A Visual Analysis of Hazardous Events in Contract Risk Management

Georgios Stathis

1, 2 a

, Giulia Biagioni

2 b

, Athanasios Trantas

2 c

, Jaap van den Herik

1 d

and Bart Custers

1 e

eLaw - Centre for Law and Digital Technologies, Leiden University,

Kamerlingh Onnes Building, Steenschuur 25, Leiden, The Netherlands

Unit ISP, TNO, New Babylon, Anna van Buerenplein 41a, Den Haag, The Netherlands

Keywords:

Intelligent Contracts, Contract Risk Management, Bow-Tie Method, Legal Visualisation, Preventive/Proactive

Law, Ontology Engineering, Ontology Visualisation.

Abstract:

This article proposes a new visual analysis method of hazardous events to be used in contract risk management.

We present our research work for the creation of an ontological extension of the Onassis Ontology to manage,

analyse and visualise risk data. Onassis is an openly accessible ontology that we earlier designed to structure

contract automation data. Onassis and its extension for risk management contribute to the development of

trustworthy Intelligent Contracts (iContracts). They allow for the creation of explicit data out of usually im-

plicit contractual information and legal processes on which it is possible to perform cross-referencing analysis

with other collections of data. The ontological model that resulted from our study additionally contributes to

the disambiguation of the bow-tie method structure, the primary method for analysing and visualising haz-

ardous events. To achieve this, we use the following methodology. We visualise the bow-tie method in an

ontology and then investigate the presence of taxonomic ambiguities or even errors in its structure. The results

present an enriched version of bow-tie conceptualisation, in which entities and relationships are translated into

openly-accessible and ready-to-use ontological terms, whereas risk analysis becomes visible.

1 INTRODUCTION

The world of LegalTech is facing the dawn of In-

telligent Contracts (iContracts). iContracts aim to

support end users with drafting legal documents

via the adoption of automation techniques (Mason,

2017). Thus far, iContracts have not been designed

to include the editing and visualisation of risk data

(Stathis et al., 2023). Nevertheless, risk data play a

fundamental role in contract risk management.

• Deﬁnition 1: Risk Data denotes a deﬁned set of

information (data), in any format (but increasingly

in digital form), that is used by an organisation

for diverse Risk Management and other business

processes

Nowadays, legal experts act as legal risk man-

https://orcid.org/0000-0002-4680-9089

https://orcid.org/0000-0002-9005-7945

https://orcid.org/0000-0001-7109-9210

https://orcid.org/0000-0001-9751-761X

https://orcid.org/0000-0002-3355-8380

https://www.openriskmanual.org/wiki/Risk Data

agers who examine legal risk data in order to safe-

guard the interests and rights of the parties that are in-

volved in a given agreement. Cross-investigation be-

tween different sources (such as databases) may help

users quickly identify the risks associated with the

(legal) documents that they are drafting(Haapio and

Siedel, 2013). In analytical processes, both visuali-

sation methods and ontologies (i.e., formal represen-

tations of knowledge) can be truly beneﬁcial to carry

out a conjoint analysis of diverse data sets (Hogan,

2020) and (Dud

s et al., 2018). They can be used to

(1) reach a deeper understanding of the risks involved

in a certain arrangement, (2) design an efﬁcient strat-

egy to visualise the manifestation of a potential haz-

ardous event, and (3) develop remedies and repair

mechanisms prohibiting disastrous events. Although

these methods may lead towards remarkable analyt-

ical results, their potential has yet to be completely

unlocked. This is mostly owing to two reasons. First,

there is no unambiguous visual structure that is unan-

imously used to observe and analyse the core entities

of the risk management framework as well as their re-

lationships (de Ruijter and Guldenmund, 2016). Sec-

Stathis, G., Biagioni, G., Trantas, A., van den Herik, J. and Custers, B.

A Visual Analysis of Hazardous Events in Contract Risk Management.

DOI: 10.5220/0012049600003541

In Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023) , pages 227-234

ISBN: 978-989-758-664-4; ISSN: 2184-285X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

227

ond, there is no ready-to-use and openly accessible

ontology that has been developed to disambiguate any

given reference system (Agrawal, 2016). Therefore,

to offer a contribution to the resolution of the prob-

lem areas mentioned in (1), (2), and (3), our research

will focus on the creation of an ontological model to

analyse, visualise, and manage risk data in iContracts.

In so doing, it will particularly examine and discuss

the limitations of the bow-tie visualisation medium to

manage and visualise risk data.

