# Literature review on applications of ontologies and knowledge graphs in railway transport safety

Habib Hadj-Mabrouk

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Habib Hadj-Mabrouk. Literature review on applications of ontologies and knowledge graphs in railway transport safety. Railway Transport and Engineering - A Comprehensive Guide [Working Title], IntechOpen, pp.1-29, 2024, 10.5772/intechopen.1006278. hal-04763991

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# Literature Review on Applications of Ontologies and Knowledge Graphs in Railway Transport Safety

Habib Hadj-Mabrouk

## Abstract

The ontology allows the representation of knowledge in a formal, explicit and reusable way by considering the semantics and interoperability of the application domain. These characteristics give ontologies a useful role in knowledge engineering with a view to formalizing, structuring, representing, capitalizing and reusing domain knowledge with the power of explanation and interpretability of data and knowledge, thus making it possible to mitigate the problem of bias involved in AI systems (black boxes), notably deep learning by providing a clear explanation in the decision-making. This article provides a literature review of applications of ontologies and knowledge graphs in the field of rail transport safety. This study shows that despite the significant impact of ontologies and graphs for railway risk management and accident prevention, several applications face obstacles. A new conceptual model called "HEXA-Onto" is proposed which is structured around six iterative and complementary dimensions: (1) knowledge acquisition, (2) identification and specification of the domain, (3) formalization, structuring and representation of knowledge, (4) knowledge extraction techniques, (5) ontology development phases and (6) components, constraints, languages, tools and ontology editors.

**Keywords:** artificial intelligence, railway safety, ontology, knowledge graph, knowledge acquisition and representation

## 1. Introduction

In recent years, in the rail transport sector, researchers and experts in the field have become increasingly interested in the application of artificial intelligence (AI) techniques to solve certain decision support problems, such as diagnosis of rolling stock breakdowns, management of servicing and maintenance operations based on operating data, analysis of the behavior of driving operators, traffic planning and forecasting, management of vehicle compatibility train routes, mining relevant data from accident and incident investigation reports, etc. The main objective is to explore the raw text in order to extract relevant information for explanatory or decision-making purposes. In the field of railway safety and from accident investigation texts and
reports, these methods are generally used to better understand the factors contribut-
ing to these accidents, in particular the causes or consequences of accidents, recurring
accidents, or causal relationships. Several AI techniques are implemented in particular
machine learning (supervised, semi-supervised, unsupervised), deep learning such
as artificial neural networks (ANN), natural language processing (NLP) and case-
based reasoning (CBR). The lack of interoperability between AI tools often based on
different modes of knowledge representation, the difficulty of structuring knowledge
in a field, the insufficiency of these approaches to bring together and capitalize on
knowledge bases and the inability to share and reuse these knowledge bases have
motivated and directed research toward the development of new approaches based on
ontology and knowledge graphs. These new approaches, based essentially on a formal
representation of knowledge and considering the interoperability and reusability of
this knowledge, have experienced significant growth in recent years.

This study shows that ontology and knowledge graphs will certainly have a greater
impact on the field of rail transport safety and in particular on the management and
prevention of rail accident risks. Two major chapters are developed: a characterization
of the ontology followed by a bibliographic study on the applications of the ontology
and conceptual graphs in the field of rail transport safety. The first part offers a litera-
ture review on the contribution of these techniques to the analysis and evaluation of
railway safety. It successively presents: (1) safety analysis approaches based on ontol-
ogy, (2) safety analysis approaches based on knowledge graphs, and (3) a summary of
the applications studied. The second part allows you to understand, apprehend, and
characterize all the concepts involved in an ontology. To this end, a new conceptual
model called “HEXA-Onto” is proposed. It is structured around several iterative
and complementary dimensions including: (1) the stages of knowledge acquisition,
(2) the crucial phase of identification and specification of the area of expertise, (3)
methods of formalizing structuring and representation of knowledge, (4) knowledge
extraction techniques, (5) phases of development of the ontology (6) constraints
of development of the ontology which are inherent to the knowledge engineering
process, (7) ontology development languages and tools, (8) the main components of
an ontology and (9) Ontology Editors.

## 2. Definition of ontology

Gruber [1] proposed a specification of a common vocabulary to support the
sharing and reuse of knowledge in AI. In this context, Thomas R. Gruber defined
the notion of ontology as an “explicit specification of a conceptualization” or even
“a specification of a representational vocabulary for a shared domain of discourse”
whose main objective is the definition classes, relationships and functions. In the con-
text of Knowledge Engineering, the ontology is “a formal and explicit specification
of a shared conceptualization” [2]. To refine these definitions, Feilmayr and Wöß [3]
stated: “An ontology is a formal and explicit specification of a shared conceptualiza-
tion that is characterized by high semantic expressiveness required for increased
complexity.” It is a form of formal, explicit and shared reproduction of knowledge by
considering the semantics of the application domain, by identifying inconsistencies
and inadequacies in the data, by establishing a shared common vocabulary for better
information sharing and thus improving knowledge interoperability. These character-
istics give ontologies a useful role in knowledge engineering with a view to formaliz-
ing, structuring, representing, capitalizing and reusing domain knowledge with great
power of explain ability and interpretability of data and knowledge. In addition, a
clear and structured representation of domain knowledge makes it possible to under-
stand and mitigate the problem of bias involved in AI systems (black boxes) especially
deep learning in providing a clear explanation in the decision-making process.

3. Status of applications of ontology and knowledge graphs in transport
railway

3.1 Ontology-based approaches

Data integration in the railway domain based on ontology was proposed in Verstichel et al. [4]. The authors proposed a methodological framework based on the “Network Statement Checker” ontology which is based on an infrastructure database describing the railway network. This ontology integrates network declarations from different countries into a single information model to determine whether a given route can be used, from a compatibility perspective. The user of the tool can select a route on the European railway map and find information on the characteristics of each track section of a route. The results of this study were evaluated using a demonstrator developed as part of a European research project called Intelligent Integration of Railway Systems (InteGRail). Indeed, to resolve the problem of compatibility of European rail routes and in the context of Directive (EU) 2016/797 relating to the interoperability of the rail system of the European Union, the European project InteGRail was financed by the Commission European Union which brings together 39 stakeholders representing the main players in the railway industry. InteGRail aimed to strengthen the sharing of information between infrastructure managers (IM) and railway undertakings (RU) and is based in particular on the “Network Statement Checker” ontology presented above. Rail interoperability often refers to the ability of the rail system to operate trains safely and seamlessly on different rail networks located in different Member States. Under Commission Implementing Regulation (EU) 2019/777 of May 16, 2019 on common specifications for the railway infrastructure register, the European Infrastructure Register (RINF) makes it possible to define the characteristics of the railway network European by providing the main parameters to be used to check the technical compatibility between the vehicle and the route.

Based on the requirements linked to the European Infrastructure Register (RINF)
which are imposed by Directive (EU) 2016/797 of May 11, 2016 relating to the
interoperability of the railway system within the European Union, Tutcher et al. [5]
proposed a tool based on the ontology called “RaCoOn” making it possible to give,
for example, the (approximate) location of the train based on the known positions
of the track circuit. Infrastructure and location data are stored and used as the basis
for modeling train movements. Track circuits were added manually, using simulated
track circuit distances. Finally, a demonstrator for validating this principle was
presented, based on a new ontology of the railway domain created at the University
of Birmingham. In the same context and in the field of railway infrastructure, Bellini
et al. [6] presented the Railway Infrastructures and Signaling Ontology approach
(RAISO) which is based on a railway signaling ontology making it possible to formal-
ize the route of a network railway including the position of track elements (signals,
train detector, etc.). Bischof and Schenner [7] also developed a railway infrastructure
topology ontology to represent infrastructure characteristics in accordance with
domain standards. In the context of urban freight transport systems, Bouhana
et al. [8] proposed a personalized route search approach. The route retrieval mechanism uses both case-based reasoning (CBR) to provide the user with an optimal route and therefore an ontology to extract information urban traffic. In the field of railway control, command and signaling, Hoinaru et al. [9] proposed an ontology with a view to modeling the European rail traffic management system (ERTMS) and in particular the European Train Control System (ETCS). Sango et al. [10] also proposed a railway ontology to take into consideration the temporal aspect in the ERTMS/ETCS ontology. To consider user needs, Oliveira et al. [11] proposed a transport ontology allowing the personalization of the content of user interfaces. The concepts used are Calendar, City, Connection link, Connection point, Interchange hub, Journey, Operator, Rail element, Rail junction, Road element, Crossing and Stopping point. To support performance monitoring in public transport systems, Benvenuti et al. [12] developed an approach based on an ontological representation. Key performance indicators (KPIs) considered include, for example, actual dwell time, scheduled dwell time, advanced dwell time and average lead time/advance.

Debbech et al. [13] established an ontology of railway equipment malfunctions based on terminologies (extracted from standards) such as failure, causes, effects and safety measures. The idea of taking dysfunctions into account is very important but requires more modeling. This is a high-level ontology that requires the in-depth instantiation of knowledge from the railway safety domain. The work of Shynkarenko and Zhuchyi [14] aims at the ontological harmonization of information systems of rail freight transport in Ukraine and in particular the automated control system of freight traffic.

In the field of railway accidents, Zhao et al. [15] proposed an approach to construct ontologies of the railway domain and in particular to process accident data relating to the derailment of a train, as for Maalel et al. [16] their approach to helping model railway accident scenarios is based on two complementary ontologies: (1) a generic ontology which brings together high-level concepts involved in railway safety such as context, dangerous elements, dangerous events and the causes of the accident and (2) an ontology of the domain centered on the railway system, the human operator and the environment. The concepts involved in the domain ontology are specializations of other concepts in the generic ontology. This is a feasibility study focused on rail collisions with a view to better formalizing and structuring the knowledge involved in rail accident scenarios. Still in the context of modeling railway accident scenarios, Hadj-Mabrouk [17] developed an approach based jointly on ontology and machine learning, the objective of which is the prevention of potential accidents. The proposed example of ontology formalizes accident scenarios according to four dimensions: (1) Symptoms (general context of the scenario, potential accidents, level of risk), (2) Causes (system, human factors, environment, interactions), (3) potential dangers and (4) safety measures adopted (protective measures and preventive measures).

In the field of railway maintenance, several works based on ontology have been developed. Lewis et al. [18] used the ontology to integrate railway condition monitoring data; Umiliacchi et al. [19] developed an ontology-based modeling approach to carry out predictive maintenance operations on railway subsystems and Rahmig et al. [20] developed several conceptual models based on ontologies to improve railway maintenance. By exploring explicit and tacit knowledge from domain experts, Ragala et al. [21] also developed an ontology to manage railway maintenance. Finally, Louadah et al. [22] proposed an approach to support the management of rolling stock maintenance with a virtual depot based on an ontology. In the context of autonomous
driving, Chouchani and Debbech [23] proposed the ontology called Autonomous Train Map Ontology (ATMO) intended for autonomous trains.

Considering the importance of ontologies in the transportation sector, Katsumi and Fox [24] presented a survey of existing ontologies in the field of land transportation by examining several characteristics of the developed ontologies such as precision, generality, granularity and the competence. Several ontologies are examined in this study which focuses largely on road safety:

* Road accident ontology to capture accidents and their relevant information;

* Road traffic management ontology to provide decision support to drivers;

* Transport ontology for personalizing information for travelers (presented above);

* Ontology of transport disruptions;

* iCity ontology to capture data collected and generated on the urban system and

* Network Statement Checker ontology to check the feasibility of running a train on a given track (presented above).

According to Katsumi and Fox [24], data in land transportation comes from different organizations, sensors and surveys and is varied, complex and published in different formats, with different granularities and with different and sometimes ambiguous properties. There is no single ontology covering all concepts relevant to transportation research. No single ontology captures the entire high-level transportation taxonomy. Yang et al. [25] carried out an inventory of systems engineering based on ontologies, considering in particular product systems (concurrent engineering), service systems, business systems and the ISO standard /IEC/IEEE 15288. The authors provide important insights related to systems engineering by successively examining the knowledge areas supported, the contribution of existing ontologies and the techniques adopted. The emphasis is placed on the main advantages of an ontology in terms of interoperability, communication, explicitness, reusability of knowledge, homogeneous terminology to eliminate inconsistencies, unified vocabulary, controlled semantics, common understanding of a domain and reasoning on logical axioms [25].

### 3.1.1 Ontologies for the harmonization of risk management standards

The objective of the work of Pardo et al. [26] aims at the harmonization of standards, in particular the ISO 27001 and ISO 20000 standards. This study is essentially based on the implementation of an ontology providing a vocabulary, terms, concepts and specialized relationships in harmonizing multiple models. The objective is to eliminate inconsistencies, confusion and terminological conflicts at the different hierarchical levels and therefore propose precise definitions, a simple representation of processes and a uniform and formal vocabulary. This article describes an ontology that provides consistent terminology to support the harmonization of multiple models. In this same context of harmonization of standards, Ansaldi et al. [27] proposed an ontology to support the implementation of ISO standards relating to risk management with a view to facilitating the choice of risk management techniques
most appropriate to the needs of a specific risk management activity. The objective of this ontology is to support the implementation of ISO standards on hazard identification, risk analysis and assessment. For this purpose, several ISO standards were examined: ISO 31000 (Risk management—Principles and guidelines), ISO 31010 (Risk management—Risk assessment techniques) and ISO 73 (Risk management—Vocabulary). Using the “Protégé” tool, the authors have developed an ontology called “OntologyRATIS” which brings together the basic concepts extracted from the ISO 31010 document. In order to facilitate the choice of risk management techniques best suited to the needs, the ontology can be questioned through general questions, such as: “What methods are used for risk identification?” For a risk analysis? For the evaluation? Which methods provide quantitative results? Or qualitative?

According to Henderson-Sellers et al. [28], the semantics of terms used in international standards (ISO) can often be contradictory or at best misaligned, between pairs of standards with similar objectives. The authors showed how conceptual modeling and ontology engineering can be effective in reconciling the variation of disparate standards particularly engineering standards. The objective is to investigate the potential usefulness of ontologies for streamlining the suite of international software engineering standards. In this context of harmonization of ISO standards, the authors identified five areas in which ontologies could be useful: (1) the definitional area often called fundamental ontologies or higher-level ontologies, (2) the modeling domain and 3) the domain of practice. Building on the work of Henderson-Sellers et al. [28] and Gonzalez-Perez et al. [29], a project was initiated within the framework of ISO/IEC, the objective of which is to provide a shared ontology for SC7 standards. According to the authors, software engineering standards often use different underlying metamodels and ontologies, which sometimes differ from one standard to another. To this end, the harmonization of these standards through the use of a domain ontology was created from existing ISO/IEC standards, in particular ISO/IEC 24744 and 24,765 which is based in particular on the ontology of definitional elements (DEO) and the configured definitional ontology (CDO). The following **Table 1** presents a summary of railway safety analysis and evaluation approaches based on the ontologies previously studied.

## 3.2 Approaches based on knowledge graphs

### 3.2.1 Preliminary remark

The difference between ontology, knowledge graph and semantic network is still debated. The three approaches are based on a taxonomy (or taxonomy) which often refers to the science of classification. Taxonomy is an approach to classifying domain-specific concepts and terms. It is often used to organize terms in natural language processing (NLP). The ontology constitutes an enriched taxonomy and the concepts (or class) and relationships are also organized in the form of a conceptual graph allowing the different semantic relationships to be visualized. Web Ontology Language (OWL) is the semantic web standard dedicated to the description of ontologies. Coming from semantic networks, the conceptual graph is a knowledge representation formalism proposed in the work of Sowa in 1976 [30]. The knowledge graph is a multilateral relationship graph composed of nodes (entities) and edges (relationships between entities). Therefore, ontology, taxonomy and knowledge graph are all used to structure, organize and classify concepts in a hierarchical structure.
<table><thead><tr><td>Areas of expertise</td><td>Ontology type</td><td>Objective</td><td>References</td></tr></thead><tbody><tr><td>Artificial intelligence (AI)</td><td>Ontology (in general)</td><td>Common vocabulary to support knowledge sharing and reuse in AI</td><td>[1]</td></tr><tr><td>Knowledge engineering</td><td>Ontology for Knowledge Engineering</td><td>Ontology for Knowledge Engineering</td><td>[2]</td></tr><tr><td>Knowledge engineering</td><td>Ontology for Knowledge Engineering</td><td>Ontology for Knowledge Engineering</td><td>[3]</td></tr><tr><td>Rail transport</td><td>Ontology of railway accident scenarios</td><td>Modeling of accident scenarios resulting from return of experience</td><td>[16]</td></tr><tr><td>Rail transport</td><td>Ontology of railway accident scenarios</td><td>Accident prevention based on ontology and machine learning</td><td>[17]</td></tr><tr><td>Rail accidents</td><td>Ontologies for derailment accidents</td><td>Ontology based on railway data</td><td>[15]</td></tr><tr><td>Railway signaling</td><td>ERTMS/ETCS (European Rail Traffic Management System)</td><td>Ontology for modeling ERTMS/ETCS</td><td>[9]</td></tr><tr><td>Railway signaling</td><td>ERTMS/ETCS (European Rail Traffic Management System)</td><td>Temporal ontology for ERTMS/ETCS</td><td>[10]</td></tr><tr><td>Railway signaling</td><td>RAISO (RAilway Infrastructures and Signaling Ontology)</td><td>Railway Signaling Ontology</td><td>[6]</td></tr><tr><td>Rail infrastructure (Route Compatibility)</td><td>"Network Statement Checker" ontology</td><td>Data integration in the railway domain based on ontology</td><td>[4]</td></tr><tr><td>Rail infrastructure (Route Compatibility)</td><td>"RaCoOn" Ontology (Rail CoreOntology)</td><td>Train location based on track circuit positions</td><td>[5]</td></tr><tr><td>Rail infrastructure</td><td>Railway Infrastructure Topology Ontology</td><td>Represent infrastructure characteristics in accordance with domain standards</td><td>[7]</td></tr><tr><td>Rail transport</td><td>Transport ontology for customizing user interface content</td><td>Customizing user interface content</td><td>[11]</td></tr><tr><td>Freight transportation</td><td>Ontology for searching personalized routes for freight transport</td><td>CBR-based personalized route search</td><td>[8]</td></tr><tr><td>Public transport</td><td>Ontology based on performance indicators</td><td>Ontology to support performance monitoring in public transportation systems</td><td>[12]</td></tr><tr><td>Rail Transport Safety</td><td>Ontology based on terminology extracted from standards</td><td>Ontological for the analysis of dysfunctions in the design of railway systems</td><td>[13]</td></tr><tr><td>Rail freight transport</td><td>Ontology for automated freight traffic control system</td><td>Ontological harmonization of information systems of rail freight transport in Ukraine</td><td>[14]</td></tr><tr><td>Railway maintenance</td><td>Ontology for railway condition monitoring</td><td>Using Ontology to Integrate Railway Condition Monitoring Data</td><td>[18]</td></tr><tr><td>Railway maintenance</td><td>Ontology for railway predictive maintenance</td><td>Predictive maintenance of railway subsystems using an ontology-based modeling approach</td><td>[19]</td></tr></tbody></table>
<table><caption>**Table 1.**
Summary of approaches studied based on ontology.</caption><thead><tr><td>Areas of expertise</td><td>Ontology type</td><td>Objective</td><td>References</td></tr></thead><tbody><tr><td>Railway maintenance</td><td>Ontology for railway maintenance</td><td>Ontology based on the explicit and tacit knowledge of domain experts</td><td>[20]</td></tr><tr><td>Equipment maintenance rolling</td><td>Ontology for maintenance of rolling stock</td><td>Support the management of maintenance of rolling stock with an ontology-based virtual depot</td><td>[22]</td></tr><tr><td>Autonomous train</td><td>ATMO ontology</td><td>ATMO: autonomous train card ontology</td><td>[23]</td></tr><tr><td>Land transportation</td><td>Road traffic management ontology to help drivers; Ontology of road accidents; Ontology for the personalization of traveler information; The ontology of transport disruptions; iCity ontology to capture data collected and generated on the urban system; Network Statement Checker ontology to check the feasibility of running a train on a track railway.</td><td>Survey on ontologies in land transport</td><td>[24]</td></tr><tr><td>Concurrent Engineering</td><td>Ontologies of product systems (concurrent engineering), service systems, business systems, etc.</td><td>State of the art of ontology-based systems engineering</td><td>[25]</td></tr><tr><td>Standards ISO 27001, ISO 20000, etc.</td><td>Ontology for the harmonization of several standards</td><td>Main concepts related to the harmonization of several models and standards</td><td>[26]</td></tr><tr><td>ISO 31000 and 73 standards (Risk management)</td><td>Ontology for identifying the most appropriate risk management approach</td><td>_ “OntologyGuide73” is built from the document ISO 73; _ “OntologyRATIS,” created at from OntologyGuide73 and contains concepts extracted from ISO 31010</td><td>[27]</td></tr><tr><td>ISO Software Engineering Standards</td><td>Ontology for ISO standards</td><td>1) Creation of the infrastructure</td><td>[28]</td></tr><tr><td>ISO software engineering standards: ISO/IEC 24744 and 24,765</td><td>Ontology for ISO standards</td><td>2) Proof of concept and application</td><td>[29]</td></tr></tbody></table>

### 3.2.2 Approaches investigated

In recent years, several works focused on knowledge graphs have been developed to improve the safety of rail transport. Based on railway accident investigation reports (published on the UK RAIB website), Liu et al. [31] proposed a knowledge graph-based
approach to explore the causes of railway operational accidents. In the framework of
railway accident causality networks, nodes represent accidents and/or their causal
factors, for example, hazards, and the edges connecting the nodes represent the relation-
ships between the nodes. In the field of maintenance of railway rolling stock and based
on the fault tree method, Ragala et al. [32] proposed an approach to construct a knowl-
edge graph with a view to assist the maintenance technician; maintenance in its task of
researching the causes and remedies for failures (or breakdowns) of rolling stock. Based
on “FAR” accident data from American railways, Yan et al. [33] proposed a knowledge
graph based on an ontology with a view to identifying, preventing and controlling the
main points of risk as well as the risk paths of railway fire accidents. The study proposed
by Wang et al. [34] focuses on the development of a knowledge graph to model the
correlations between the dangers linked to rail accidents in the United Kingdom covering
a decade. In the context of the construction of railway bridges Lai et al. [35] developed a
method for the dynamic updating of a knowledge graph based on multisource data in the
bridge construction process. The authors propose a scientific basis necessary for bridge
management and thus help to improve the level of management of bridge computer-
ization. To prevent railway operating accidents Liu et al. [36] proposed an interesting
hazard prediction approach based on knowledge graphs. This approach to modeling
railway operating accidents called ROAKG made it possible to identify the main lessons:

* Missing or late deceleration applied by a train driver;

• Error of assessment of the current dangerous situation;

* Deterioration or damage to rolling stock including bogies and wheels;

* Failure of a signaling system or equipment.

Several other approaches to preventing railway dangers and accidents are presented
in Liu et al. [36] in particular the distribution of freight rail accidents in Canada from
1995 to 2015 according to fatigue, the distribution of accidents at railway crossings
level in the United States between 2009 and 2014, the distribution of the severity of rail
accidents in the United States from 2000 to 2016 according to the types of accidents, the
study of the frequency of fatal accidents according to different types of level crossings,
the evaluation of the frequency of freight train derailments, the study of the frequency
of 407 accidents/incidents which led to the most important cause, namely human error,
the study of the relationships between human errors and types of railway accidents. In
accordance with European regulations on the interoperability of European rail systems,
it is mandatory to check the compatibility of routes between the European rail network
managed by infrastructure managers and the vehicles for which the railway undertak-
ings are responsible. In 2020, in order to check the compatibility of routes, the European
Union Agency for Railways (Agency) developed a knowledge graph which is based in
particular on data from two European registers: the European Register of infrastructures
(RINF) and the European Register of Authorized Type Vehicles (ERATV). The knowl-
edge graph covers 27 European countries and describes more than 2000 vehicle types,
270,000 track segments and 50,000 stations [37].

According to Rojas et al. [38], the verification of route compatibility is based in
particular on: (i) an ontology of the railway infrastructure and the types of autho-
rized vehicles, (ii) a knowledge graph describing the railway infrastructure European;
(iii) a system architecture for use case development and (iv) an open source, native
RDF (Resource Description Framework) web application to support route compatibility checks. Aguado et al. [39] also proposed a knowledge graph for checking the compatibility of routes. The objective is to exploit the terminological harmonization achieved in railway legal documents by developing a reference vocabulary based on a reusable knowledge graph that can be used by railway stakeholders and thus improve the interoperability of railway data. The interest and importance of knowledge graphs as tools for explainable machine learning are studied through a detailed investigation in Tiddi and Schlobach [40]. The need to improve interpretability and explainability in machine learning systems and in particular deep learning, often considered as black boxes that are difficult to verify and interpret, has directed researchers toward the development of approaches, techniques and tools allowing explainable learning. According to the authors, knowledge graphs, which naturally provide basic domain knowledge in a machine-readable format, could be integrated into explainable machine learning approaches to provide meaningful explanations and thus improve the interpretability and reliability from the user's point of view. Indeed, semantic approaches and symbolic representations in the form of ontologies and knowledge graphs make it possible to structure, represent and capture knowledge and data in the domain. The following **Table 2** presents a summary of rail safety analysis and evaluation approaches based on knowledge graphs.

<table><caption>**Table 2.**
Summary of the approaches studied based on knowledge graphs.</caption><thead><tr><th>Railway transport subdomain</th><th>Objectives</th><th>References</th></tr></thead><tbody><tr><td>Infrastructure and Rolling stock</td><td>Route Compatibility</td><td>Knowledge graph for checking route compatibility [38]</td></tr><tr><td>Infrastructure and Rolling stock</td><td>Route Compatibility</td><td>Knowledge graph for checking route compatibility [39]</td></tr><tr><td>Infrastructure Register (RINF) and Vehicle Register (ERATV) held by the European Union Agency for Railways</td><td>Route Compatibility</td><td>Knowledge graph for checking route compatibility [37]</td></tr><tr><td>Railway operation</td><td>Accident prevention</td><td>Knowledge graph to prevent railway operating accidents [36]</td></tr><tr><td>Accident investigation reports United Kingdom</td><td>Accident prevention</td><td>Knowledge graphs to explore railway operational accidents based on UK rail accident investigation reports [31]</td></tr><tr><td>Maintenance of railway rolling stock</td><td>Accident prevention</td><td>Knowledge graphs to identify fault tree-based railway rolling stock failures [32]</td></tr><tr><td>Railway accident database Americans</td><td>Accident prevention</td><td>Knowledge graph to prevent railway fires [33]</td></tr><tr><td>Database of railway accidents United Kingdom</td><td>Accident prevention</td><td>UK rail accident knowledge graph [34]</td></tr><tr><td>Railway bridges</td><td>Prevention of railway bridge construction hazards</td><td>Knowledge graph for predicting railway bridge construction hazards [35]</td></tr><tr><td>All railway subdomains</td><td>Knowledge Graph Survey</td><td>Knowledge graphs as tools for explainable machine learning [40]</td></tr></tbody></table>
## 4. Toward a “HEXA-Onto” conceptual model characterizing an ontology

The design and implementation of a domain ontology requires the use of several means:

* techniques and tools for extracting data and knowledge;

* knowledge representation methods;

* a precise approach to developing an ontology;

* languages and tools to design and create the ontology.

In order to take all of these dimensions into account, we have developed a conceptual model called “HEXA-Onto” (in the form of a hexagon) to provide a general framework and methodology for ontology development. This approach in no way claims to be exhaustive or representative of all research work in the field of ontology. The objective is simply to summarize the ontology development cycle in a fairly basic way. Moreover, in 1997, Fernandez et al. [41] already proposed a methodological framework called “METONTOLOGY” which is structured around six main steps:

* Specification to identify the purpose of the ontology;

* Conceptualization to identify concepts, instances, relationships;

* Formalization to give a structure to the acquired knowledge;

* Integration to know the resources and/or reuse of existing ontologies;

* Implementation to codify the ontology model in a formal language;

* Maintenance.

In addition to these stages, the author [41] also defined three support activities: (1) knowledge acquisition, (2) documentation and (3) evaluation for verification and validation of the ontology. The “HEXA-Onto” model proposed in this study completes the methodological work of Fernandez et al. [41] and takes into consideration six iterative and complementary dimensions (Figure 1):

* The first dimension is made up of the knowledge extraction stages;

* The second dimension offers the methods, techniques and tools for acquiring the necessary knowledge during the first dimension of knowledge extraction;

* The third dimension brings together the stages of developing an ontology with emphasis on the stages of verification, validation and maintenance of the ontology;

* The fourth dimension recalls the methods, languages and tools necessary for the construction of ontology;
<figure><img src="image_4.png" /><figcaption>**Figure 1.** HEXA-Onto Model – A methodology for developing an ontological knowledge-based system.</figcaption></figure>

* The fifth dimension involves the socio-technical environment of the domain studied;

* The last dimension focuses on integrating the different constraints inherent to the engineering process of knowledge.

All six dimensions involved in the HEXA-Onto model are presented successively in the following paragraphs. To simplify the articulation of these six dimensions, we have agreed to bring together the last two in a single paragraph which describes both the environment and the constraints involved in the development process. Indeed, these two dimensions are closely linked because they must be considered jointly during the knowledge extraction stages and during the ontology construction stages.

## 4.1 Acquisition of knowledge

Knowledge acquisition (KT), recognized as a “bottleneck” since the advent of expert systems, is still considered a crucial task in the construction of ontologies. Extraction or “elicitation” refers to the collection of knowledge from the domain expert, while the notions of “transfer” or “transmission” of expertise refer to the collection and then formalization of an expert’s knowledge. The term “knowledge acquisition” refers to all of the steps necessary to develop a knowledge base for a system. CA is a multidisciplinary theme which benefits from the fruitful contributions of cognitive psychology (human reasoning models, knowledge collection techniques), software engineering (description of the life cycle of a knowledge-based system), linguistics (to make the use of documents more efficient or guide the interpretation of verbal data) and ergonomics (analysis of the activity of the expert and the future user). CA constitutes one of the central themes of research on ontology-based
systems and one of the keys not only to the successful development of such a system but also to its integration and use in an operational environment. Initially centered on the Expert/Cognitician couple, CA raised crucial problems such as the identification of user needs or the choice of a mode of knowledge representation. The excessive gap between the language used by experts to describe their problem and the level of abstraction of knowledge representation formalisms has motivated numerous researches aimed at facilitating the transfer of expertise. The design of a knowledge base requires the extraction, analysis, structuring and formalization of know-how in a field which is accessed through one or more individuals, qualified as experts. Therefore, the transfer of this expertise raises several questions: who really holds the expertise? How can we access it? How extract it? How to formalize it without distorting it? Which representation to choose? How to validate and maintain the knowledge collected?

Various researches are carried out to better understand these problems inherent in the acquisition of knowledge and the design of a knowledge-based system (KBS). Means (methods, techniques, tools) for the acquisition of knowledge are accessible to the cognitive scientist and the expert and offer a methodological framework for the development of an SBC.

## 4.2 Identification and specification of the area of expertise

This step of the knowledge acquisition methodology makes it possible to verify that the context of the problem is favorable for collecting knowledge. It makes it possible in particular to verify the existence and availability of one or more experts, the presence of expertise in line with the objectives of the study and the possibility of attending processing of real cases on the site for example. The domain identification step also makes it possible to understand the nature and origin of the information used by the expert as well as the specification of the form of solution to be developed by the decision support system envisaged. Several knowledge gathering techniques such as interviews or questionnaires can be used to identify the area of expertise. At the end of this stage, the knowledge engineer (cognitician) is familiar with the vocabulary of the field.

## 4.3 Knowledge extraction

### 4.3.1 Surface knowledge and deep knowledge

The interest in knowledge extraction allows, among other things, to extract knowledge that is often implicit among experts and to make it more explicit and usable in two forms: surface knowledge and deep knowledge. After having clearly defined the field of study, the surface knowledge extraction step consists of identifying the main lines of the expert's activity when faced with real problems. It aims to generally identify the mechanisms and modes of reasoning of the expert, the strategies and heuristics for solving the problem, the relationships between the key concepts of the domain, etc. The technique of verbalization and/or protocol analysis can be implemented during this stage in order to observe the expert at work. For the extraction of deep knowledge, this involves the detailed specification of the different knowledge modules as well as the refinement of the concepts used and their relationships. This step makes it possible, in fact, to precisely identify the expert's reasoning
mechanisms. To carry out this step, the use of several knowledge collection techniques is necessary. The protocol analysis technique can be applied to acquire the observable part of the expert's activity. On the other hand, the conceptual sorting technique can be used to identify the mental organization of the concepts manipulated by the expert. This extraction step ultimately leads to the identification of the main facets of the expert's reasoning. The results of this step include detailed descriptions of all knowledge (object, class, entity) as well as problem-solving heuristics.

### 4.3.2 Knowledge extraction techniques

There are currently various techniques, often inspired by work in cognitive psychology, to access and make as explicit as possible the knowledge manipulated by the expert in the field. These techniques are generally referred to as “cognitive” methods, “manual” methods or “empirical” methods. All of these techniques are often classified into two groups according to the mode of access to the knowledge that they allow:

* Direct techniques are generally based on informal collection methods and focus on extracting directly from the expert what he knows how to express verbally;

* Indirect techniques are semi-formal methods which attempt to reveal the expert's skills using indicators interpretable by the cognitive scientist. The data acquired by these techniques are essentially knowledge used implicitly that the expert does not know how to express explicitly.

Before applying a technique, it is fundamental to know what type of information it allows to collect, to know the limits of validity and the conditions of implementation. It is desirable that cognitive scientists have the means to indicate to them the techniques best suited to the fixed or evolving nature of the field of expertise, to the experts' mode of reasoning as well as to the characteristics of the available knowledge. In practice, a single technique is insufficient to extract all of the knowledge and it is necessary to combine several depending on the complexity of the domain. The role of the cognitive scientist is not only to choose the appropriate techniques but also to define their orders of application. There are several collection techniques such as interviews, questionnaires, protocol analysis and conceptual sorting. In order to compensate for the insufficiency and complexity of an empirical knowledge collection approach, extraction tools have been developed. They aim to automate certain stages of the acquisition process by developing even the knowledge base. These tools, such as AQUINAS, ROGET and MOLE, are based on the use of one or more knowledge collection techniques. They are generally dedicated to particular forms of knowledge or categories of problems such as diagnosis or design.

## 4.4 Formalization, structuring and representation of knowledge

The knowledge acquired previously is then analyzed and structured in modules (in the form of ontologies for example). Each of these modules is associated with a problem-solving task. This involves transcribing the knowledge resulting from the deep knowledge extraction phase into an appropriate representation module such as production rules, semantic networks, frames, knowledge graphs or ontologies, with a view to their implementations. The steps of the knowledge acquisition methodology,
presented above, follow one another in a cyclical manner in order to consider the
human variability of experts in the field.

# 5. Overview of the “HEXA-Onto” model through a conceptual ontology

In order to better explain all of the concepts developed below, this work presents the HEXA-Onto conceptual model in an ontology using the “Protege” tool. Several examples are shown in the following **Figures 2–15**.

## 5.1 Ontology development languages and tools

In order to represent the information involved in an ontology and to make it
jointly interoperable on a syntactic and semantic level, it is necessary to use specific

<figure><img src="image_5.png" /><figcaption>**Figure 2.**
HEXA-Onto model: the ontology development cycle.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 3.**
Knowledge acquisition techniques and approaches.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 4.**
Description language.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 5. The Semantic Web.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 6. OWL language.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 7.**
*OWL and its derivatives.*</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 8.**
*OWL Full as an extension of OWD DL.*</figcaption></figure>

languages. Some of them are called “high level,” others “low level,” however most of them are based on the technical characteristics of the XML, XML Schemas, RDF, RDF Schemas and OWL languages. Based on description logic, OWL is the most used and best-known language for describing ontologies. According to Sowa [30]: “Conceptual graphs form a knowledge representation language based on linguistics, psychology and philosophy.” A conceptual graph is made up of two types of nodes: “relationship” nodes and “concept” nodes. Concepts represent entities, attributes, states and events, and relationships show how concepts are interconnected. A semantic network is a directed and labeled graph. This is a multigraph. A taxonomy (or taxonomy) whose objective is the classification of elements is a very common structure in the context
<figure><img src="image_12.png" /><figcaption>**Figure 9.**
"V" cycle of ontology development.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 10.**
Ontology development phases.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 11.**
Classes, Instances, Relationship, etc.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Figure 12.**
*Example of constraints.*</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>**Figure 13.**
The components of an ontology.</figcaption></figure>

of ontologies. The concepts of an ontology are often organized into taxonomies. The name “description logic” can be interpreted in two ways: (1) this language allows you to write the “description” of the concepts of a domain and (2) this language offers formal semantics defined in first-order logic. This knowledge representation language is generally used to represent ontologies because it takes into consideration not only the readability and formalization of knowledge by using, for example, semantic networks but also the notion of expressiveness and decidability by using first-order logic. The semantic web, in particular, the representation of ontologies, automatic processing and representation of knowledge are examples from description logic. We generally distinguish between the “classic” Web and “semantic” Web. In the “classic” web, we find the Extensible Markup language (XML Language) which is often used as a
<figure><img src="image_17.png" /><figcaption>**Figure 14.**
The “Protege” Editor which uses OWL and RDF.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>**Figure 15.**
The “Pellet” reasoner.</figcaption></figure>

reference format for the exchange of data and documents (XML data and XML document). Usually, XML provides syntax for structured documents but does not require any semantic constraints. In the “classic” web, we also find the Hypertext Transfer Protocol Secure (HTTPS) protocol. This secure hypertextual transfer protocol allows the visitor to verify the identity of the website using an authentication certificate. Finally, Uniform Resource Identifiers (URIs) are also part of the “classic” Web, allowing a resource to be identified on a computer network. However, the pages and links offered by HTML are interpretable only by their users and do not offer semantics usable by machines. In the context of the “semantic” Web, several languages which consider the semantics of data have been proposed:
* Resource Description Framework (RDF);

* RDF Schema;

* Web Ontology Language (OWL);

* SPARQL Protocol and RDF Query Language (SPARQL)

SPARQL is a query language whose objective is to search, add, modify or delete RDF data available across the Internet. Coming from the field of description logic, the OWL knowledge representation language was built on the RDF data model and is often implemented to describe ontologies by defining terminologies to describe concrete domains. This terminology is based essentially on concepts (or classes) and properties (or attributes). In the context of description logic, “properties” are also called “roles.” There are three levels of OWL or three sublanguages: OWL-Lite, OWL-DL and OWL-Full. The OWL-Lite language is the simplest version of the OWL language (Figures 4–8).

