# Building a Knowledge Graph of Distributed Ledger Technologies

L.Lukas<EMAIL_ADDRESS>Corresponding author. .[
S.Sebastian<EMAIL_ADDRESS>[ St. Pölten
University of Applied Sciences, Austriapresep=
]e1

(0000)

###### Abstract

Distributed ledger systems have become more prominent and successful in recent
years, with a focus on blockchains and cryptocurrency. This has led to various
misunderstandings about both the technology itself and its capabilities, as in
many cases blockchain and cryptocurrency is used synonymously and other
applications are often overlooked. Therefore, as a whole, the view of
distributed ledger technology beyond blockchains and cryptocurrencies is very
limited. Existing vocabularies and ontologies often focus on single aspects of
the technology, or in some cases even just on one product. This potentially
leads to other types of distributed ledgers and their possible use cases being
neglected. In this paper, we present a knowledge graph and an ontology for
distributed ledger technologies, which includes security considerations to
model aspects such as threats and vulnerabilities, application domains, as
well as relevant standards and regulations. Such a knowledge graph improves
the overall understanding of distributed ledgers, reveals their strengths, and
supports the work of security personnel, i.e. analysts and system architects.
We discuss potential uses and follow semantic web best practices to evaluate
and publish the ontology and knowledge graph.

URI: https://w3id.org/DLTOntology
DOI: 10.5281/zenodo.6497619

Distributed Ledger Technology,

Blockchain Security,

Ontology,

Knowledge Graph,

###### keywords:

††volume: 0

##  1 Introduction

While the success of blockchains and especially cryptocurrencies has helped to
spread the word about distributed ledger technology (DLT), it has lead to
misunderstandings about what the technology actually consists of and what it
is capable of. In [1] this is highlighted very well with a comparison to
facial tissues and the brand product Kleenex, which often get used
interchangeably, even though the latter is just one implemented variant of the
former. Such a misunderstanding of the technology can hinder its adoption.
Each variant of distributed ledger technology operates in a slightly different
way with regard to distinct requirements, which therefore means that there are
also different usage scenarios and implementations that can be realized with
distributed ledgers. Limiting the broad field of the technology to just one
single variant of it leads to a distorted over-representation and use of that
one variant, because the valid alternatives are simply not emphasized in the
same way. There are observable attempts of reverting this trend by stating the
technicalities and comparing different types of distributed ledger systems to
one another, with new research and reviews stating up to 5 different types
that are regularly seen as equally valid implementations of a distributed
ledger system [2,
3].111https://i.pinimg.com/originals/9d/d1/1b/9dd11bbaf5f025f3bd345e6816a4fc16.png
222https://medium.com/@support_61820/different-types-of-dlts-and-how-they-
work-cfd4eb218431

One way to unify the attempts of providing clarity on distributed ledgers is
to use a knowledge graph to capture different technical capabilities and the
wider ecosystem. In recent years, the concept of knowledge graphs has become
increasingly popular, the idea being to use a graph-based data model to
collect and convey knowledge about the real world [4]. To define the semantics
of the terms used in the graph, an ontology, i.e. a formal representation of
the relationships and classes in the graph, is used [5]. As we point out in
detail in Section 5, an all-inclusive approach towards a distributed ledger
ontology is currently still lacking. Existing ontologies and vocabularies
typically focus on a limited sub-group of distributed ledger systems, or build
an ontology for a single distributed ledger product or application.
Blockchains and blockchain-based systems are often the focus of such works,
which leaves a large part of distributed ledgers unaccounted for.

This work therefore establishes a modular ontology, which encompasses and
relates the following concepts and areas: Distributed ledger technology with a
broader perspective than just a limited focus on blockchains, broader security
considerations and technical implications of such systems, as well as
organizational boundaries and real world applicability. Additionally, this
ontology and the respective knowledge graph aims to clarify the distinctions
between different types and systems of distributed ledgers, as it includes a
broader spectrum than merely the view on blockchain as the paragon of the
entire technology.

The model and subsequent knowledge graph created in this work can be used in a
multitude of ways: Besides the already mentioned perspective of the entire
ecosystem, it also allows for a more distinct observation of the included
subjects. For instance, it can be used for threat analysis and risk
assessments by identifying attacks and vulnerabilities of a system. In
particular, security analysts will benefit from such a model, as it enables
them to store and retrieve critical information about systems they might use,
and to highlight further measures where they are needed to secure a system.

The main contributions of this work can be summarized as follows:

  * •

a survey of literature and information on distributed ledger technologies,
use-cases, fields of application, standardizations and legal issues;

  * •

a set of competency questions that cover the main aspects of the collected
information;

  * •

an RDFS-based ontology that allows to model the surveyed aspects;

  * •

a knowledge graph of relevant entities and relations, extracted from the
literature;

  * •

an evaluation of the model via SPARQL queries; an online interface to execute
the queries.

The remainder of this paper is structured as follows: Section 2 provides an
overview about distributed ledger technologies, as well as a short
introduction to Ontologies, Knowledge Graphs and their terminologies. Section
3 details the methods, i.e., the information collection, the competency
questions and evaluation of the ontology. Section 4 describes the ontology in
detail, and explains the elements and contents of the knowledge graph. In
Section 5 we discuss related work and conclude in Section 6.

##  2 Background

###  2.1 DLT Data Structures

#### Blockchain.

The most well-known example of a distributed ledger technology is a blockchain
system. Especially the specific blockchain application Bitcoin sparked a hype
around cryptocurrencies that lead to an often misunderstood terminology where
Bitcoin and Blockchain are used interchangeably [6, 7], which is not true
however. There are many more blockchain applications; cryptocurrencies are
merely one aspect of the broader picture. Other examples of blockchain
technologies are Ethereum and its renewed introduction of smart contracts,
which have become increasingly popular over the last years, as well as the
Hyperledger project by the Linux Foundation and IBM [8].
At its technological core, a blockchain is a data structure that
(cryptographically) links blocks of data into a chain of blocks. Blockchains
are usually further separated into either public, private or federated
blockchains, according to the degree of access that is implemented where a
public blockchain is accessible to anyone and a private blockchain is
restricted and usually used internally [2, 9].

#### Directed Acyclic Graph.

Directed Acyclic Graphs (DAG) as a data structure used for distributed ledger
systems started to emerge with the applications NXT, and IOTA with its
_Tangle_ , which is especially focused on IoT, micro transactions and devices
with lower computational power [2, 3]. Instead of having data blocks hashed
into one single line, DAGs are used where connections between transactions in
the graph show where a valid transaction is present. Since each transaction is
processed individually and does not require the formation of blocks as with a
regular blockchain, there is technically no transaction limit for DAGs and
therefore they are very scaleable.

#### Hashgraph.

The most prominent product for Hashgraphs is _Hedera Hashgraph_ as a next
generation DLT that focuses on fairness and transaction speed with regard to
the order of events as they unfold. It is very efficient to use as it uses
virtual voting for reaching consensus and information about transactions are
shared with a gossip protocol. Technically, the data structure used for
Hashgraphs is similar to a DAG, which is bound to the sequence of time and the
nodes participating in the network. By that they fundamentally differentiate
themselves from regular DAGs [2, 3].

#### Holochain.

Contrary to the previous types of DLTs, the major change with _Holochain_ is
the shift from a focus on data itself towards an agent-centric structure which
some consider the purest form of distributed ledger, as every node virtually
is responsible for its own ledger. Validation is done by the introduction of a
ruleset to the Holochain network. This ruleset is referred to as DNA and can
be used to verify a node and to spot malicious actors. The changes introduced
with Holochain make mining obsolete, which makes it a very energy efficient
alternative [3].

###  2.2 Security Challenges, Opportunities and Use Cases for Blockchains &
Distributed Ledger Systems

As with regular ICT systems, distributed ledger technologies are not immune to
threats. While the introduction of distributed ledgers by itself can mitigate
certain risks already, it also introduces new dangers that need to be
considered. Especially since there are a number of myths revolving around the
technology that make it seem almost invincible [6, 8].

#### Organizational Challenges.

One of the challenges of using blockchains for supply chains or as a mean of
ensuring transparency and auditability is that if manual input is required,
the people involved are still prone to errors and false input [10].
Additionally, the knowledge about and familiarity with distributed ledgers is
still limited generally speaking. This lack of knowledge also hampers the
willingness to become an early adopter of the technology [10, 7, 6]. Another
challenge would be a lack of standardization for distributed ledgers or the
integration into existing standardization frameworks or questions about
governance, legal requirements, and regulations, especially for cyber security
[11, 12]. This challenge is only aggravated by the fact that there is no
unified terminology or vocabulary as well [13].

#### Technical Challenges.

So far there has been intensive research on the vulnerabilities and attacks on
distributed ledger systems. A major part of these focuses on blockchain
systems where _Bitcoin_ and _Ethereum_ are often times the center of
attention. Examples for blockchain attacks are 51%-Attacks, Fork Attacks, and
Selfish Mining [14]. Additionally, blockchains are often affected by
scalability issues and low transaction rates due to mining and consensus
mechanisms [15, 16]. For other forms of distributed ledger, like a DAG, there
is a strong focus on IoT and IIoT [17], where security features that are
present in a blockchain structure are sacrificed for an increase in
performance.

#### Opportunities and Use Cases

There are plenty of other fields of use for distributed ledger systems outside
of the world of cryptocurrencies. Since that is where the hype started, the
introduction of other financial applications that use the technology came to
no surprise. This includes not only payment services but also the financial
infrastructure and international finance for example [9, 18]. However,
distributed ledgers are also already extensively used in supply chains around
the globe as well. Instances of that would be shipping and logistics, ethical
supply chains, or food safety [10, 6]. Another opportunity for distributed
ledgers is their use for government services, be it for voting, record-
keeping, transparency or combating fraudulent activities [9, 18]. Other
recommended and proposed fields for the use of distributed ledger technology
are for example healthcare, identity management, the internet of things,
notary and insurance services, and (intellectual) property ownership [19, 6].

##  3 Methodology

Methodologically, our ontology creation process follows the recommendations by
Noy and McGuiness [5]. The modelling and creation of the ontology followed an
iterative process and was done using Protégé. The applied steps can be
summarised as follows:

  1. 1.

In the first step we determine the domain and scope of the ontology.

  2. 2.

We gather literature about distributed ledger technologies, their components
and existing standards, application domains, security vulnerabilities and
threats (cf. Sec. 3.2).

  3. 3.

We define a set of questions “that a knowledge base based on the ontology
should be able to answer.” [5] We derive use cases and create competency
questions based on the collected information (cf. Section 3.3).

  4. 4.

We create an ontology to express the collected information (cf. Section 4).

  5. 5.

We evaluate the ontology by providing SPARQL queries to answer the collected
competency questions.

  6. 6.

Finally, we instantiate the developed ontology with named entities and
relations extracted from the literature (cf. Section 4.1).

###  3.1 Ontology Scope

The scope of the ontology is a model for a holistic, modular approach on the
field of distributed ledger technology that does not focus on a single or a
subset of technologies, but rather the wider distributed ledger ecosystem. The
goal is to include factors which affect the implementation and operation of
distributed ledgers, including existing standards and its usage across the
industries.

As this ontology does not serve as an overview of one single product or
technology stack, the technological composition of a system is merely a
starting point for a broader observation that includes its strengths and
weaknesses, as well as the general applicability of a system for specific use
cases. It therefore serves as an application-neutral knowledge base for the
broader field of distributed ledger technology.

However, to achieve this general applicability it was necessary to focus on
the broader picture, which means that detailed technical implementations and
processes like message flows and triggers, and involved accounts are out of
scope.

###  3.2 Information Collection

The core concept of this ontology encompasses the three major areas
_technology_ , _business & market use_, and _legal & standardization_.

In Section 2 we already discussed major structural differences between
distributed ledger systems, including challenges and threats. The research in
this mentioned section forms the basis for the technical part of the ontology,
with additional information from further
comparisons333https://i.pinimg.com/originals/9d/d1/1b/9dd11bbaf5f025f3bd345e6816a4fc16.png.
Additionally, technological differences between variants of
blockchain/distributed ledger systems or smart contract platforms have been
discussed and compared in [20, 21], from where we drew additional input for
the ontology. Further information on the technological composition of
distributed ledgers and possible technology stacks can be found in [22, 23]
and from technical reviews444https://101blockchains.com/web-3-0-blockchain-
technology-stack/.

For use cases and the real-world application of blockchains and distributed
ledgers, we build upon existing assessments and research on the possibilities
and likelihood of distributed ledgers being used as a tool in different
business sectors, which can be found in e.g. [9,
6]555https://101blockchains.com/blockchain-digital-
transformation/666https://101blockchains.com/wp-
content/uploads/2019/01/Applications-of-Distributed-Ledger-Technology-DLT.png.
On top of that, these business sectors can further be split up into specific
use cases, with a majority of these being mentioned in Section 2.2 as well.

