=Paper=
{{Paper
|id=Vol-2919/paper13
|storemode=property
|title=Ensuring Semantic Interoperability Based on the Merging of Ontological Models
|pdfUrl=https://ceur-ws.org/Vol-2919/paper13.pdf
|volume=Vol-2919
|authors=Dmitry Korneev,Alexander Boichenko,Vasily Kazakov
}}
==Ensuring Semantic Interoperability Based on the Merging of Ontological Models==
https://ceur-ws.org/Vol-2919/paper13.pdf
Ensuring Semantic Interoperability Based on the
Merging of Ontological Models1
Dmitry Korneev 1[0000-0001-7260-4768], Alexander Boichenko 1[0000-0003-3113-9446] and
Vasily Kazakov 1[0000-0001-8939-2087]
1
Plekhanov Russian University of Economics, Moscow, Russia
Korneev.DG@rea.ru
Abstract. The article describes an ontologies merging algorithm used to ensure
the semantic interoperability of information systems (IS). The algorithm is
based on a set-theoretic approach for calculating measures of semantic proximi-
ty of vertices of homogeneous ontologies at the level of subject areas and the
level of tasks. The measure of semantic proximity is calculated taking into ac-
count the comparison of the attributes of the compared concepts of ontologies
and the values of these attributes, the location of the selected nodes within the
corresponding ontologies, and also taking into account the comparison of the
presence and types of links of the evaluated concepts.
Keywords: semantic interoperability, ontological engineering, an algorithm for
integrating ontologies
1 Introduction
Interoperability in ISO / IEC 24765-Systems and Software Engineering-Vocabulary
[1] refers to “the ability of two or more systems or elements to exchange information
and to use information obtained as a result of the exchange”.
Interoperability standards and studies address different levels of interoperability be-
tween systems. Most often in scientific research they refer to the European Interoper-
ability Framework v2.0 (EIF stack) [2], in which the following logical levels of inter-
action are distinguished:
1. Regulatory - involves the interaction of systems in a single regulatory and legisla-
tive environment;
2. Organizational - refers to the organizational aspects of the functioning of infor-
mation systems and presupposes the commonality of business processes and regula-
tions for their functioning;
3. Semantic - the ability of systems to understand the meaning of the information that
they exchange;
4. Syntactic - the ability to exchange data, the ability of systems to integrate;
5. Technical - the organization of the relationship between systems.
1
The article was prepared with the support of the Russian Foundation for Basic Research (grants No. 18-
07-01053 and No. 20-07-00926).
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
Proceedings of the of the XXIII International Conference "Enterprise Engineering and Knowledge Management"
(EEKM 2020), Moscow, Russia, December 8-9, 2020.
At the first two levels of the EIF stack, initial requirements for the design of infor-
mation systems are set, organizational measures are taken to unify the relevant regula-
tory documents and business processes.
To ensure the fourth and fifth levels of EIF stack in the design and development of
information systems, they must include certain software tools. The indicated levels of
interoperability are well enough studied and their practical implementation does not
cause serious difficulties at present.
Currently, the greatest scientific and practical significance is the solution of problems
of ensuring the semantic interoperability of information systems (IS). This is also due
to the fact that in recent years the intelligence of IS, including devices operating on
IoT technology, has sharply increased. Information systems are being created that are
capable of replacing a person in many respects, including in the field of making intel-
ligent decisions. Understanding the meaning of the request (and not just the syntax of
the request) that comes to the IS from another system will allow you to give a more
correct answer, which, in turn, should be as correctly understood as possible by the
system that generated the request. Ensuring semantic interoperability is associated
with the need to apply ontologies of concepts used in processes and describing the
processes of functioning of an information system.
Based on the studies carried out [3, 4, 5], the authors formalized the requirements for
the structure of the ontology to ensure semantic interoperability: the basic concepts
that allow describing both the static state and dynamic changes in the states of objects
in the subject area, sets of attributes (properties) of concepts and the main types of
links between concepts, sets of attributes (properties) links. In particular, it is pro-
posed to use the following types of concepts: "Object class", "Object" and "Entity".
Concepts can be linked together by the following types of unidirectional or bidirec-
tional relationships: "Inheritance", "Association", and "Action". In [4], the language
OWL-DL was chosen as the optimal means for describing ontologies, and the
ORACLE-11g DBMS was chosen as the storage medium for ontologies.
Based on the results of the studies carried out [4, 5], the following ontology construc-
tion algorithm was proposed to ensure the semantic interoperability of SIS:
1. Allocation of ontology concepts and definition of the semantics of links in accord-
ance with the rules [4].
2. Description of the ontology by means of the OWL DL language using the Protégé
5.0 ontology editor (creating an OWL file).
3. Creation of structures for storing ontologies in the ORACLE 11g DBMS.
4. Filling out the structures in accordance with the description of item 2 (loading the
OWL file into the ORACLE 11g DBMS).
5. Creation of additional user rules for obtaining implicit knowledge in the ORACLE
11g DBMS environment.
2 Ontology merging algorithm used to ensure the semantic
interoperability of information systems
To ensure the semantic interoperability of information systems, it is necessary to
compare the ontologies that underlie them and find out their commonality and differ-
ences. This problem is solved by using methods for assessing the semantic proximity
of ontology concepts. Many well-known methods for finding a measure of proximity
between ontology concepts are based on Tversky's set-theoretic approach, based on
comparing the properties of concepts [6]. In works [7-12] the mutual arrangement of
vertices within the ontology is analyzed. The lengths of paths between pairs of con-
cepts are calculated. The length of the shortest path is determined as the number of
concepts in the ontology located between the two nodes under consideration, which
are interconnected. It is believed that the shorter the path length between the vertices,
the semantically closer the pair of concepts of the considered ontology [7]. In [13],
the frequency of occurrence of a concept and its subclasses in one and another ontol-
ogy is taken as the basis for calculating the measure of semantic proximity of two
concepts of different ontologies. The methods described above for calculating prox-
imity measures between ontology nodes are symmetric. The work [14] describes a
calculation method, the essence of which is that the closeness of two concepts de-
pends on the closeness of concepts with which there are hierarchical relationships,
and is calculated recursively.
The most promising for use in algorithms used to calculate measures of semantic
proximity of ontology concepts are the so-called hybrid measures. The hybrid meas-
ure proposed in [15] consists of three parts - taxonomic, relational, and attributive.
Difficulties in comparing different ontologies of subject areas lie in the difference in
the names of concepts and relations, as well as in the approaches to the definition of
concepts. When mapping two ontologies, a search is performed for each concept of
one ontology of a similar concept of another ontology, taking into account the synon-
ymy of concepts. In works [16, 17], a method for calculating a measure is proposed,
taking into account the lexical proximity of concepts, properties, domains and ranges
of relations (ranges of values of the arguments of relations), parent/child concepts.
The main disadvantage of most methods for determining semantic proximity is the
need to involve an expert to confirm the correctness of detecting similarities and dif-
ferences in semantic concepts.
Below we will consider the problems of integrating ontologies that reflect either dif-
ferent points of view on the same subject area, or different points of view on the same
problem (i.e., we will integrate homogeneous ontologies at the level of subject areas
and levels of tasks). The purpose of the integration is to preserve the existing and
define new semantic dependencies of the concepts contained in both ontologies.
In accordance with the results of works [4, 5], the following formal definitions can be
given regarding ontologies used to ensure the semantic interoperability of IS.
1) A lot of concepts are defined as follows:
C = {C1, C2, C3}, (1)
where: C1– concept of the “Object class” type;
C2 – concept of the "Object" type;
C3 – concept of the "Entity" type.
2) The set of relationships between concepts is defined as follows:
= { 1, 2, 3}, (2)
where: 1– relationship "Inheritance" (relationship "class-subclass");
2 – relation "Association";
R3 – is the "Action" relation.
3) The ontology used to ensure the semantic interoperability of the IS can be formally
presented in the following form:
= {Ci (Аij, Sik), Rij, Pm}, (3)
where: Ci - ontology concepts;
Аij – j-th attribute of the i-th concept;
Sik – the k-th synonym for the i-th concept;
Rij – is the relationship between concepts i and j;
Pm – inference rules.
It is proposed to build an algorithm for integrating ontologies to ensure the semantic
interoperability of the IS based on the calculation of the semantic proximity of the
vertices of two ontologies 1 and 2. For each concept С1i of the ontology О1, we
calculate the measures of semantic proximity with the concepts С 2j of the ontology
2.
In the algorithm described below, the calculation of measures of semantic proximity
of ontology concepts used to ensure the semantic interoperability of IS will be based
on the set-theoretic approach [6]. The main idea of this approach is that to calculate
the measures of semantic similarity, it is necessary to take into account not only the
general properties of objects, but also their differences. The proposed algorithm will
calculate the measures of semantic proximity of homogeneous concepts, that is, con-
cepts that have the same names or names that are synonyms. The proximity measure
will consist of three parts:
• Attributive measure, which is calculated based on the comparison of the attributes of
the compared concepts and the values of these attributes;
• Geometric measure, which is calculated taking into account the location of the se-
lected vertices within the corresponding ontologies;
• Relational measure, which is calculated on the basis of comparing the presence and
types of relationships of the evaluated concepts with other concepts of the corre-
sponding ontologies.
Let us introduce the following characteristics of measures of semantic proximity:
• Equivalence. We will assume that the vertex C1i of the O1 ontology is equivalent to
the vertex C2j of the O2 ontology if: 1) the composition of attributes and their values
coincide or differ by intervals not exceeding the minimum threshold values
(attributive measure); 2) the selected concepts are located in ontologies in such a way
that the lengths of the minimum chains (bridges) between these concepts and two
other equivalent concepts in each ontology does not exceed the minimum allowable
threshold value (that is, in ontologies the selected concepts are "surrounded" by con-
cepts the evaluations carried out are equivalent (geometric measure); 3) the evaluated
concepts are associated with concepts with the same types of links, or the number of
different types of links does not exceed a certain minimum threshold value (relational
measure).
• Conformity. Determined according to the rules described above. In the case when
the minimum allowable threshold value of the corresponding measure of proximity is
exceeded, a comparison is made with the maximum allowable threshold value of the
corresponding measure of proximity of concepts. In this case, the maximum threshold
value must not be exceeded. Vertices possessing the above characteristic of the prox-
imity measure will be called corresponding.
• Difference. Determined according to the rules described above. In the case when the
maximum permissible threshold value of the corresponding proximity measure is
exceeded. Vertices possessing the above-described characteristic of the measure of
proximity will be called different.
To construct an ontology merging algorithm, it is also proposed to use the concept of
a bridge - a chain of ontology vertices that correspond to equivalent concepts used to
establish a mapping of two ontologies in [18].
To integrate ontologies used to ensure semantic interoperability, the following se-
quence of actions is proposed.
Step 1. In the ontologies O1 and O2, bridges are computed, consisting of vertices that
in pairs have equivalent or corresponding proximity measures. The lengths of the
bridges (the number of vertices) must coincide.
Step 2. Calculate the weight of each bridge. Assigning to the vertices with an equiva-
lent measure of proximity, the maximum coefficient is equal to 1, and to the vertices
with the corresponding measure of proximity - a coefficient in the range from 1 to 0.5,
depending on the approach to the threshold values of the estimated parameters of the
attributive, geometric, and relational measures of proximity.
Step 3. As the base for merging, we choose the ontology in which there is the largest
number of vertices. Let in our case it be O1 ontology. We select in it all the bridges
defined in the previous steps.
Step 4. For differing vertices in the O2 ontology, we find the bridges with the largest
weight and the smallest length to the vertices included in the bridges defined in Step
2.
Step 5. Add the bridges found in Step 4 to O1 ontology.
Steps 4 and 5 are repeated iteratively. We start looking for bridges with a length equal
to 1, sequentially increasing the length of the bridge by one vertex at each iteration.
Moreover, if the new vertex C2i is already included in the O1 ontology as a result of
performing Steps 3 and 4 (it became a new vertex C1j), then the vertices C2i and C1j
are considered equivalent.
In this case, the algorithm, due to the formalization of the ontology structure (see
formulas 1-3 above), makes it possible to avoid the semantic conflicts described in
[19], which arise during the merging of ontologies at the time of transferring vertices
connected by the types of links of the type ". In ontologies used to ensure semantic
interoperability and constructed in accordance with the rules described in [4, 5], the
vertices indicated in [4] will be linked by links of the "Association" type. This will
allow avoiding semantic conflicts when using the ontology merging algorithm de-
scribed above.
3 Summary
Currently, there are algorithms and their software implementations for the automatic
merging of ontologies. Information about such algorithms is given, for example, in
[20]. Each of the currently existing algorithms for automatic merging of ontologies
has, along with advantages, a number of significant disadvantages. The disadvantages
are primarily associated with attempts to create a universal algorithm for combining
ontologies that describe concepts of one subject area, but have different structures and
algorithms for their initial construction. The article presents an original algorithm for
the integration of ontologies representing structured knowledge about each of the
interacting ISs, taking into account the fact that their structures and construction algo-
rithms are clearly defined and unified.
4 Acknowledgements
The authors of the article are grateful to the Russian Foundation for Basic Research
for their support in writing the article (grants No. 18-07-01053 and No. 20-07-00926).
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