In 1979, a diagram to analyse hazards was ﬁrst

presented at the Imperial Chemistry Industry course

of the University of Queensland in Australia (Kluwer,

2017). The shape of the ﬁgure sent back took the form

of a bow tie, from which it consequently took the

name. Today, the bow-tie diagram is de facto stan-

dard used to perform visual legal risk management

analysis. The method mirrors the conceptualisation

framed in ISO 31000:2018 that has been developed by

the International Standardization Organisation (ISO)

(Kishchuk et al., 2018) and (de Ruijter and Gulden-

mund, 2016). ISO 31000:2018 is a guideline that

provides a generic framework for the management of

all types of risks, including legal risks

. The bow-

tie visualisation system makes the relationships be-

tween the entities designated by ISO discernible and

explicit. Although attempts have been made, the theo-

retical structure of both ISO 31000:2018 and the bow-

tie method has not yet been translated into the expres-

sivity of a ready-to-use ontology (Agrawal, 2016).

Ontologies enable, inter alia, model-based meta-

analysis (Becker and Alow, 2019). Meta-analysis can

be applied to conduct different risk management anal-

yses either to infer new pieces of information or to

draft more solid clauses related to risks in contracts.

Ontologies are designed to reduce ambiguity between

the entities and clarify the relationships between them

(Nirenburg and Raskin, 2001).

According to Ruijter and Guldenmund, there is no

consensus on the speciﬁc deﬁnition of the bow-tie vi-

sualisation system except its shape and core concepts

(de Ruijter and Guldenmund, 2016). The main di-

verging points may originate from the ambiguity of

the relationships among the entities in the bow-tie

structure. This may result in subjectivity, in terms of

both interpretation and intended use.

To clarify the bow-tie and ISO 31000:2018 struc-

ISO is an independent, non-governmental interna-

tional organisation with a membership of 164 national

standard bodies, founded in 1947 and headquartered in

Geneva, Switzerland. In this concern cf.https://www.iso.

org/about-us.html

ISO, (2018), 31000:2018: Risk management – Guide-

lines, iso.org, abstract

tures, and derive new insights into the method and

standard, we will therefore design an ontology mir-

roring the relationships portrayed in the bow-tie di-

agram. We will then test it against taxonomic con-

straints that lead to the creation phases of ontologies.

The resulting ontological vocabulary will be linked

to the Onassis ontology that we previously designed

(Stathis et al., 2023)

Designing a set of machine-understandable vo-

cabulary terms allowing to monitor and manage risk

in relation to the violation of speciﬁc contractual

clauses means taking the expressiveness of iContracts

a step further. Moreover, having openly accessible

ontological vocabularies for risk management data

further allows smaller entities with limited economic

availability to implement monitoring strategies to re-

duce the occurrence of hazardous events in relation

to contractual clauses. By including risk data and risk

management strategies, iContracts will not only serve

the purpose of formally describing an agreement be-

tween different parties, but will also even monitor and

prevent the occurrence of hazardous events connected

to legal risk. Thus, the avoidance of dispute conse-

quences becomes possible.

The contribution of this research is therefore two-

folded. First, it explores the limitations of the bow-tie

visualisation method to perform large scale analysis.

This will similarly bring new insights into its struc-

ture. Second, it proposes a set of openly-accessible

vocabulary terms to structure and manage legal risk

data.

As a result of the aforementioned information the

RQ guiding our investigation is: To what extent is it

possible to translate the bow-tie method into a visu-

alisation of an ontology for contract risk management

without altering the bow-tie structure?

We structured the paper as follows. Section 1 pro-

vided the introduction. In Section 2, a brief literature

review is provided. Section 3 presents the method of

research. Section 4 states the results of our research

and Section 5 discusses those results. Finally, Section

6 answers the RQ and provides our conclusion.

2 LITERATURE

In this section, the state-of-the-art literature is pre-

sented. Section 2.1 introduces sources on contract

risk management. Section 2.2 presents the latest re-

search regarding ontologies developed to structure

risk management data.

Onassis is accessible at https://github.com/

onassisontology/onassisontology

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

228

2.1 Contract Risk Management

The most exhaustive academic source on contract

risk management, which is targeted to practition-

ers, is Haapio and Siedel’s book, Guide to Contract

Risk (Haapio and Siedel, 2013). Contract risk can be

identiﬁed in multiple sources. Ideally a database of

contract risks should exist to help legal experts iden-

tify instances more quickly and efﬁciently, as it hap-

pens in other domains(Patterson and Neailey, 2002)

and (Kuwahara et al., 2015).