When it comes to organizational standardization of distributed ledger
technology, finished work that is entirely focused on it is scarce. Reports
like [7] offer a prediction and assessment of when and what to expect
regarding standardization areas, others see it as a hurdle which is
responsible for slow adoption rates of the technology, as mentioned in 2.2.
However, there are many standards in the works and several standardization
organizations have set up focus groups or committees to create new normative
standardization reference material specifically catering towards distributed
ledger technology and blockchain [11].

Legal issues on the other hand are still mostly unresolved and require further
considerations [24]. Especially when it comes to governance and (shared)
responsibility or liability when using blockchains. The introduction of
specified laws and a legal basis, in particular for governance and adoption of
the technology, is seen as a crucial factor by the authors of [25]. With ever
rising numbers in cyber crime and based on the existing catalogues of laws on
technology, it can be expected that distributed ledgers will receive their own
set of laws in the future.

###  3.3 Evaluation

The competency questions in Table 1 are based on the collected information
(see section 3.2) and grouped into three main categories:

  * •

Technology and Security involves questions regarding the components of a DLT
system, as well as technical threats and vulnerabilities of systems and
components. From this category we derive relations between vulnerabilities of
the respective systems and components, and attacks that exploit these
vulnerabilities.

  * •

Industry and Application involves questions about applications, business
sectors, and use cases of DLT systems. Relevant relations in this category are
the applications of DLT systems in specific use cases, as well as the mapping
of use cases to a business sector.

  * •

Standardization and Regulation involves questions about standards, technical
controls, standardization organizations, and relevant laws. From this category
we derive relations regarding compliance to standards, as well as the
technical control of specific DLT components.

Table 1: Competency Questions used to evaluate and validate the ontology. ID |  Competency Question
---|---
Technology and Security
T1 |  Which components are part of the distributed ledger system?
T2 |  Which technical threats have to be considered regarding the consensus algorithm of the system?
T3 |  What are known smart contract vulnerabilities?
T4 |  What are the data structures of the system?
T5 |  Which types of DLT attacks could be used against a system or its components?
Industry and Application
I1 |  Which use cases can be realised with distributed ledger technology?
I2 |  Which industries could use distributed ledger systems?
I3 |  Which distributed ledger systems are used for public transportation and smart cars?
I4 |  Which types of record keeping could be realized with distributed ledger technology?
Standardization and Regulation
S1 |  Which standardization organizations are active in regards to distributed ledger technology?
S2 |  Which normative references do exist for distributed ledger systems?
S3 |  Which industry standards do exist?
S4 |  What are relevant laws in regards to distributed ledger systems?
S5 |  What are organizational controls and mitigations for a distributed ledger system and/or component?
S6 |  Is there an industry initiative that directs and regulates the used distributed ledger system?

In Listing LABEL:lst:sparql_t2, LABEL:lst:sparql_i5, and LABEL:lst:sparql_s1
we give three SPARQL translations of the competency questions. In
LABEL:lst:sparql_t2 we ask for existing technical threats that potentially
threaten the consensus algorithm of a system; in LABEL:lst:sparql_i5 we
translate question I4, which asks for specific use cases of a DLT system; the
query in Listing LABEL:lst:sparql_s1 lists standardization organizations that
actively publish DLT standards.

⬇

PREFIX : <https://w3id.org/DLTOntology#>

SELECT ?threat WHERE {

?threat :threatens [ a :ConsensusAlgorithm ]

}

Listing 1: T2 - Which technical threats have to be considered regarding the
consensus algorithm of the system?

⬇

SELECT ?dltsystem ?usecase WHERE {

?dltsystem :isSpecializedFor ?usecase .

?usecase a :RecordKeeping .

}

Listing 2: I4 - Which types of record keeping could be realized with
distributed ledger technology?

⬇

SELECT ?stdorga ?standard WHERE {

?stdorga a :StandardizationOrganization ;

:creates ?standard .

?dltsystem :compliantTo ?standard .

}

Listing 3: S1 - Which standardization organizations are active in regards to
distributed ledger technology?

The complete list of questions as SPARQL queries for all competency questions,
including the respective classes and properties, can be found in the online
documentation: https://w3id.org/DLTOntology.

##  4 DLT Ontology and Knowledge Graph

Based on the collected information and competency questions we developed DLT
Ontology. The ontology covers the technical setup and components of a DLT
system, security aspects, as well as its applications and use cases. It covers
115 classes and 15 properties, and consists of a total of 571 triples.

The DLT ontology provides classes and properties which are used to describe
various aspects of a distributed ledger ecosystem. The core concepts are
displayed in Figure 1: DLTSystem and DLTComponent describe the essential parts
of a DLT system; Vulnerability and Attack connect potential security aspects
of the system; UseCase, and BusinessSector link existing applications of the
described systems.

Figure 1: Overview of the core concepts of the DLT Ontology.

The relations between the included classes are described in detail in Table 2,
where a property-centric view of the core concepts, including domains and
ranges of the properties of the ontology is provided.

Table 2: Main Properties of the DLT Ontology.

Property Description canExploit Domain: Attack Range: Vulnerability The
_canExploit_ property links an attack on a distributed ledger system or
component to the respective vulnerability, which could be used by an attacker
to enable further malicious activity in an effort to manipulate or gain
control of the system. threatens Domain: Attack Range: DLTSystem/Component
This property indicated that there is an existing attack on a distributed
ledger system or one of its components. Attacks can manipulate specific
components and data, or generally weaken the structural integrity of the
entire system. hasVulnerability Domain: DLTComponent Range: Vulnerability This
property indicates that one or multiple of the components used in a
distributed ledger system carry with them one or an array of vulnerabilities.
Having an overview of existing vulnerabilities is crucial for securing the
system. mitigates Domain: Technical/Org.Mitigation Range: DLTSystem/Component
Once disaster strikes, it is important to have a series of mitigations in
place, to reduce the impact of an attack, malicious event or generally in case
of the system breaking down. This property links a technical or organizational
mitigation measure to a distributed ledger system or specific component.
hasComponent Domain: DLTSystem Range: DLTComponent It links an individual
component to the DLT system. It is used to show what a system is comprised of
and to get a better understanding of the elements and building blocks. Such an
overview allows for an analysis or the implementation of measures for a single
component specifically and to tailor solutions to certain requirements.
isCompliantTo Domain: DLTSystem/Component Range: Standard When it comes to
using distributed ledger systems in an enterprise setting, national and/or
international standardization can become a necessity. This property expresses
compliance to existing standardization or normative references on a system or
component level. controls Domain: Technical/Org.Control Range:
DLTComponent/System Implemented controls can secure components and the entire
system itself. This property links a technical or organizational control
measure to a distributed ledger system or individual component.
createsStandard Domain: Stand.Organization Range: Standard Behind every
normative reference material and standardization there is an organization with
the needed authority to publish such material, which can be either a national
institute or an international organization. This property links a standard to
its organization. isRegulatedBy Domain: DLTSystem Range: Law As with regular
ICT-systems, a distributed ledger system will have to comply with the legal
status of the respective country it is used in by any organization. This
property connects existing legislation for ICT and DLT systems.
isSpecializedFor Domain: DLTSystem Range: UseCase Not all implementations of
distributed ledger systems are the same or similar in their nature and
complexion. This property links existing DLT systems which are tailored for
the delivery of a specific use case. hasUseCase Domain: BusinessSector Range:
UseCase This property is used to show which use cases could be used in a
selected industry. Not all forms of usage of distributed ledger systems are
fit for all types of business. isUsedFor Domain: DLTSystem Range:
BusinessSector There are certain distributed ledger systems that are
specialized for the use in one specific field or industry. This property is
used to highlight this exact relation. comesFrom Domain: IndustryInitiative
Range: BusinessSector It is entirely possible for several organizations of an
industry to form an initiative to steer and develop the future of distributed
ledger technology within that industry in a common effort. This property shows
the origins of an industry initiative with regards to the industry of involved
parties. hasBusinessSector Domain: UseCase Range: BusinessSector Distributed
ledger technology can be used to realize a variety of different use cases.
However, not all of them are a fit for any field or industry. This property
highlights the connection between a use case and the specific business
sector/industry it is used in.

###  4.1 Knowledge Graph

The knowledge graph uses the above introduced DLT Ontology, and is based on
the information collection described in Section 3.2. It consists of three
parts: (i) standards and legal authorities [11], (ii) technical details,
vulnerabilities and security aspects [14], and (iii) use cases and business
sectors [26]. Additionally, we reviewed white papers for further information
about the technical collocation of DLT systems
[27].777https://www.r3.com/reports/corda-technical-whitepaper/,
https://ethereum.org/en/whitepaper/

In total, the knowledge graph consists of 746 triples; Table 3 lists the
number of entities for the core classes.

Table 3: Number of triples and entities in the knowledge graph. Triples | Standard | Std.Orga. | DLTComponent | UseCase | BusinessSector | Vuln. | Attack
---|---|---|---|---|---|---|---
746 | 18 | 8 | 9 | 55 | 9 | 7 | 11

###  4.2 Availability of the Resources

DLT Ontology and Knowledge Graph. The knowledge graph and respective
documentation are available online at https://w3id.org/DLTOntology under the
CC-by-4.0 license; a DOI is provided via the Zenodo
repository.888https://doi.org/10.5281/zenodo.6497619 The online documentation
consists of a comprehensive overview, examples of how to use the ontology, and
SPARQL queries for the respective competency questions.

SPARQL Interface. We provide a SPARQL query interface at
https://w3id.org/DLTOntology which allows to execute the competency questions
provided in Section 3.3. The interface is based on the Comunica SPARQL Widget
[28].

##  5 Related Work

#### Glossaries and Vocabularies for Distributed Ledgers.

There have been made various attempts and proposals on the creation of
terminologies, glossaries and vocabularies regarding blockchains and
distributed ledgers. Most importantly, there is the ISO working group TC 307
[29], the ITU-T _Focus Group on Application of Distributed Ledger Technology_
[30], and the German Institute for Standardization (DIN) [31]. On top of that
there is a multitude of private companies and blockchain enthusiasts that
offer their own glossaries for blockchains and distributed ledgers like
Blockchainhub Berlin999https://blockchainhub.net/blockchain-glossary/, 101
Blockchains101010https://101blockchains.com/blockchain-definitions/, and
ConsenSys [32].

#### Ontologies for Distributed Ledgers.

EthOn (the Ethereum Ontology) [33] is an approach to model the major concepts
of the Ethereum blockchain, e.g., Blocks, Accounts, Transactions, etc. The
ontology itself is based on OWL [34] and there exist extensions that can be
used to describe ERC20 compliant
tokens111111https://github.com/ConsenSys/EthOn/tree/master/ERC20 and smart
contracts.121212https://github.com/ConsenSys/EthOn/tree/master/Contracts While
EthOn focuses on modelling the technical details of a particular blockchain,
we propose an approach that allows to model DLT systems, their specific use
cases, and their potential security threats, independent of the concrete
implementation.

Also related to our efforts is the BLONDiE ontology (Blockchain Ontology with
Dynamic Extensibility) [35]. BLONDiE provides classes and properties to
describe the structure and related information of the three most prominent
blockchain projects – Bitcoin, Ethereum and Hyperledger. However, while our
goal is to cover the overall ecosystem and landscape of existing DLT
technologies, BLONDiE describes concepts that specifically relate to the
implementation of the three technologies (e.g., “Ethereum Payload”, and
“Hyperledger Transaction”).

##  6 Conclusion

Although distributed ledger technology has seen an enormous increase in usage
and spread over the last decade, there are still many misconceptions and
misunderstandings revolving around it. First and foremost is the equalization
of distributed ledger technology with blockchains, where the latter is simply
one of many manifestations of the former. Furthermore, there is a lack of
holistic vocabularies and conceptualization of the technology as a whole,
rather than just one of its manifestations or even a single product. To tackle
and overcome present issues, we have contributed the following:

  * •

We developed an ontology for distributed ledgers. This ontology considers
various aspects of DLT systems, including threats, vulnerabilities, and the
legal situation and standardization of the technology; an overview of the
market situation and real-world applications is also included.

  * •

We have demonstrated the use of the ontology by building a knowledge graph
containing entities and relations for all the core classes of the ontology,
i.e. (i) standards and legal authorities, (ii) components and technical
details of DLT systems, as well as corresponding vulnerabilities and (iii)
exemplary use cases.

  * •

We have validated the ontology based on a pre-defined set of competency
questions. On top of that, a set of SPARQL queries is provided to evaluate the
competency questions and thus the capabilities of the knowledge graph.

  * •

The ontology and the knowledge graph are available for public use under an
open license; the documentation and downloads can be found at
https://w3id.org/DLTOntology.

One limitation of our work in this regard is the lack of existing data that
can be integrated in a knowledge graph: while it was possible to gather
information on the technical components of DLT systems, existing
standardization documents, and potential use cases in different industries,
this requires intensive research, extensive manual work, and expert knowledge
in the domain.

This research was funded by the Josef Ressel Center for Blockchain
Technologies & Security Management (BLOCKCHAINS). Sebastian Neumaier received
funding through the Austrian Research Promotion Agency (FFG) Bridge project
880592 “SecDM – Sichere und vertrauens-würdige on-premise data markets”. The
financial support by the FFG and the Christian Doppler Research Association is
gratefully acknowledged.

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