## 5.2 Development of the ontology

The main characteristics of this approach are as follows:

* The life cycle is summarized by a “V” diagram including a descending branch for the specification and design of the ontology and an ascending branch for the integration and validation of the ontology;

* The chronology of the works is sequential, since each phase is conditioned by the completion of the previous phase. The transition from one phase to the next is conditioned by the review of one or more output documents;

* During these phases, the nature of the work to be carried out and the actors involved are clearly identified;

* These different phases correspond to increasing levels of definition. This is an approach whose phases represent different levels of abstraction (Figures 9 and 10).

### 5.2.1 Specification

This step aims to develop the specifications of the ontology in terms of functional needs and interfaces with the environment. It makes it possible to specify the description of the expected functions and the constraints to be respected by the ontology. A specification must be complete, precise, consistent, clear and controllable. It is recommended to use several complementary modes of expression. Formalized expressions contribute to the precision and coherence of knowledge. It is recommended to use the Structured Analysis and Design Technique (SADT) formalism which is a graphical tool associated with a modular and hierarchical top-down analysis method. A specification constitutes a general design phase (high level of abstraction) during which the choices for dividing the ontological system into subsystems, constituents, modules or functions. The specification is a statement of objectives within the framework of a task specified in the specifications. It relates in particular to a function to
be provided, a concrete object to be produced or an abstract object to be defined. In terms of ontology, the specification stage allows in particular: the identification of terms (vocabulary) and key concepts of the domain, the identification of the “why” of the ontology, the definition of classes and the hierarchy of classes, defining properties of classes and attributes and identifying axioms, taxonomies, instances, etc. This problem specification phase also makes it possible to clarify the objectives to be achieved by the considered ontology, for example, the reuse of domain knowledge, the modeling of domain knowledge for common understanding, analyze and explain knowledge in a field, etc.

### 5.2.2 Preliminary conception

The preliminary design stage is a process during which the choices for realizing the functions of the ontology are defined and implemented. Unlike the specification stage, the objective of which is to describe the “what” of the problem to be addressed, the design specifies the “how” to address the problem. The design must explain and justify the implementation choices (coding) as well as the types of control to be carried out and the results to be obtained. The preliminary design must answer the question: how to satisfy the needs formalized during the specification phase? This step leads to the development of the general architecture of the ontology, the choice, definition and justification of this architecture. This step makes it possible in particular to limit the exchange of data between modules, to decompose the functions of the problem into tasks then ontological modules and to carry out a hierarchical decomposition of the modules if necessary.

### 5.2.3 Detailed design

The detailed design must satisfy the needs formalized during the preliminary design phase and allows the results of the preliminary design to be detailed to a sufficient level allowing the coding and creation of the ontology. This is a definition by successive refinements of the data.

### 5.2.4 Creation of the ontology (coding)

This step allows the results of the previous phase to be translated into a program using a given programming language. It makes it possible to produce clear and commented source code meeting the requirements of the preliminary and detailed design phases. Building an ontology requires consideration of at least the following fundamental components: vocabulary, class, properties, instance, relationship, class hierarchy, etc.

* Identify all the key terms in the field of application in order to develop a common vocabulary (lexicon);

* Identify the list of classes (or concepts). Each class groups together a collection of instances (or objects) possessing similar properties;

* Identify class properties. Each class has properties or attributes or functions. These are the characteristics or functionalities that classes or concepts or objects can possess and share;
* Define the facets and particularities of these concepts, in particular the cardinality of the attributes and the value types of the attributes (enumerated attributes, Boolean attributes, multivalued attributes, symbolic attributes, Instance type attributes, etc.);

* Identify instances of classes (or individuals) which are basic objects and are therefore indivisible in an ontology;

* Identify potential relationships to determine connections (or links) between classes (or concepts) or instances. This involves discovering the correspondence relationships between all the entities of the ontology: between classes of objects (concepts), between instances, between class and instances, etc.

* Develop a hierarchy of classes in the form of conceptual graphs;

* Decide on the levels of abstraction of the ontologies developed: high-level ontology, low-level ontology, domain ontology, etc.

* Integrate, merge, unify, align, match and manage ontologies taking into consideration interoperability (ontology reconciliation) and on the basis of the vocabulary identified previously;

* Code the ontology in order to explain the conceptual representation of the ontology in a formal language like OWL and RDF (Figure 11).

### 5.2.5 Ontology unit testing

Ontology unit testing is an activity whose objective is to verify, for each component (or element) of the ontology taken in isolation, that all the paths (branches), nodes, concepts, links (relationships), the input and output data are explored and traversed (at least once) and that the investigation results conform to the preliminary design file.

### 5.2.6 Ontology integration testing

This step makes it possible to check the correct functioning of the software after grouping the different modules of the ontology. The goal is to demonstrate that all modules (designed during the preliminary design stage) interact correctly to ensure their functions intended by the specification phase. We can use a Test Plan whose aim is to specify the objectives to be tested, the characteristics to be verified, the logic of the test sequence as well as the methods and tools to be implemented to carry out the unit testing phases, integration and validation.

### 5.2.7 Functional validation of the ontology

During this step, it is necessary to ensure that the developed ontology complies with the specification file. Validation is a control process aimed at verifying that all the required functions of an ontology are actually obtained. Generally, during this validation phase, the checks are carried out by a team separate from the one which designed the ontology. The progress of this activity requires passing through three types of successive controls: unit control, integration control and functional control. Usually, we distinguish between “external” validation in relation to the objectives
specified during the specification phase and “internal” validation, also known as
verification. Verification is an activity which aims to ensure that the results from a
phase of the development cycle (descending branch of the V-shaped cycle) comply
with the benchmark of the previous phase.

5.2.8 Ontology maintenance

Maintenance refers to the modification made to the ontology after its implementa-
tion, to correct faults and improve its effectiveness. The objective is to ensure that the
ontology is maintained following the correction, improvement or adaptation opera-
tions carried out.

5.2.9 Ontology development constraints

The constraints proposed below must be considered either during the develop-
ment of an ontology or during the knowledge acquisition phase. These constraints
can also be considered jointly during two processes of knowledge acquisition and
ontology construction. Some authors specify that the construction of ontologies
must respect principles which have been defined by Gruber [1], Uschold et al. [42]
and Guarino [43]. Generally, these principles concern the necessary clarity of the
definition of the elements that the ontology must contain as well as the separation of
the design and implementation phases in a formal language of the ontology. These
principles can be summarized as follows:

* Clarity/objectivity, completeness/perfection, consistency, maximum monotonous extensibility, commitments minimal ontological [1];

• Ontological distinction: the classes of an ontology must be separated [44];

* Minimal modularity in order to minimize coupling between modules [45];

* Subdivision of taxonomies, minimum semantic distance, standardization of terms [46].

The HEXA-Onto development model (dimension 6) takes into consideration the
following constraints:

* Completeness: this involves describing explicitly and exhaustively all data, functions, concepts, relationships, classes of objects, etc. necessary to meet a well-defined objective during the specification stage;

• Coherence: there must be no contradictions;

* Clarity: the form of an object, a concept, a problem, a relationship, a piece of knowledge, a reasoning, a result, a document, etc. must facilitate understanding;

• Precision: each significant concept, parameter and attribute must be indicated;

* Non-ambiguity: it must not be possible to interpret a concept in different ways;
* Readability: it is the suitability of a specification, a model, a language, a program, etc. to be easily understandable. For example, languages, knowledge and declarative representation modes (used in AI) offer good readability. This is declarative knowledge as opposed to procedural knowledge used in computer science;

* Extensibility: the objective is to facilitate corrections and updating and scalability of knowledge;

* Reliability: ability of an ontology to perform without failures all the functions specified in a reference document (from the requirements specification phase) in a specific environment and for a given duration of use;

* Modularity: this characteristic of the software makes it possible to guarantee a modular structure in program units that are highly independent of each other, autonomous and identifiable for compilation, testing and association with other units (modules) (Figure 12).

## 5.3 Ontology and knowledge-based systems (KBS)

As with knowledge-based systems (KBS) or expert systems, within the framework of ontologies, it is also about reasoning and deducing knowledge from new facts. In SBC, domain knowledge, described in a knowledge base generally in the form of production rules, is well separated from the inference engine (reasoner) whose objective is to exploit this knowledge and deduce new facts or conclusions. This idea of separation between “declarative” knowledge and the inference engine for the “procedural” aspect made it possible to quickly develop SBCs by reusing certain generic elements relating to the reasoning or domain knowledge. The objective remains the same when constructing an ontology. Domain knowledge is developed in the form of a reusable ontology and the inference engine makes it possible to reason about this ontology. Compared to SBC, only the mode of representation of knowledge changes from a representation in the form of production rules for example to a much richer representation in the form of ontology.

## 5.4 The components of an ontology

The development of an ontology requires knowledge of four fundamental components: classes, properties, instances and relationships. A class (or concept) groups together a set or collection of instances (or objects) having similar properties. In the context of the semantic web, a meta-class designates a set of classes that share certain common characteristics. Each class has properties (in RDF) or attributes (slot in XOL) or functions (in OML). Thus, we speak of attributes or properties to characterize a class (concept) or describe an instance. These are the properties or characteristics or functionalities or characteristic parameters that classes or concepts or objects can possess and share. Instances (or individuals) of a class are basic objects that are indivisible in an ontology. Finally, relationships are used to determine connections (or links) between classes (or concepts) or instances (Figure 13). The objective is to identify the semantic association between all the classes (concepts) identified in an ontology. These relations between classes are generally formulated by specific terms such as “is a,” “is a kind of,” “part of,” “repealed by” and “imposed by.”
## 5.5 Ontology editors

For the implementation of an ontology, there are different editors allowing you to systematize and rationalize the engineering of ontologies such as Protégé, Ontolingua, Ontosaurus, WebODE and WebOnto. Developed by a team from Stanford University (https://protege.stanford.edu/), “Protégé” integrates Semantic Web standards and in particular the OWL language. It is a free, open source and most widely used ontology editor/framework. In addition to its graphical interfaces such as “OntoGraf” to visualize the constructed ontologies (classes and class hierarchy), Protégé implements reasoners (inference engines). There are several reasoners making it possible to reason about ontologies described in description logic. With the Protégé tool, it is possible not only to create and edit an ontology in OWL format but also to use an inference engine like Pellet (Figures 14 and 15).

## 5.6 Types of ontologies

The literature distinguishes several types of ontology, in alphabetical order, we find Meta-ontologies, Conceptual ontology, Lexical ontology, Application ontology, Domain ontology, High-level ontology, Method ontology, Representation ontology, Task ontology, Task-domain ontology, Task/method ontology, Generic ontology, Light ontology and Heavy ontology. The domain ontology is usually the most used in practice to formalize and structure the knowledge and data of a given domain.

# 6. Conclusion

An ontology (or knowledge graph) is a formal, explicit, interpretable, reusable and shareable description by a group of individuals. It has the capacity to structure, formalize and represent explicit knowledge from several data sources (domain expert, database, archive, report, etc.). Ontology does not seek to model the ideas and methods of solving a given problem, but rather to specify and characterize the objects, concepts, entities, properties of concepts, attributes, facets and instances as well as the semantic links that exist between them in order to construct an explicit specification while respecting the meaning, syntax and semantics of the objects. Research in this area distinguishes several types of ontology: core ontology, domain ontology, task ontology, application ontology, high-level ontology, etc. In an ontology, objects, concepts, classes or terms are structured in the form of a knowledge graph in a hierarchical and descending manner and always respecting the semantic links between these objects. The leaves of this hierarchical tree are used to represent the instances of a new object in the domain. The following **Table 3** presents a summary of the applications of ontologies and knowledge graphs in the field of railway safety. These applications can be classified into six subareas related to railway safety. In order of involvement:

* Prevention and/or modeling of railway accident and incident scenarios generally based on investigation reports;

* Compatibility and route assignment generally from the Infrastructure Register (RINF) and Vehicle Register (ERATV) held by the European Union Agency for Railways pursuant to Directive (EU) 2016/797 of the European Parliament and
<table><caption>**Table 3.**
Summary of applications of ontologies and knowledge graphs in the field of railway safety.</caption><thead><tr><td>Rail safety field</td><td>References</td></tr></thead><tbody><tr><td>Prevention and/or modeling of railway accident and incident scenarios</td><td>[16]<br>[17]<br>[31]<br>[36]<br>[32]<br>[33, 34]<br>[35]<br>[15]</td></tr><tr><td>Route compatibility</td><td>[4]<br>[5]<br>[38]<br>[39]<br>[37]</td></tr><tr><td>Railway maintenance</td><td>[18]<br>[19]<br>[20]<br>[22]</td></tr><tr><td>Harmonization of standards</td><td>[22]</td></tr><tr><td>Standards ISO 27001, ISO 20000, ISO 31000 and 73 standards (Risk management)</td><td>[26]</td></tr><tr><td>ISO/IEC 24744 and 24,765 for software engineering</td><td>[27]<br>[28]<br>[29]</td></tr><tr><td>Railway signaling (European Rail Traffic Management System: ERTMS/ETCS)</td><td>[9]<br>[10]<br>[6]</td></tr><tr><td>Others</td><td></td></tr><tr><td>Rail infrastructure topology</td><td>[7]</td></tr><tr><td>Search for personalized routes</td><td>[8]</td></tr><tr><td>Customizing user information</td><td>[11]</td></tr><tr><td>Control of goods traffic</td><td>[14]</td></tr><tr><td>Performance indicators</td><td>[12]</td></tr><tr><td>Analysis of dysfunctions in the design of railway systems</td><td>[13]</td></tr></tbody></table>

of the Council of May 11, 2016 relating to the interoperability of the rail system within the European Union;

* Railway maintenance and surveillance;

* Harmonization of standards, particularly risk management and software engineering: ISO 27001, ISO 20000, ISO 31000 and 73, ISO/IEC 24744 and 24,765;

* Railway signaling within the framework of the European rail traffic management system: ERTMS/ETCS;

* The other subdomains successively concern the topology of the railway infrastructure, the search for personalized routes, the personalization of user information and the control of freight traffic.
Useful both to researchers involved in the field of knowledge engineering and particularly ontology engineering as well as to researchers and experts in railway safety, this work can serve as a resource and contribute to the present and future development of applications of ontology and knowledge graphs to the field of railway safety. However, certain remarks seem necessary. Despite the certain interest of the works analyzed, each of the ontologies developed concerns a particular application and does not cover all of the concepts involved in railway safety. Rare is the work that focuses on the design, construction and consideration of safety from the specification and design phases of the rail system. The work examined focuses mainly on the operation, maintenance and feedback phases. Data, information and knowledge on safety come largely from accident and incident investigation documents and reports, ISO standards, infrastructure and rolling stock registers, specifications linked to the European traffic management system. Railway: ERTMS/ETCS and user information in terms of needs. Consequently, no ontology can capture, on its own, all the data and knowledge involved in railway safety. The last and most important limitation concerns the terminology and concepts used to develop an ontology related to railway safety. Certain works, such as those linked to ISO standards, ERTMS/ETCS and European registers, take into consideration the imposed terminology. In other works, the vocabulary used suffers from a lack of precision, clarity, in particular, the visible confusion between the term's danger, risk, accident, incident, potential accident, dangerous event, dangerous situation, dangerous element, analysis of risks, hazard analysis, risk assessment, risk management, risk reduction, safety, security, etc.

In this context, the vocabulary used when developing ontologies and knowledge graphs to define classes and properties cannot in any way guarantee the semantics, interoperability and reusability of the ontologies. Consequently, the problem of the validity of certain approaches arises since these are approaches intended for risk management for critical systems such as rail transport. Therefore, it is worth noting the absence (for some applications) of common, identifiable and reusable concepts as initially suggested by the process of developing an ontology. This constitutes an obstacle for future work which sometimes aims to reuse and exploit certain existing ontologies. Thus, the implementation of a common conceptual model, based on an ontology, which considers the semantics of all the terms and concepts used in railway safety and risk management is essential and will be the subject of the next document.

Nevertheless, and initially, a “HEXA-Onto” conceptual model was presented to clarify the concepts involved in an ontology in particular the acquisition of knowledge, the identification of the domain, the representation of knowledge and the development of ontology, constraints, languages and ontology editors.

**7. Perspectives on the contribution of ontologies to railway safety and interoperability**

In recent years, the field of European rail transport has now been governed by a set of legislative and regulatory acts and in particular the following two directives: (1) Directive (EU) 2016/797 of the European Parliament and of the Council of May 11, 2016 relating to the interoperability of the railway system within the European Union and (2) Directive (EU) 2016/798 of the European Parliament and of the Council of May 11, 2016 relating to railway safety.
Directive (EU) 2016/797 on interoperability clarified all railway subsystems by distinguishing between “structural elements” such as infrastructure, energy, rolling stock (locomotives and wagons) and “functional elements” such as control-command and signaling subsystems, traffic operation and management, telematics applications (passengers and freight), maintenance and accessibility for disabled people and people with reduced mobility. For each subsystem, a list of “interoperability constituents” has been established such as the catenary, the pantograph, the rails, the ballast, the axles, the wheels, etc. This directive also imposed, for each railway subsystem, a set of “essential requirements”: “safety” of components (for example brakes), “availability” (for example, monitoring and maintenance of components critical or essential for safety), “health” and environmental protection, “reliability” and technical “compatibility.”

Technical specifications for interoperability (TSI) for each subsystem or part of a subsystem have also been developed. A TSI specifies all the conditions that an “interoperability constituent” must respect as well as the procedure to follow for the assessment of conformity or suitability for use.

Authorization to put the infrastructure into service or to place rolling stock (vehicles and vehicle types) on the market is granted either by the national safety authority or by the European Union Railway Agency iron in view of a “technical file” including all the attestations, declarations and certificates established by accredited bodies such as the notified body and the risk assessment body. In this context, the European Commission has adopted a series of regulations which supplement Directive (EU) 2016/797 with regard to all of these technical aspects mentioned above. The final objective is to guarantee railway interoperability which is often defined by “the ability of a railway system to allow the safe and seamless circulation of trains which achieve the required levels of performance.”

To ensure the “safe movement” of trains, Safety Directive (EU) 2016/798 established several measures intended to develop and improve rail safety within the EU and in particular the definition of safety objectives common safety indicators, common safety methods, a safety management system, a safety certificate for railway undertakings, a safety approval for infrastructure managers, a system vehicle maintenance carried out by the entities in charge of maintenance, etc. This directive also required investigations into railway accidents and incidents to be carried out by independent technical investigation bodies as well as the control and surveillance of railway safety by national safety authorities.

Remember that a summary of the applications of the ontologies and knowledge graphs studied was presented in **Table 3**. This assessment shows that ontologies and knowledge graphs can contribute in particular to (1) the analysis of railway accidents based on railway accident reports and surveys, (2) establishment and control of route compatibility, (3) railway maintenance, (4) harmonization of ISO standards and (5) railway signaling (ERTMS/ETCS).

The applications of ontologies concerning the “analysis and prevention of railway accidents” fall within the framework of Directive (EU) 2016/798 relating to safety and more precisely in the context of technical investigations. However, we can question the consistency and completeness of the data related to accidents from railway feedback; on one hand, how these analyses based on ontologies can really help technical investigators and national safety authorities and on the other, how to establish real collaboration between AI specialists and railway stakeholders. The first decisive step consists, initially, of developing a common and standardized vocabulary between AI researchers and investigation bodies which considers at least the following terms: dangerous elements, causes, dangers, potential accidents, accidents,
incidents, precursors to accidents, frequency (or probability) of accidents, severity of the damage caused, risk or rather level of risk (probability/severity). This proposal is currently being developed by the author.

Applications related to “route compatibility” between infrastructure and types of
railway vehicles fit well within the framework of Directive (EU) 2016/797 on railway
interoperability.

Applications of the ontology to “railway maintenance” concern the functional subsystem in accordance with Directive (EU) 2016/797 and contribute to railway safety in application of Directive (EU) 2016/798.

However, applications relating to “harmonization of standards” relate to ISO standards and not to standards directly linked to railway safety such as the CENELEC EN 50126 standards (operational safety: Reliability, Availability, Maintainability and Safety), EN 50128 (software safety) and EN 50129 (hardware safety) which are regulatory obligations imposed by European directives. Therefore, to improve the overall efficiency of the railway system, it is appropriate to develop applications based on ontologies with a view to harmonizing and helping to operate these railway standards.

Applications relating to “railway signaling” (ERTMS/ETCS) fall within the framework of the “functional” subsystem and relate to “control-command and signaling.” In this context, two studies were proposed by the authors. The first aims at an ontology to model the European ERTMS/ETCS (sic) system and the second study aims to consider the temporal aspect of this same system. However, it seems to us quite difficult to develop a complete ontology of this system which is quite complex and moreover the article in question does not really show this ontological modeling.

In addition to these applications studied, and in accordance with Directive (EU) 2016/798 relating to railway safety, it is interesting to consider research work based on ontologies for decision support, particularly for decision making during the preparation of the safety certificate by the railway companies, during the preparation of the safety approval by the infrastructure manager, during the authorizations to put the railway system into operation granted by the European railway agency and/or the national safety authorities of each Member State. Finally, an interesting application could be the analysis and evaluation of risks from the system design phase and more precisely during the identification of hazards and the development of the hazard register imposed by the safety management system. This last perspective is currently being developed by the author.

Funding

The author declares that he has no specific funding for the production of this article.

Authors’ contributions

This study was developed, written and revised by the author.

Conflict of interest

The author declares that he has no known competing financial interests or per-
sonal relationships that could appear to influence the work reported in this article.
**Ethics approval and consent to participate**

Ethical approval and consent for participation were not required for this study, as it is a literature review on the applications of AI in the transportation sector. Therefore, no human subjects were involved, eliminating the need for ethical considerations or consent.

**Consent for publication**

The author of this article consents to its publication.

**Availability of data and materials**

The data and materials used in this research are available from the corresponding author on request.

**Author details**

Habib Hadj-Mabrouk
Gustave Eiffel University, France

*Address all correspondence to: habib.hadj-mabrouk@univ-eiffel.fr

**IntechOpen**

© 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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