is

variable.

Table 1: Ontologies and terms.

Ontologies #C Terms

sweto simplified 115 Person, Event

cmt 29 Co-author, Review

confOf 38 Participant, Conference

iasted 140 Delegate, Activity, Place,

Fee, Deadline, Submission

Conference 59 Submitted contribution,

Topic

edas 103 Author, Paper, Workshop,

Program, SocialEvent, Call,

Country, Document,

ReviewRating, Rejectedpaper

paperdyne 45 Conference, Location

OpenConf 62 Paper Review, People

ekaw 73 Review, Possible Reviewer

sigkdd 49 Author, Award, Sponzor

travel 34 Destination,

AccommodationRating

PPOntology 88 Case, Biotic Disorder,

Organism, Disorder, Mineral, Plant

Observatio, Pesticide, Abnormality,

Bacterium, Roots

OTN 179 Feature, Service,

Road Element, Manoeuvre,

Temperature, Face, Tourism,

Accident, Forest, Junction

Table 2: Extraction of modules using approaches from

Seidenberg and al., Cuenca and al. and COMET.

COMET CUENCA

SEIDENBERG

S = 0.2 S = 0.0 Bottom

Ratio

3,48%

18,26%

3,48%

24,14%

10,34%

82,76%

24,14%

26,32%

13,16%

26,32%

34,29%

40%

38,57%

16,95%

35,59%

38,98%

18,64%

19,42%

44,66%

45,63%

23,30%

40%

37,78%

40%

4,44%

35,48%

11,29%

46,77%

93,55%

5,48%

20,55%

5,48%

32,65%

40,82%

42,86%

32,65%

2,94%

44,12%

73,53%

19,32%

37,50%

46,59%

98,86%

13,97%

32,96%

29,05%

Average

21,11%

29,77%

39,43%

36,31%

Legende: column O = Ontologies.

A = sweto simplified, B =cmt, C= ConfOf, D = Conference,

E= iasted, F = edas, G=paperdyne, H=OpenConf, I=ekaw,

J = sigkdd, K = travel, L =PPOntology, M = OTN, #C =

Size.

4.2 Analysis of the Results

In order to determine the most efficient extraction

tool, the authors evaluated each module extracted

from the ontologies listed in Table 1 according to the

metrics of Alani and al. (Alani & Shadbolt, 2006).

Independent of the ontology to modularize and the

modules generated, it is a question of calculating the

score of each metric used, then comparing the scores

of the modules obtained between them. Each of the

metrics is intended to evaluate a specific aspect of the

ontological module.

Class Matching (CMM) assesses the ability of an

ontology to cover a given set of terms. In this case, it

is intended to calculate the percentage of both exact

and partial matching between the classes of the

module and those of the reference ontology, in order

to find the degree of representation of the classes of

the initial ontology in the extracted module. It is

obvious that the larger the module, the more likely it

is to cover the set of concepts (corresponding to the

list of searched terms) presented in the initial

ontology. After analyzing of Table 1, it is noted that

in most cases of ontologies studied, the COMET

modules obtain a better score from the CMM, which

suggests that the modules they generate are more

representative of the initial ontology.

The density (DEM) expresses the degree of

precision of a given concept, that is, the richness of

COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach

its attributes. This definition implies that an adequate

representation of a concept must be able to offer as

much information as possible about it. The density

depends essentially on the number of subclasses, the

number of attributes associated with this concept or

betters still, the number of related concepts. In the

present case, it is observed that the modules generated

using COMET have a significantly higher DEM score

than those extracted from the other tools (Figure 3).

This is explained by the fact that COMET crosses the

ontology horizontally through semantic relationships

in order to search for the relevant concepts and once

they have been identified, the hierarchy is gone

through in order to add their descendants to the

module. However, it should be remembered that in

the case of the COMET tool, the hierarchic depth is a

parameter which, like the semantic threshold, is

defined by the user. It is through this parameter that

the expert is able to determine the limit at which the

algorithm should stop during the course of the

taxonomy. The hierarchical depth goes in peer with

the level of specialization, which is an important

factor in the calculation of density. From there, the

authors can deduce that the deeper the hierarchical

depth of the COMET, the more the score of the DEM

module increases.

The density does not depend on the number of

concepts in the module. This hypothesis is verified in

the cases of ontologies 8, 11 and 12, where it is noted

that, although the size of the top-modules extracted

with OWL Module Extractor represent about 75% to

95% of the size of source ontologies, their score

remains less than that of the modules extracted with

COMET whose average size remains largely inferior

to 50% (Figure 3).

The semantic similarity (SSM) calculates the

proximity between the classes corresponding to the

terms searched in the ontology. Thus, concepts that

correspond to these terms must be linked either by

hierarchical relationships or by semantic

relationships. This measurement is based on the

calculation of the shortest path between pairs of

concepts. It can be seen that in the majority of cases,

the modules generated with COMET have a SSM

elevated score, which is in line with the COMET

approach where the detection of relevant concepts is

done through the path of object type properties. The

more semantic relationships there are in the module,

the more differing paths there will be between peer of

concepts in the module. Similarly, the more the

concepts of a module are interconnected by semantic

and hierarchical relationships, the higher the SSM

score of this module will be.