In the industrial, energy and environmental areas,

visualisation methods, such as the bow-tie, are often

used to manage risk and prevent the occurrence of

hazardous events within domain-speciﬁc projects (for

Chemical Process Safety, 2018). A bow-tie method

is used for visualising risk in a holistic manner by

taking into consideration proactive and reactive risk

measures (Kluwer, 2019).

Figure 1: Bow-tie method.

A bow-tie diagram helps to visualise and control

contract risk (Haapio and Siedel, 2013). It has been

used in a variety of risk analysis environments and for

various risk management purposes (Khakzad et al.,

2012).The usefulness of the bow-tie method stems

from its ability to visualise the complexity of legal

risk (Dauer, 2006). Figure 1 shows a representation

of the bow-tie method.

The bow-tie method mirrors, at the level of the

entities, the conceptualisation framed in the ISO

31000:2018 standard

. The standard deﬁnes eight

concepts that are considered to be essential to man-

age risk: Risk, Risk Management, Stakeholder, Risk

Source, Event, Consequence, Likelihood and Control.

In multiple industries, including the legal industry, it

is also possible to develop a risk matrix or risk reg-

ister after visualising the risk management process

ISO, 2018, ISO 31000:2018 Risk management —

Guidelines, iso.org)

via the bow-tie diagram (Leva et al., 2017). In the

risk matrix/register, beyond the Risk, Cause, Conse-

quence, Likelihood, Impact, one may also add a Pri-

ority Ranking, Response plan, the entity Responsible

for the plan, a Deadline, and a Validation section (Lu

et al., 2015). Consequently, risk ranking becomes

possible for a clearer visualisation of the immediate

risks requiring management as well as a contract risk

response. In relation to contract risk, the response

mostly relates with contract drafting and procedural

aspects (Espenschied, 2010; Fox, 2008).

2.2 Ontologies for Contract Risk

Management

Although ontologies may be a powerful instrument to

perform contract risk management analysis, they are

not very common in the ﬁeld. This is mostly due to

the scientiﬁc immaturity of the domain. However, ef-

forts have been made to build a framework enabling

both the analysis of the risk assessment process, and

the codiﬁcation of the relationships (i.e., roles and re-

sponsibilities) between the various entities involved

in a risk management organisation. Examples are

provided by the Description of a Model Ontology

(DOAM)

, and the Risk Function Ontology (RFO),

respectively

. Although DOAM and RFO offer great

contributions to the ﬁeld, they still lack the needed

level of expressiveness that can allow users to struc-

ture risk management data at a processing level while

framing information within the modus operandi of the

bow-tie method. Regarding ontologies aiming to mir-

ror the implementation phases of the bow-tie process,

we could not ﬁnd any vocabularies designed for this

speciﬁc purpose either on the Linked Open Vocabu-

laries (LOV) catalogue

or the Open Risk Manage-

ment (ORM) Foundation website

. Ultimately, we

did observe an attempt to develop an ontology for the

ISO risk management standards which did not result

in a ready-to-use model (Agrawal, 2016).

3 METHOD

Our methodology aims to identify (1) the possible

presence of violations of taxonomic constraints, and

(2) ambiguous constructs in the conceptualisation of

the bow-tie visualisation medium. In 3.1, we trans-

https://www.openriskmanual.org/ns/doam/index-en.

html

https://www.openriskmanual.org/ns/rfo/index-en.html

https://lov.linkeddata.es/dataset/lov

https://www.openriskmanagement.com/

A Visual Analysis of Hazardous Events in Contract Risk Management

229

lated the bow-tie design into the expressiveness of an

ontology by mirroring the bow-tie relationships and

entities. In 3.2 we introduce three types of taxonomic

errors. We subsequently, in 4.1, check the presence

of taxonomic ambiguities and errors by following the

best practices for the development of ontologies and

the detection of fallacies in the models. The outcomes

of our analysis are presented under results.

3.1 Ontology Visualisation of the

Bow-Tie Method

As presented in the previous section and discussed by

(de Ruijter and Guldenmund, 2016), the entities pic-

tured in the bow-tie method relate to one another as

follows.

1. The causes of a hazardous event are protected by

proactive controls.

2. The proactive controls relate to a hazardous event.

They result from the causes and are designed

based on the nature of the hazardous event.

3. The hazardous event is contained by both the

proactive and reactive controls (barriers).

