=Paper=
{{Paper
|id=Vol-3020/KR4L_paper_3
|storemode=property
|title=Blockchains as Knowledge Graphs - Blockchains for Knowledge Graphs (Vision Paper)
|pdfUrl=https://ceur-ws.org/Vol-3020/KR4L_paper_3.pdf
|volume=Vol-3020
|authors=Luigi Bellomarini,Markus Nissl,Emanuel Sallinger
|dblpUrl=https://dblp.org/rec/conf/ecai/BellomariniNS20
}}
==Blockchains as Knowledge Graphs - Blockchains for Knowledge Graphs (Vision Paper)==
https://ceur-ws.org/Vol-3020/KR4L_paper_3.pdf
Blockchains as Knowledge Graphs – Blockchains
for Knowledge Graphs (Vision Paper)
Luigi Bellomarini1 , Giuseppe Galano1 , Markus Nissl2 , and Emanuel Sallinger2,3
1
Central Bank of Italy
2
TU Wien
3
University of Oxford
Abstract. A body of recent work introduced the modelling of blockchain
data as graph-based structures. Nevertheless, advanced tools for process-
ing such data are mostly developed on top of the graph structure and are
tailored to a specific analytical task, while the use of knowledge graph
management systems that provide state-of-the-art reasoning algorithms
is still in its infancy. In this paper, we discuss our vision for the FinTech
field on the connection of the blockchain and knowledge graph domain,
and provide various possible research topics by discussing, among oth-
ers, the challenges in the field of blockchain analytics and the generation
of legally compliant, unmodifiable and verifiable RegTech applications
running on blockchain infrastructure by using knowledge graphs.
Keywords: Blockchain · Knowledge Graphs.
1 Introduction
Knowledge Graphs (KGs) have become a major topic in AI, in academic research
and industrial applications. In the FinTech space, KGs are employed for many
purposes, including advanced reasoning services to gain insight from that data.
In central bank settings, KGs are currently used for manifold settings such as
checking regulatory compliance, anti-money laundering, or hybrid data science
pipelines that combine a multitude of AI approaches [5]. Recent developments,
e.g., on the regulation of cryptocurrency at the EU level [17], emphasize the need
to offer such services also over blockchains.
A knowledge graph can be described as a semi-structured data model
characterized by three components: (i) a ground extensional component hav-
ing relational constructs for schema and data, e.g., a graph-like structure, (ii)
an intensional component of inference rules over the constructs, and (iii) a de-
rived extensional component produced by activating the inference rules over the
ground extensional component in a so-called reasoning process [5].
Recent work has suggested solutions to various reasoning and data extraction
tasks, such as entity resolution [11] for solving the question of whether two nodes
refer to the same entity, link prediction [29] for predicting edges in the graph,
Copyright c 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
2 L. Bellomarini et al.
knowledge fusion [13] for predicting whether a node-edge-node triple is true, and
computation of KG embeddings [31] with advanced deep learning algorithms.
Many researchers [19, 26] in the area of blockchain analytics have recog-
nized that blockchains share common features with graph-like data structures
and started implementing algorithms on top of the graph structure, such as
blockchain identity clustering [28], financial fraud detection [22], price predic-
tion [1] or ransomware payment tracking [21].
All of these tasks are crucial, yet they are typically considered in isola-
tion. At the same time, what these tasks have in common is the goal of inferring
new nodes and edges of a graph. That is, they suggest a precise mapping of
the blockchain transaction graph into the KG extensional component and the
newly generated nodes and edges into the intensional component. Such a KG-
oriented view allows to see the mentioned blockchain tasks as reasoning, enabling
the exploitation of knowledge shared among tasks and domain experience. For
example, share reasoning tasks, such as the clustering of blockchain identities
which is comparable to entity resolution, and shared inference rules such as op-
erationalized domain knowledge, e.g., representing known money laundering or
fraud schemes, or blockchain contained knowledge representing the functionality
of smart contracts, e.g., rules for a lottery application.
With the option to model smart contracts as KGs, we also see the potential
to generate smart contracts via KGs. This creates a bidirectional connection,
allowing to monitor blockchain activity at the KG and creating legally compliant,
verifiable RegTech applications for the blockchain by using the operationalized
domain knowledge in the KG. I.e., in one direction the KG is used to create
parts of the blockchain, and in the other direction the blockchain is used to
create parts of the KG. RegTech stands for Regulatory Technology and describes
the usage of information technology for enhancing the regulatory process, with
its main application in the financial space. We suggest extending KGs with
blockchain technology, such as verification, to be able to generate trustworthy
smart contracts.
In this paper, we present our vi-
sion on the connection of blockchains Smart Contract Generation (Section 4)
and KGs. Figure 1 shows the overall
Knowledge Graph
vision presented here, and the sections Enhancement
KG BC
of the paper we will discuss the parts (Section 5)
in. Our ultimate goal is analyzing and
Blockchain Analytics (Section 3)
monitoring blockchain data as well en-
abling the construction of RegTech
applications for blockchain platforms, Fig. 1. Overview of our vision.
exploiting the capabilities of KG sys-
tems. In particular, our main contri-
butions are:
– A novel view on blockchain analytics based on KGs by defining analytics
as the derived extensional component of a blockchain KG, i.e., produced as a
Blockchains as Knowledge Graphs – Blockchains for Knowledge Graphs 3
result of reasoning. This will provide fully explainable analytics, allowing for
deeper insights into the complex relations among the involved transactions.
– A new way of smart contract generation by using data and inference
rules stored in KGs. This will result in legally compliant, verifiable smart
contracts perfectly fitting for RegTech applications.
– An extension of KGs with blockchain technology by integrating digital
signatures and consensus finding mechanisms. This will provide a form of
“explainable trust” in KGs, a key feature for FinTech AI.
The remainder of this paper is organized as follows: In Section 2 we give
background information on blockchains. In Section 3 we present our first vision
of using KGs as data structure to reason over blockchain data, in Section 4 we
discuss our vision of generating smart contracts by using KGs, and in Section 5
we present our vision to enhance KGs by blockchain technology. We provide
additional related work in Section 6 and conclude this paper in Section 7.
2 Background
A blockchain is a distributed ledger, where the blocks create a single-linked list
(“chain”) by a hash reference from a block to the previous one. Each block
contains a list of transactions. A transaction defines the information required
for transferring data and coins between different accounts that are normally
created by a private and public key pair, where the private key is used to spent
the coins and (the hash of) the public key to receive the coins, thus also named
the address of the account.
Depending on the blockchain state
model, the transactions are han- Block Transaction (Un)lock-Script Address Tag
dled differently. Bitcoin uses the
model of Unspent Transaction Out-
put (UTXO), where the outputs are
used as inputs for the next transac-
tions, which creates an acyclic graph
of transactions. In detail, the outputs
and inputs are scripts, where the in-
put unlocks the lock script of the Fig. 2. Graph view on bitcoin structure.
output, offering support for more ad-
vanced operations such as requiring multiple parties to unlock the output.
In comparison, Ethereum simplifies the transaction management by decou-
pling the data from the transaction in a separate state per account, which is
modified during a transaction. Scripts are handled within smart contracts. A
smart contract is, intuitively speaking, a type of account that has to be actively
invoked by other accounts. Smart contracts can contain and execute arbitrary
code. For a detailed introduction into blockchains see [16].
Figure 2 gives a visualization of the Bitcoin structure as a graph, summarizing
the discussed concepts. White square represents blocks, dark squares stand for
transactions, white circles are inputs and outputs, and dark circles represent
4 L. Bellomarini et al.
addresses. In addition, the figure contains tags of addresses, which is an external
information required for blockchain analytics, described in Section 3.
Note that Figure 2 represents just one possible graph-based modeling. Others
are possible and may be even favorable in certain reasoning tasks. Note that while
in this section we focused on the graph aspects, the knowledge graph aspects
discussed later will precisely allow translations between different graph-based
models.
3 Knowledge Graph Reasoning for Blockchain Analysis
Throughout the last years, blockchain analysis has been a focus of research in a
number of fields related to the financial, security, and societal domains [2]. Yet,
there are challenges which highlight the necessity to develop new approaches and
new reasoning techniques for blockchain data, which we lay out in the following.
Motivation. Most challenges arise in the context of large data volumes, with
the consequent need for high scalability.
Variability in fundamental operation. Blockchains provide different ledger, block
and transaction structures, smart contract languages (cf. [16, 6]) and privacy en-
hancing features [3]. While differences between data structures can be addressed
using traditional data integration techniques, knowledge of the rules that govern
the inner features of blockchains is essential for reasoning over the data.
Multi-layered blockchain data. Blockchains present a rich, multi-layered dataset
consisting of the transaction graph at the uppermost level but going down to
information present – sometimes in compiled form – in smart contracts.
Specific domain knowledge. Analytics queries typically need to reason on domain
data and knowledge in addition to data contained in the blockchain. For example,
tracking revenues of illicit activity [15] requires specific knowledge of laundering
patterns. Moreover, information from the outside, such as tags [24], are required
to link some sort of real-world entity with blockchain identities.
Uncertainty about available information. Probabilistic reasoning is required, for
example, to assess the probability of a link between inputs and outputs of Bit-
coin transactions or to apply heuristics that cluster addresses which are likely
“controlled” by the same entity.
Solutions. A promising solution are KGs, which are designed for complex data
and knowledge integration tasks as well as reasoning tasks. KGs naturally allow
to represent differences in fundamental concepts using knowledge that represents
the differences in operation, thus not requiring hard-coding such knowledge into
reasoning algorithms. By building a unitary network of transactions involving
multiple assets and rules, the data of smart contracts can be encoded in a trans-
parent way for the reasoning process.
Having a system capable of dealing with the challenges mentioned, allows to
use enriched blockchain data to get a deep understanding of the cryptoassets
phenomenon [10], including the estimation of the daily transferred value or the
tracking of illicit activities to identifiable points such as exchanges.
Blockchains as Knowledge Graphs – Blockchains for Knowledge Graphs 5
Looking ahead. Finally, it is worth noting that, although KGs enrich the in-
formation context of blockchains and enable sophisticated reasoning tasks, tech-
nology alone cannot overcome intrinsic limitations of blockchain analysis, which
depend on the potential lack of the required information. For example, off-chain
transactions, where only the final position is settled on the blockchain and mix-
ing techniques, where the transaction target is hidden, increase the privacy of the
user, but affects the analyses of the blockchain data. However, KGs can help to
reduce the intrinsic limitations, for example by integrating different data sources
in the reasoning process.
4 Knowledge Graph Generated Smart Contracts
Smart contracts are used to specify human-readable contracts and associated
obligations by code. While smart contracts are written nowadays mostly in
object-oriented style, a mismatch has been detected between the style and the
intended purpose of enforcing conditions in a contract while the environment
(blockchain) changes [12].
Motivation. Recent work identified numerous challenges to correctly codify
smart contracts such as unmodifiability or invulnerability [18]. They suggested
to use different smart contract formats based on Prolog [12] to close the mis-
match mentioned above as well as to generate smart contracts via a grammar of
institution [18] to protect against insecure smart contracts. While both are good
approaches to simplify smart contract creation, they both have some limita-
tions. The former requires writing logical programs not compatible with current
blockchains, while the latter requires to structure sentences into logical parts.
Solutions. We suggest generating smart contracts based on the knowledge stored
in the KG. We see three major motivations for doing so: (i) KGs already provide
inference rules and a knowledge base, (ii) established blockchain platforms are
supported, and (iii) external domain knowledge can be integrated.
We demonstrate the benefits through an example where we use the bidi-
rectional communication to generate trustworthy initial coin offerings (ICOs)
for funding new projects. ICOs have been suspect of exit scams over the last
years [25]. For simplicity, let us assume that ICOs have to follow legal regulation
to be considered as safe and there is a publicly certificated KG provider that en-
sures the validity of the ICO. By using KGs, we are able to integrate domain
knowledge such as legal text or news announcements to check the validity of
the provided ICO information. In case of a valid ICO, the KG provider gen-
erates the smart contract and publishes it with its own signature (trusted).
Since RegTech applications are exposed to frequently changing rules, the KG
provider has the possibility to create a bidirectional communication chan-
nel by monitoring the smart contract and updating the rules according to legal
changes. For example, assume that the KG provider monitors the composition
of the team stored in the smart contract. When the KG gets updated with a
new team composition stemming, e.g. from trusted news sources, this updated
knowledge is inspected. If is determined that a member has left the team and
6 L. Bellomarini et al.
that this change is not included in the smart contract, the KG provider updates
the rule of the smart contract.
We want to note, that this example uses exhaustive rights of the KG provider,
which have to be prohibited. We refer to Section 5, where we present our vision
of verified KG events.
Looking ahead. To realize this vision, a number of concrete challenges need to
be solved. We mention a few of them:
Interactions with smart contracts. Today’s infrastructure of major blockchain
technologies are not capable to update the code of smart contracts, thus requiring
storing changeable rules in the storage of the contract and use them in the
reasoning process of smart contracts.
Generation of smart contracts. Blockchains have diverse languages. Therefore, a
modular algorithm for generating smart contracts has to be defined that includes
a mapping between KG content and imperative smart contract languages.
Verification and integration of heterogeneous domain knowledge. The KG uses
various heterogeneous data sources such as legal texts, user inputs, or web data.
Algorithms have to be developed to intelligently verify the claims.
5 Enhancing Knowledge Graphs by Blockchain
Technology
In this section we briefly present our vision to enhance KGs by fundamental
concepts of blockchains, namely (i) consensus protocols which define the rules
for block generation including conflict management as well as the rules of a
valid block and transaction, (ii) digital signatures to sign transactions so that
the block creators can verify that the transactions are executed on behalf of the
owning parties, and (iii) an unmodifiable data structure by using hash algorithms
to prevent changes of historical data.
Motivation. The application of such concepts to the KG allows to improve
the reasoning algorithms by enriching the KG with trustworthy and historical
knowledge to produce more reliable results. This requires an adaption of state-
of-the-art reasoning algorithms to include the trustworthy aspect as well as a
sounded analysis of the different integration possibilities of such concepts in the
KG. For example, an unmodifiable data structure on the node-layer allows for
fast history scans per node but signing the validity of a connected component
requires at least the applicability on a cluster of nodes and edges.
Solutions. Having a KG capable of these concepts would improve the quality
of KGs. Such a system can help to solve the long term evolution of real-time
KGs, which is still an open problem [7], by integrating the unmodifiable and
accessible history concept and can provide verified knowledge graphs by in-
tegrating the concept of digital signatures, which would allow to contain verified
events in the KG, building a trustful connection between KGs and blockchains,
solving, among others, the exhaustive rights problem of the KG provider men-
tioned in Section 4.
Blockchains as Knowledge Graphs – Blockchains for Knowledge Graphs 7
Looking ahead. Apart from direct improvements to KGs, there are also other
disciplines which may profit from integrated blockchain technology. For example,
many AI researchers are currently working on explainable AI systems. This
means, they try to build intelligent systems that are able to answer questions con-
cerning how and why automatic decisions were made in a human-comprehensible
way. One way to build explainable AI systems is by using KGs [20]. We think
that a verified and trusted state provided by blockchain technology in KGs may
help explainable AI systems to decide and argue why they have made a specific
decision. One aspect of this is the full, verifiable history of all facts established
through a blockchain. For example, if a historic decision should be explained,
it is very easy to go back in time via the blockchain and thus give fully verifi-
able explanations of AI decisions. Another aspect is the that blockchains allow
a second level of explanation: For example, assume that a decision based on a
KG is explained via a number of facts. If these facts are actually established via
blockchain processes, e.g. voting, the explanation actually does not end here, but
can be continued by explaining how that fact was established via a blockchain
processes.
6 Related Work
The discussion of related work regarding blockchain and KGs is very limited. In
the following, we discuss related work we see related to this topic and which has
not been mentioned in the previous sections.
Exchange of Assets. We first focus on the blockchain as a mechanism for ex-
changing assets. GraphChain [27] uses the blockchain to store serialized versions
of an RDF (Resource Description Format)-graph. Similarly, Naim and Klas [23]
suggest to embed an RDF-graph in the blockchain. GraphOs [8] announced a
network for exchanging knowledge assets, such as data, code, or asset ownership.