The centrality (CEM) measures the degree of

representation of all the concepts corresponding to the

terms searched for in an ontology. It is based on the

calculation of the shortest route through each concept

of ontology. The most solicited concepts during the

ontology process have a higher centrality than those

of other concepts. In view of the results shown in

figures 2, 3, 4 and 5 below, it is noticed that in 7 of

the 13 ontology cases studied, the modules generated

by COMET have a significantly higher CEM score

than those of modules generated by OWL Module

Extractor and SegmentationApp. The authors can

justify this result by the fact that these last two tools

have produced modules with few semantic

relationships, thus reducing the number of paths

between pairs of concepts. This observation leads to

the conclusion that the structure's course in the

calculation of the CEM of the OWL Module

Extractor and SegmentationApp modules is mainly

done through hierarchical relationships. It is

important to remember that the density, the centrality

and the semantic similarity are structural measures

which are founded essentially on the degree of

interconnection of concepts in the module.

In Scenario 1, the results of which are shown in

Figure 2, the authors consider both class matching

and density to be priorities. To do this, the authors

distribute the weights as follows: 0.4CMM, 0.4DEM,

0.2SSM, 0.0CEM. In this case, the authors estimate

that centrality has no impact on the global quality of

the module.

Figure 2: Module score as a function of the metrics of Alani

and al.: scenario 1.

In scenario 2, the weights are assigned equally to each

metric, resulting in the following distribution:

0.25CMM, 0.25DEM, 0.25SSM, 0.25CEM. The

authors estimate that no one metric is more important

than another. The results obtained are shown in

Figure 3.

KEOD 2021 - 13th International Conference on Knowledge Engineering and Ontology Development

Figure 3: Module score as a function of the metrics of Alani

and al.: scenario 2.

In Scenario 3, priority is given to the

correspondence of classes, followed by density, then

similarity and finally centrality. The weights are

assigned as follows: 0.4CMM, 0.3DEM, 0.2SSM,

0.1CEM. The results obtained are shown in Figure 4.

Figure 4: Module score as a function of the metrics of Alani

and al.: scenario 3.

In scenario 4, whose results are shown in Figure

5, semantic similarity is considered to be the most

important metric, while centrality remains the least

important, resulting in the following distribution:

0.2CMM, 0.3DEM, 0.4SSM, 0.1CEM.

After analyzing Figures 2, 3, 4 and 5, it is noted

that in the majority of ontologies studied, more

precisely in 8 of the 13 cases of ontologies used,

COMET produced the modules whose quality is the

most satisfactory according to Alani and al. measures.

The hierarchic depth and semantic threshold are

essential parameters that allow us to control the size

of the modules. The more an ontology is rich in object

type relationships, the more likely the COMET

algorithm will return larger modules.

Figure 5: Module score as a function of the metrics of Alani

and al.: scenario 4.

Hence the need for the user to define a semantic

threshold, in order to control the proportions of the

modules.

5 CONCLUSION

In this paper, the authors propose a new technique of

modularization of ontologies, which is a combination

of existing structural and semantic approaches. Based

on this technique, the authors have implemented a

prototype module extraction tool: COMET. Tests

were carried out on different modules generated using

extraction tools, including COMET, in order to

demonstrate that the authors could derive benefits

from both the structure and semantic of an ontology,

in order to produce modules of satisfactory quality.

COMET is a tool that, from an ontology and a list

of terms, generates a module. The latter represents a

segment that is made up only of concepts judged

relevant according to a segmentation algorithm. This

algorithm is based on two parameters which are the

hierarchical depth and the semantic threshold;

essential parameters allowing to regulate the

taxonomy path of source ontology and to control the

proportions of the module to extract.

Potentially relevant concepts are observed

through semantic relationships. As for the hierarchy,

it intervenes in two cases, either when searching in a

taxonomy for concepts having object type properties,

more specifically, concepts linked to others by a

relationship whose weight is superior or equal to the

threshold; or when it is to add the derivative of the

relevant concepts to the module. After the process of

segmentation of the ontology, a second algorithm

proceeds to its pruning in deleting all concepts

COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach

deemed irrelevant in order to generate the final

module.

The authors have also set up a validation protocol

to evaluate the quality of the different extracted

modules using a number of metrics. The validation

of the tools is based on a comparative study of the

modules generated compared to a reference ontology,

which this case is the source ontology.

On the strength of the results obtained during the

tests, it was observed that density is an essential

characteristic for it represents the level of

completeness of an ontology. A dense ontology is an

ontology rich in semantic relationships, that is, an

ontology whose classes are clearly defined. From the

series of tests, it can be concluded that the more dense

an ontology is, the more the module returned by

COMET is as well. However, the authors believe that,

given the current limitations of COMET and with a

view to future development, improvements could be

made both at the algorithm as well as at the tool

implementation levels. Thus, the following points can

be addressed:

 Choose a better semantic distance. It would be

interesting to look at another measure such as a

semantic distance based on WordNet, because

in addition to being a database containing the

lexical semantic content, WordNet equally

presents an ontology. This representation can

be used to evaluate the semantic distance

between two concepts not according to their

position in the ontology to modularize, but

rather according to their position in the

WordNet taxonomy.

 Propose an empirical approach which can set

the semantic threshold and the hierarchical

depth. The expert must carry out a certain

number of tests in order to find the ideal

threshold, hence the necessity to elaborate a

protocol by which these tests are to be

conducted.

 To be inspired by methods of graph exploration

based on heuristics or incremental deepening

during the course of the ontology and the

addition of the derivative of the relevant

concepts to the module. Indeed, it would be a

question of exploring the nodes of the graph

representing ontology according to the weights

associated with them. Depending on the depth

set by the user, the algorithm cannot

systematically add the concepts derivative

identified, but rather add concepts belonging to

this derivative based on their weights.

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COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach