=Paper=
{{Paper
|id=Vol-2608/paper20
|storemode=property
|title=Designing complex intelligent systems on the basis of ontological models
|pdfUrl=https://ceur-ws.org/Vol-2608/paper20.pdf
|volume=Vol-2608
|authors=Kostyantyn Tkachenko,Olga Tkachenko,Oleksandr Tkachenko
|dblpUrl=https://dblp.org/rec/conf/cmis/TkachenkoTT20
}}
==Designing complex intelligent systems on the basis of ontological models==
https://ceur-ws.org/Vol-2608/paper20.pdf
Designing Complex Intelligent Systems on the Basis of
Ontological Models
Olha Tkachenko1[0000-0003-1800-618X], Oleksandr Tkachenko2[0000-0001-6911-2770],
Kostiantyn Tkachenko1[0000-0003-0549-3396]
1
State University of Infrastructure and Technologies, I. Ogienko str., 19, Kyiv,
02000, Ukraine
oitkachen@gmail.com
tkachenko.kostyantyn@gmail.com
2
National Aviation University, Liubomyra Huzara ave. 1, Kyiv, 03058, Ukraine
aatokg@gmail.com
Abstract: The article discusses the problems of automation of complex
systems’ design process, which include intelligent systems, using the example
of designing a complex technical object – motor vehicle. The concept of
designing the intelligent systems on the basis of modeling is considered. An
intellectual system model is a composition of following models: tasks, subject
area, user, presentation, dialogue script. The ontology is considered in the
article as a model of the architecture of an intelligent system.
Keywords: intelligent system, subject area, system models, ontology, ontology
model, knowledge base, architecture of intelligent system.
1 The Intelligent System and Ontologies
Information in the modern world has become one of the most important resources,
and intelligent systems (IS) have become a necessary tool in various fields of human
activity. By now, a huge amount of information has been accumulated, which is not
fully utilized. In addition, such information can be represented in many different
formats, which makes it difficult to use. All this led to the emergence of the problem
of heterogeneous information unification.
To solve these problems, a description of the data, its processing tools and
configurations that are the objects of modeling when creating a universal semantic
model for representing and processing knowledge is used [1, 2, 5-7, 8]. All
components of IS and the relationship between them can be represented in the form of
semantic model.
Currently, ways to solve the problems of using semantic modeling are being
studied to coordinate descriptions of interacting systems, methods and technical
implementation of such semantic models [3, 4, 9, 12-15]. However, problems
associated with practical implementation are still relevant, in particular: the lack of
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
agreed methods for the formation of ontological models, their integration and
transformation, the lack of methods for creating and coordinating ontological models
in the development (design) of IS.
The ontology is a detailed specification of the structure of a specific subject area
(SA). The main function of ontologies is to integrate information. Ontologies suggest
the implementation of the following aspects:
− they give a definition of the formal semantics of information, automating the
processing of this information;
− they determine semantic relations with real world objects, allowing to
connect the information that is presented in the form necessary for computer
processing with information that presented in a convenient form for human
perception, on the basis of general terminology [3].
In addition, in any area of human activity, ontologies allow:
− refine the structure of knowledge. In an ontological analysis, the concepts of
the subject domain and the relationships between them are determined so that the
result is a clear specification of the concepts and terms used in relation to the body of
knowledge that must be built [4].
− reduce conceptual and terminological ambiguity. Ontological analysis
provides the basis for the synthesis of the objects under consideration with different
needs (or viewing angles) depending on the specific context [9].
− provide knowledge sharing. Thanks to ontological analysis, many
conceptualizations of a certain subject area and a set of terms supporting it are
achieved. Having adequate syntax, these conceptualizations and the relationships
between them are expressed and encoded in ontology [8].
Intelligent systems belong to the class of complex systems. A characteristic
feature of modern IS are the transition to the architecture of heterogeneous, including
distributed objects, services and infrastructures. For example, autonomous external
intelligent systems can be considered as objects. In this situation, one of the most
important problems is the problem of creating a common IS architecture and
maintaining integrity at all stages of its life cycle.
By architecture we mean the organization of a system that takes into account the
composition of its components (elements, subsystems, etc.), their relationship with
each other and the external environment, the principles of creation, management and
development. When developing the architecture of IS, it is necessary to coordinate all
the requirements for the system with the methods for their implementation,
determined by the characteristics of the interacting subsystems. The complexity of
creating IS is determined, in particular, by such reasons:
− there is no universally accepted conceptual base for the description of
subsystems, the conceptual base for the description of the architecture of each
subsystem reflects its subject area;
− a big influence on the architecture of IS is provided by the system that
provides the process of IS developing; therefore, it is necessary to ensure the
interaction at the level of the target and supporting systems when coordination of
functional, technological and structural descriptions is required.
Consider the approach to creating a unified ontology of IS architecture, based on
knowledge management technology, its usage to harmonize the functional and
structural descriptions of the target and supporting systems. The knowledge base that
was formed on the basis of the proposed ontology allows one to accumulate the
experience of building architectures and system relationships of IS and use the
experience to develop new IS. The IS model is a composition of:
− model of tasks;
− model of domain;
− model of users;
− model of presentation;
− model of dialogue scenario.
Intelligent systems describe the behavior of managed objects in a complex
dynamic environment. IS design is carried out on the basis of a conceptual model of
the main properties of the studied subject area, the construction of the knowledge
base. This approach provides the opportunity for a formal definition of concepts and
laws of classification [8].
2 Conceptual Ontology Model
A conceptual model of knowledge about design models and methods is
implemented on the basis of an integrated ontology with a reasonable structure and
content. The system that describes the knowledge base when real-time information
systems are functioning contains elements that provide ontology modification based
on the extension of standard integrated ontology and metaontology procedures.
By a formal ontology model we mean a set consisting of three sets
O A, R, F , where A is a finite set of concepts of SA; R is the finite set of
relations between the concepts of SA; F is a finite set of interpretation functions
defined on concepts [12].
A formal model of an integrated ontology assumes the presence of:
− FORMALIZATION OF COMPLEX ONTOLOGY, including:
modernization of the classic model;
modernization based on the concept of the system.
− FORMALIZATION OF METAONTOLOGY, including:
model based on the concept of a system;
model based on formal theory.
− FORMALIZATION OF THEORETICAL MODELS OF KNOWLEDGE
ENGINEERING, including:
model “Field of knowledge”;
model “Pyramid of Knowledge”.
− FORMALIZATION OF THE AXIOM OF ONTOLOGY, including:
axioms of identification;
axioms of planning;
axioms of computing.
The ontology is based on the construction and analysis of a semantic model, the
objects of which are systematized according to the functional attribute of their
properties, which are the part of the class hierarchy. Relations determine the structure
of the system, and elements determine the function of nodes in this structure. The root
quality structure can be expanded to more specific classes by dividing properties at
each level in accordance with the division of the class.
The considered classification can be refined (renamed, expanded, supplemented
by arbitrary classes) depending on the problem of processing information and
changing knowledge about the designed dynamic object [4, 5-7, 9-12].
For example, the class INFORMATION STRUCTURE in the classification of
properties can be refined by highlighting the properties of the STRUCTURE BY
MANAGEMENT and the STRUCTURE BY DATA. This will entail highlighting in
the classification of components within the INFORMATION COMMUNICATION
class of the COMMAND TRANSFER and DATA TRANSFER subclasses, to which
the corresponding notation can be assigned.
Thus, the systematic approach [13-15] allows you to use your own set of modeling
tools: elements and relationships in solving each specific task of information
processing. At the same time, it is possible to diversify instances of classes of
information links and elements, preserving the corresponding components in the form
of abstract classes.
The systematic approach to the interpretation of design results and the knowledge
base which is based on a complex ontology allows us to represent the state tree of a
complex system, which is also an IS.
IS integrates the achievements of artificial intelligence, methods of classical
mathematics, fuzzy logic, the theory of neural systems and a genetic algorithm to
solve difficult formalized decision-making problems in a complex dynamic
environment.
Within the framework of such an interpretation, the principle is maintained that
the properties of elements and relationships are determined by the hierarchy of classes
that take these properties into account. This provides the possibility of system
decomposition of the task of creating IS in the interpretation of complex dynamic
situations. When formalizing the task of constructing a knowledge base, ontologies
improve the interpreted characteristics of IS and simplify its usage for analysis and
modeling of design decisions, especially in emergency and extreme situations.
To ensure the functionality of the IS, the law of system decomposition is used
[13-15]: elements on the i-tier of the system are in relation to support the functional
ability of the (i + 1)-tier of the system (the system should support the subsystem,
subsystems – the system, etc.). Compliance with this law is ensured by fulfilling the
following system decomposition rules:
− the correct attachment of elements to each other in accordance with the
qualitative and quantitative characteristics of the bonds (rule of attachment);
− ensuring a qualitative and quantitative balance of incoming and outgoing
functional relationships (rule of balance);
− closure of the supporting bonds (rule of closure).
The systematic approach formulated in this way can be applied at the stage of
substantiating the principles of construction and functioning of a system for modeling
and analysis of complex situations. The used ontology model describes a computer
interpretation (building models for formal systems) of the subject area related to the
formalization of the task of monitoring the functioning of IS and modeling emergency
and extreme situations that arise during operation.
3 Domain Ontology Model / Ontology Model of Subject Area
The development and formalization of a complex ontology during the creation of
IS consists in a formalized representation of the SA model, which assumes a
description of many objects and concepts, knowledge about them and the
relationships between them.
Definition 1. Subject area (SA) – a part of reality displayed using intelligent
technologies to obtain new information (knowledge). SA, when formalizing
knowledge in IS, is interpreted as part of the real world that has a certain semantic
localization – spatial, temporal, functional, etc. When considering the semantic space
of the studied software, it is necessary to first carry out its semantic localization.
Definition 2. Semantic localization in IS is associated with the determination of
the interface between SA in the semantic space of the subject areаs. At the stage of
formalizing knowledge, the components included in software can be considered as
sets of their semantic properties ( M SA1 ,..., M SA N ) [16]:
M SA1 {SS11 , SS12 ,..., SS1m },..., M SA N {SS N 1 , SS N 2 ,..., SS Nm } .
In this case, the intersection of the sets of semantic properties of various SA
M SA1... M SA N {0} ,
which allows us to write down the criteria for the localization of SA in the
semantic space M SA1... M SA N {0} .
A necessary criterion for the existence of SA is the distinguishability of its
properties in the presented semantic localization. For the set of SS properties of this
model of the studied SA, its unique identification follows s, s SS M SA .
At the same time, it is believed that the properties remain identical to themselves
for a time sufficient to build the SA model and use it in a formalized IS knowledge
system, which is especially promising when implementing aspect-oriented modeling
and programming technology.
A great importance in SA modeling at the development stage is functional
completeness and logical integrity. The SA model is necessary for solving a certain
class of design problems and their geometric and analytical interpretation. Therefore,
it should include only the necessary and sufficient properties for this. The functional
completeness of the SA model implies only fixing those properties of objects that are
necessary and sufficient to solve the design problems.
The criterion for the functional completeness of the SA model depends on the
class of tasks being solved and requires the determination of the criterion for the
depth of detail of the SA. A formalized formulation of the tasks to be solved in a
given SA allows you to highlight the features of the current situation, the modeling of
which is necessary and sufficient for a rational choice of design solutions.