4. The reactive controls are conceptualised based

upon the nature of the hazardous event to

marginalise its consequences. They relate to both

a hazardous event and its consequences.

5. The consequences are limited by the reactive con-

trols to which they relate.

6. The risk which is not represented in the bow-

tie diagram can be intuitively associated with the

hazardous event itself. This is mostly based upon

the guidelines of the ISO 31000:2018 standard.

7. The stakeholder, who is not represented in the

bow-tie diagram, can be intuitively associated

with the hazardous event. This can be derived

from the guidelines of the ISO 31000:2018 stan-

dard.

8. The likelihood, which is not represented in the

bow-tie diagram, measures the probability of the

occurrence of a hazardous event as described in

the guidelines of the ISO 31000:2018 standard.

When translating the entities and relationships of the

bow-tie diagram into the expressiveness of an ontol-

ogy, we will ﬁrstly attain the structure presented in

Figure 2.

The validity, efﬁciency, and consistency of the on-

tological structure presented in Figure 2 can be tested

against the presence of taxonomic errors.

Figure 2: Bow-tie Centred Ontology.

3.2 Three Types of Taxonomic Errors

According to (Gomez-Perez, 1995), (G

omez-P

erez

et al., 2004), (G

omez-P

erez, 2001), (Fahad et al.,

2008) and (Fahad and Qadir, 2008), there are three

main types of taxonomic errors. They are inconsis-

tency errors, incompleteness errors, and redundancy

errors.

Inconsistency errors may be caused by circulatory

errors (i.e., entities deﬁned as sub-entities or super-

entities of itself), partition errors (i.e., instances be-

longing to various disjointed classes), or semantic in-

consistency errors (i.e., when ontologists deﬁne con-

cepts as sub-classes of concepts to which they do not

pertain).

Incompleteness errors may be of three types,

namely, (a) incomplete concept errors (they occur

when concepts and relationships of the domain are

overlooked and not deﬁned in the structure), (b) in-

complete axiom errors (they occur when ontologists

omit important axioms and information about the

classiﬁcation of a concept), or (c) sufﬁcient knowl-

edge emission errors (they take place when concepts

have only necessary descriptions). Incompleteness er-

rors lead to ambiguity and create a lack of proper rea-

soning mechanisms.

Redundancy errors happen when pieces of infor-

mation are inferred more than once from the relation-

ships, classes, and instances of the ontology. They

can be: redundancies of sub-class/sub-property er-

rors (they may occur when classes or relationships di-

rectly or indirectly have more than one sub-class/sub-

property relationship); identical formal deﬁnition of

classes, properties and instances (i.e., proprieties,

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

230

classes and instances have different names but provide

the same formal deﬁnition); or redundancy of disjoint

relationships (i.e., concepts explicitly deﬁned as dis-

jointed from other concepts more than once).

In order to derive more insights into the bow-tie

method and to test the actual taxonomic validity of

the bow-tie centred ontology displayed in Figure 2,

we tested this ontology against the three types of tax-

onomic errors discussed in the preceding paragraphs.

Our results are presented in the subsequent section.

4 RESULTS

This section presents the results of our research. The

results concern the taxonomic errors in the ontology

visualisation of (4.1) the bow-tie centred ontology and

(4.2) the enriched bow-tie ontology.

4.1 Taxonomic Errors in the Bow-Tie

Centred Ontology

The bow-tie centred ontology (Figure 2) presented in

the previous section mirrors the exact relationships

that are framed by the bow-tie method (Figure 1) and

a few of the concepts of ISO 31000:2018. As dis-

cussed above, the risk, stakeholder, and measure of

likelihood are not addressed by the bow-tie analytical

medium. This resulted in the absence of their con-

cepts from the bow-tie centred ontology. The same

can be said for the measure of likelihood regarding

the occurrence of a hazardous event. The lack of these

concepts in the diagram and ontology causes several

knowledge emission errors. Other incompleteness er-

rors that can be found in the bow-tie centred ontology

are incomplete concept errors.

In the bow-tie diagram (Figure 1), only the rela-

tionships between (1) the causes and proactive con-

trols, (2) the proactive controls and hazardous event,

(3) the hazardous event and reactive controls, and (4)

the reactive controls and consequences are described.

However, these four relationships may lead to am-

biguity when performing cross-referencing analysis

with a bow-tie centred ontology and/or diagram.

Below we discuss two types of ambiguities. First,

the problems identiﬁed in the bow-tie centred ontol-

ogy originate from the ambiguity in the relationships

between the entities that are framed by the bow-tie vi-

sualisation method. Second, the taxonomic errors that

we individualised, namely incomplete concept errors

and sufﬁcient knowledge emission errors, do not only

concern the relationships between causes, proactive

controls, and the hazardous event (i.e., the left side of

the bow-tie visualisation method) but even the rela-

tionships between the hazardous event, reactive con-

trols, and consequences. Ultimately, no other type of

taxonomic errors have been detected in the bow-tie

centred ontology.

4.2 The Enriched Bow-Tie Ontology

To resolve the taxonomic errors identiﬁed in the bow-

tie centred ontology (Figure 2), we shifted the re-

lationships in the previously presented ontological

model from a cause-sequential order of connected en-

tities to a node-centred one. In the resulting version of

the model, the cause, proactive control, reactive con-

trol, and consequence are directly connected to the

hazardous event. We identiﬁed the hazardous event as

the most core component. The hazardous event is here

conceived as a physical occurrence possibly associ-

ated with a point in time. Furthermore, we considered

the cause, reactive control, proactive control, and con-

sequence as human-derived observations rather than

actual physical situations. This makes it so that ev-

ery time a hazardous event is identiﬁed by a legal ex-

pert, a unique identiﬁer shall be created for it, regard-

less of whether its nature is similar or identical to an-

other event. Unlike the case of the hazardous event,

the identiﬁers of identical causes, reactive controls,

proactive controls, and consequences can be reused

across use cases.

As previously discussed, the bow-tie visualisation

method (Figure 1) does not include (1) the relation-

ship between the hazardous event and the risk, (2) the

relationship between the hazardous event and stake-

holders, and (3) the relationship between the mea-

sured probability of a hazardous event and the haz-

ardous event itself.

The new vocabulary terms of the Onassis ontology

demonstrate the relationships presented above as fol-

lows. The risk is connected to a hazardous event that

is, in turn, linked to a measure of probability. Based

on the structure of the risk matrix analysis, we con-

nected the stakeholder directly to the impact of a haz-

ardous event. The probability and impact measures

are then associated with a level of risk which is subse-

quently linked to the risk itself. Both probability and

impact measures are connected to a source. Figure 3

displays the new vocabulary terms that we added to

the Onassis ontology.

To conclude, the rationale behind the new classes

and properties of the Onassis Ontology can be de-

scribed as follows.

1. The Risk involves a Hazardous Event and a Risk

Measure.

A Visual Analysis of Hazardous Events in Contract Risk Management

231

Figure 3: New vocabulary terms of the Onassis Ontology.

2. The Hazardous Event has a Cause, Proactive Con-

trol, Reactive Control, Impact, and Consequence.

3. The Cause is contained by the Proactive Control.

4. The Consequence is contained by the Reactive

Control.

5. The Impact of a Hazardous Event affects an

Agent.

6. The Hazardous Event has a Probability number.

7. The numbers of the Probability and Impact result

in a Level of Risk.

8. They are based on a Source.

9. The Level of Risk is ultimately involved in a Risk.

To design the new vocabulary terms for Onas-

sis, we used the Resource Description Frame-

work Schema (RDF/S) and Ontology Web Language

(OWL).

The logical consistency of the new vocabulary

terms has been tested via an ontology reasoner

. The

coherency of the model with domain knowledge has

been validated by running competency questions on

sample data which are accessible via GitHub

5 DISCUSSION

The discussion concentrates on examining the re-

search consequences for (5.1) ontology visualisation,

(5.2) contract risk management, and (5.3) iContracts.

Speciﬁcally by launching reasoner Hermit

1.4.3.456 on sample data in the Prot

e edi-

tor: https://mvnrepository.com/artifact/net.sourceforge.

owlapi/org.semanticweb.hermit/1.4.3.456

https://github.com/onassisontology/onassisontology/

blob/main/img/Visualisation.png

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

232

5.1 Ontology Visualisation

Visualisation analysis allows users to examine and

compare large amount of data (Dud

s et al., 2018).

However, if some details are omitted, they can mis-

lead analysts by leading them to erroneous analytical

results. As it is possible to observe from the applica-

tion of ontological taxonomic constraints on the bow-

tie structure, the bow-tie visualisation system suffers

from some limitations. In a large-scale comparative

analysis, the bow-tie method can possibly lead to data

ambiguity due to the lack of direct relationships be-

tween the core component (i.e., hazardous event) and

related entities (i.e., cause, consequence, proactive

and reactive controls). Furthermore, we recognised

that the system does not represent all concepts theo-

rised in the ISO 31000:2018 standard to manage risk.