4 Formalization of the Domain Ontology
The conceptual foundations of the formalization of different SA knowledge ontology
are considered in [1-3, 10, 11, 17-23]. Within the framework of the approach
proposed in [18], classes of terms, relations, and transformations corresponding to
physical and abstract entities for solving SA problems are distinguished. The SA
representation serves as a signature for creating a model of subject knowledge. The
ontology of SA is denoted Ont ( SA) S and defined in the following form [22]:
Ont ( SA) S T ( S ), R ( S ), Ax( S ) ,
where S (Subject) is a finite set of SA objects; T ( S ) (Terms) – a finite set of terms
(concepts) of software that have qualitative features that make up their distinctive
feature in the ontology S ; R ( S ) (Relations) – a finite set of relations between
classes of terms; Ax ( S ) (Axioms) is a finite set of axioms (interpretation functions)
defined on the classes and relations of the ontology Ont ( SA) S .
The ontology formation approach proposed in [18] uses the principles of object-
oriented analysis and consists in a phased refinement of constructions of the type
object - attributes and interactions between objects.
Strong name groups (for example, in texts they can be nouns) help to describe a
lot of objects that are combined into classes of terms, forming a factor set. To support
the IS developer, a dictionary of word classes close to each other is used.
Linguistic problems are eliminated by the fact that each object in a given situation
gets some direct name, which distinguishes it from various indirect ones – classifiers
and indirect names. Ontology can be considered as a language-dependent conceptual
model [22].
For the convenience of formalization of the information about the SA, the matrix
K ONT (Knowledge-Ontology) is used, in which the columns are the groups “object”
and “facts”, and the rows are the corresponding records with the names of objects and
the facts related to them.
General characteristics of groups and sets used in the matrix K ONT :
− group “Object” (Object), where i 1,2,..., n ; n is the number of identified
objects;
− attributes group, where j 1,2,..., m ; m is the number of attributes of the
corresponding object;
− behavior group that is added for each object, where l 1,2..., p ; p is the
number of behaviors of the corresponding object.
− group of interaction that defines the subject and object of the proposal.
5 Justification of the Approach to the Construction of IS Ontology
IS knowledge bases, developed on the basis of ontological models, represent the
design process in the form of an ordered structure with clearly defined relationships
between the elements (components, steps, tasks) of the design process.
Consider the ontology of SA – “Vehicle Design”, which contains the concepts of
this SA, the interpretation of knowledge and relationships within this area.
The ontological approach in this area is described in the form of the following
generalized algorithm of actions:
− compilation of a dictionary of SA (Thesaurus) based on the design
components of a complex technical system, which is a vehicle;
− obtaining ontology according to the Thesaurus of SA “Vehicle Design”
reflecting the “natural” connections between concepts;
− verification by experts in this field of the resulting ontology of the SA
“Vehicle Design”, support and filling of the ontology.
Thesaurus SA “Vehicle Design” can be used as a tool for standardization and
formalization of knowledge, as well as to provide access for users who solve the
problems of optimal design of a vehicle.
Thesaurus of the SA “Vehicle Design” is designed to solve the following
problems:
− classification and unification of the concepts of SA;
− classification of methods and tasks of designing vehicles;
− the construction of descriptions of the methods and tasks of vehicle (aircraft)
design in the knowledge base to support the optimal design of a vehicle;
− classification and search for background information on this topic.
Ontology for SA “Vehicle Design” uses the Thesaurus and is necessary for:
− developing a common understanding of the field of knowledge in question;
− presentation of knowledge in a form that is convenient for their processing
by appropriate IS;
− opportunities for obtaining and accumulating new knowledge;
− providing the possibility of reuse of knowledge.
Ontology describes the basic relationships and relationships between elements of
the design process.
IS design involves the usage of resources of the supporting system ( S SS ). The
description of the SA of IS, including the conceptual model and the task model, is the
input information, and the IS architecture is the result of the function S S SS ( S D ) ,
where S is the developed (designed) IS providing a description of its components
(elements, subsystems, etc.); S D – system for describing the SA; S SS – providing
the system for the development and operation of IS S .
S SS is a complex system in which each type of activity has its own separate,
independent support system (subsystem). When developing an IS, it is necessary to
solve the problems of identifying and coordinating problems solved within the
framework of IS S , developing appropriate data structures, their processing tools, a
user interface, and a model of transitions between individual elements (components)
of the system. Then
S S D , ST , S I , S C , S G , S N ,
where S T is a system for describing the processes and tasks of various SA and
their management; S I – information model management system that allows you to
describe information objects and data structures; S C – component development
system; S G – user interface development system; S N – a system for developing a
navigation model – a model of transitions between individual elements (components).
The development of IS (its architecture) involves modeling processes, including a
description of ontological models, taking into account the interests (concerns) of
interested parties (stakeholders), including models for describing the relationships
between the functions of the system and its components.
For each of the interests, separate groups of system descriptions (views) are
created. Each group of descriptions reveals a separate aspect of the system, and a set
of groups forms its complete description.
The agreements by which a group of descriptions is created, displayed and
analyzed, are established by the description method (viewpoint). Architecture ( AS )
of the system S : AS { ADSC } , where ADSC M i , Ai , Ri , Z i ; ADSC –
group of descriptions; Z i is a description method; i (i 1,..., n) is an aspect
characterizing the ADSC corresponding to Z i , connecting the set of elements M i
defined on the set of their attributes Ai through the relations Ri . Using the semantic
description method, with each S SS , we associate its description group and the
ontology of the SA:
− OS –ontology of the target system – the result of the development and
description of the technical implementation of IS;
− OD – ontology of SA;
− OT – ontology of tasks;
− ON – navigation ontology;
− OI – ontology of information elements;
− OC – ontology of components;
− OG – ontology of the user interface.
Each of the groups of descriptions is characterized by its own set of models, tasks,
tools, executors, structure, functioning, etc. For each group of descriptions, methods
for considering and describing systems and rules for their application are determined.
When reused for each description group, individual ontological models are defined
that are elements of the description group ontology.
S
The joint usage of ontologies implies the need for an ontological system O :
O S Ometa ,{OD&T }, M MO ,
where Ometa is metaontology (top-level ontology), that is, the ontology of the
architecture of the system S ; {OD &T } – many ontologies of terms (concepts) and
ontologies of SA problems; M MO is a model of an output machine associated with
S
the ontological system O .
Ometa describes classes that allow you to:
− represent the concepts of SA (terms, definitions) and their relationship;
− integrate knowledge of subsystems and use this knowledge in solving
problems.
The ontology of IS (its architecture) involves a set of necessary ontological
classes. Examples of definitions presented in the ontology of IS architecture, for
example, may be:
− model of ontology = {ontology of concepts, ontology of operations, ontology
of notations};
− system = {target system ( S ), providing system ( S SS )};
− description group = {domainview, taskview, infoview, navview, guiview,
compview}, where the following description groups are used:
domainview – conceptual model of software;
navview – the navigation structure of the application;
infoview – information elements and their data schemes;
guiview – user interface;
taskview – processes and tasks;
compview – components of IS.
The ontology consists of classes of SA and classes of classifiers. The set of
classifier objects defines a partition of the set of objects of the class of SA into
groups. Many classifiers, together with their relationships, define the class of the
relationship template, since the relationship structure of their objects defines the
relationship structure between objects of the SA classes. An inheritance system is
established between classifiers, and a system of relations between specializations
(specifications) of templates between templates. Example of relationship:
− the type of model corresponds to – the type of primitive modeling;
− the type of the model of the user interface subclass – the type of the primitive
modeling of the user interface;
− the type of the user interface model corresponds to – the type of the primitive
modeling of the user interface;
− the model consists of – a primitive simulation;
− user interface model – subclass model;
− user interface model consists of – a primitive user interface modeling.
The connections between S and SSS (including between their components), as well
as set M om ontological models are determined by templates defined by the
PROJECT class and its classifiers. Elements of the set M om when creating and
using the knowledge base can be selected into separate subsets of models (by
introducing the necessary classes and relationships) and reused in various projects.
The integration of description group ontologies is provided by the annotation
mechanism. An annotation is a pair u , e , where e is a concept from Ometa , or a
composition of concepts from Ometa , u is an instance of concept e . Thus, the
classes and ontologies of description groups are instances of concepts that are
subtypes of metaontology concepts Ometa .
We will consider:
− Ometa metaontology, describing a set of designs used in wide class of
information models;
− ontologies of description groups V and U ;
− sets of annotations AV and AU , respectively, in terms (concepts,
definitions) of the Ometa metaontology associated with the classes of description
group ontologies.
For the annotation operation, a special relationship is created between the
subgraph from V or U and the concept from Ometa . For this a special class
COMMUNICATION is introduced.
Ensuring the interoperability of models is supported by mapping the M U (V )
model V to model U . These models are not connected in any way, so it is necessary
to find semantically close constructions of models. We consider semantically close
pairs of constructions u , v , u U , v V for which there exist annotations
v, e Av and u, d AU such that e is a subring of d .
We define task m , in foj , guil , navlink i , f k as elements from description
groups: taskview, infoview, guiview, navview, compview, respectively. In the
ontology of IS (its architecture), for each of the ontology description groups, we
define classes of modeling primitives (in the general case, the class inheritance tree).
Determining the relationships between the classes of primitives and applying the
annotation mechanism will allow us to present models from different groups of
descriptions and determine the architecture of IS.
6 Design Features of Ontology-Driven IS
The creation of ontologies is carried out not only in the development of
environments aimed at sharing information between several users, but also in the
design of knowledge bases, the creation of expert systems and decision support
systems, and the development of various search engines.
When designing IS managed by ontologies, it is worth considering the features of
the life cycle of the ontologies themselves. The life cycle of ontologies is intertwined
with the life cycles of projects for the development of specific software products that
are connected to the ontology of the same applied sphere. Both life cycles are
supported by the respective roles: software engineer and ontology engineer.
M. Fernandez emphasized a certain analogy between the two processes and
investigated well-known models of the software development life cycle, presenting
them as potential paradigms for the development of ontologies. Among the
considered paradigms are [5-7]:
− cascading model;
− phased model;
− evolutionary model.
The development of ontology does not imply the implementation of a plan
sequence with intermediate results (only individual ontology components can be
developed in this way). Therefore, cascading models are inappropriate.
A phased model is more appropriate. Starting from the root (an element of the
ontology of the highest level), formalization of individual elements of the SA is
carried out. However, a strong connection with the previous stages of development
makes the final ontology redundant.
The most suitable for the development of ontology is the evolutionary model,
which begins with a prototype ontology containing some basic definitions. These
definitions can be presented in the form of a reference book (Thesaurus). Further, the
implementation of each SA product project initiates a new ontology evolution cycle.
Ontologies are a promising tool for transferring knowledge from project to
project; from one development cycle to the next project; from a project in one SA to a
project in another SA.
7 Final Results and Conclusions
Researches show that the complex ontology used in the design of IS can be based
on various formalizations.
The developed ontology allows us to formalize the description of the architecture
of IS and to integrate models created in the framework of different ontological
models.
The presented ontology can be used in the design of IS.
As a result of the ontology usage, the need for introducing special models of
ontologies that are of a general nature has been identified. One such ontology is the
ontology of the IS life cycle.
In the long term, ontologies can become an attractive paradigm of software
engineering.
The described methodology will provide an opportunity to use it when designing
compatible models and to reuse the IS elements, as well as to reduce the cost of
designing and developing of software products.
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