To overcome the above discussed problems, we cre-

ated an ontological model that embraces the full ex-

pressiveness of the ISO 31000:2018 standard. Ulti-

mately, we ensured the presence of connections be-

tween all entities described by ISO in order to prevent

the occurrence of ambiguity in ontological knowledge

representations.

The new vocabulary terms that we designed ex-

tend the Onassis core ontology that we previously cre-

ated. They do not aim to replace the conceptualisation

of the entities of the bow-tie method. They focus on

enriching it while providing a ready-to-use ontologi-

cal model to manage and describe risk data in iCon-

tracts. When visualised, the enriched bow-tie model

takes the shape of a more complex and amorphous

Figure 3 rather than the classical papillon Figure 1.

This is due to the granularity of the ontolology.

5.2 Contract Risk Management

Having openly-accessible vocabulary terms to de-

scribe risk data is vital for improved analysis and

management of contract risk. A legal expert is able

to (1) better deﬁne contract risk, (2) improve contract

clauses, and (3) better understand the level of risk per

contract. An additional beneﬁt is that the open-source

nature of our work can help companies exchange risk

data to reﬁne the measuring of impact probabilities

for improved risk ranking.

Although we identiﬁed some limitations in the

conceptualisation of the risk management visualisa-

tion framework provided by the bow-tie method, we

equally deem that such a structure can play a funda-

mental role in cross-referencing analysis with differ-

ent collections of data to manage risk if the poten-

tial cause of ambiguity is resolved. Based on this

consideration, we decided to enrich the bow-tie by

(1) adding more relationships in its structure and (2)

translating the “enriched bow-tie” into the expressive-

ness of an ontology. This will allow interoperability

between data sets while hopefully leading to the at-

tainment of new insights for the evaluation of risk in

contractual clauses.

5.3 Intelligent Contracts

Our research shows that risk data are able to con-

tribute into making iContracts more responsible. The

responsibility mainly derives from the fact that risk

data can be explicitly examined thanks to the cre-

ation of a set of interlinked metadata (i.e., the exten-

sion of the Onassis ontology that we designed) that

can be used to structure information regarding risk.

The new vocabulary terms can, in fact, make the usu-

ally implicit information about contact risk evident

and clear while fostering the possibility to compare

different data collections coming from diverse data

sets. As a result, risk management can improve due

to the large scale comparative analysis of diverse data

records (Haapio and Siedel, 2013).

6 CONCLUSION

The present section provides (a) the answer to the RQ,

(b) further research suggestions, and (c) the research

novelty.

Regarding our RQ (viz. To what extent is it pos-

sible to translate the bow-tie method into a visuali-

sation of an ontology for contract risk management

without altering the bow-tie structure?), we provide

the following answer.

The conversion process of the bow-tie conceptu-

alisation into ontological terms highlighted the pres-

ence of missing relationships between entities in the

bow-tie method as well missing ISO-speciﬁed con-

cepts. To reduce the possibility of introducing ambi-

guity into the analytical and management processes of

risk data, we searched for and found further relation-

ships between entities in the ontology compared to

those that are represented in the bow-tie system. Ulti-

mately, we added the missing ISO-speciﬁed concepts

that were not present in the bow-tie method. This re-

sulted into a new version of the bow-tie visualisation

medium as well as an openly-accessible ontological

model to manage and describe risk data.

In relation to further research, the key question

at this point is how to best move forward from here.

An essential step in conducting further research is to

validate the successful integration of the developed

contract risk management ontological framework in

A Visual Analysis of Hazardous Events in Contract Risk Management

233

iContracts. Our following research will focus on val-

idating the integration experimentally.

The research novelty is that we have demonstrated

how it is possible to make explicit a traditionally im-

plicit process, in such a way that data processing be-

comes possible. So far, legal experts have not reached

consensus on how to manage contract risk. Our pa-

per shows how it is possible. Moreover, in relation to

the bow-tie method, we presented a new theoretical

version for it which builds upon the old one, disam-

biguate some of the bow-tie constructs, and enrich its

conceptualisation.

ACKNOWLEDGEMENTS

Georgios is the main author. Giulia provided a sci-

entiﬁc framework for the research. Athanasios con-

tributed in the literature. Jaap and Bart are the main

supervisors.

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