Crowdsourcing. Systems are proposed where blockchains are used for verify KG
knowledge established via crowdsourcing. For example, Wang et al. [32] suggests
using crowdsourcing on the blockchain platform to update the KG and the AI
system with a “trustful” value.
Analytics. We now move from surveying blockchains for KGs to using KG tech-
nology for blockchain analytics. Bartoletti et al. [4] created a general framework
for blockchain analytics focusing on common tasks used in recent work to gen-
erate a shared view between them. This is an important first step, yet, this is
not enough to address the challenges we discussed in Section 3. Similarly, reg-
ular path query (RPQ)-based languages in general show insufficient expressive
power for our tasks as they do not support full recursion. Vo et al. [30] described
research perspectives for the database community for blockchains in the fields
of database management (creating indexes, data structures, generation of smart
contracts, etc.) and blockchain analytics (missing data, query federation, etc.).
In comparison, our vision on blockchain analytics focuses particularly on the in-
tersection with KGs. We provide novel perspectives and highlight tasks specific
to KGs.
8 L. Bellomarini et al.
Other Sources. Fluree [14] announced a semantic graph database with support of
blockchain functionality to allow history-based queries. Cagle [9] discussed that
KGs need blockchains to secure the keys, and that blockchain needs the KG to
provide a context and provenance for the keys. By keys they mean some sort of
public key to uniquely identify users and thus establish a chain of liability.
7 Conclusion
In this paper we have highlighted possible research directions at the intersection
of blockchain and KG research by showing a bidirectional connection between
these technologies. On the one hand, the monitoring aspect of the blockchain
and on the other hand the smart contract generation aspect by the usage of
KGs. In future work, we want to focus on these visionary topics in detail and
look forward to presenting first solutions in this domain.
Acknowledgements. The work on this paper was supported by the WWTF (Vienna
Science and Technology Fund) grant VRG18-013, the EPSRC grant EP/M025268/1,
and the EU Horizon 2020 grant 809965.
References
1. Akcora, C.G., Dey, A.K., Gel, Y.R., Kantarcioglu, M.: Forecasting bitcoin price
with graph chainlets. In: PAKDD (2018)
2. Akcora, C.G., Kantarcioglu, M., Gel, Y.R.: Blockchain data analytics. In: ICDM
(2018)
3. Alonso, K.: Zero to monero: First edition. https://www.getmonero.org/library/
Zero-to-Monero-1-0-0.pdf (2018), [Online; accessed 2019-02-24]
4. Bartoletti, M., Lande, S., Pompianu, L., Bracciali, A.: A general framework for
blockchain analytics. In: SERIAL@Middleware (2017)
5. Bellomarini, L., Fakhoury, D., Gottlob, G., Sallinger, E.: Knowledge graphs and
enterprise AI: the promise of an enabling technology. In: ICDE (2019)
6. block.one: Eos.io technical white paper v2. https://github.com/EOSIO/
Documentation/blob/master/TechnicalWhitePaper.md (2018), [Online; accessed
2019-03-23]
7. Bonatti, P.A., Decker, S., Polleres, A., Presutti, V.: Knowledge graphs: New direc-
tions for knowledge representation on the semantic web (dagstuhl seminar 18371).
Dagstuhl Reports (2018)
8. Butcher, M.: Graphpath plans to combine knowledge graphs with the
blockchain. https://techcrunch.com/2018/05/14/graphpath-plans-to-
combine-knowledge-graphs-with-the-blockchain/ (2018), [Online; accessed
2019-11-18]
9. Cagle, K.: The coming merger of blockchain and knowledge graphs. https:
//medium.com/@kurtcagle/685e052c614c (2019), [Online; accessed 2019-11-18]
10. Chimienti, M.T., Kochanska, U., Pinna, A.: Understanding the crypto-asset phe-
nomenon, its risks and measurement issues. https://www.ecb.europa.eu/pub/
pdf/ecbu/eb201905.en.pdf (2019), [Online; accessed 2020-01-07]
Blockchains as Knowledge Graphs – Blockchains for Knowledge Graphs 9
11. Christen, P.: Data Matching - Concepts and Techniques for Record Linkage, Entity
Resolution, and Duplicate Detection (2012)
12. Ciatto, G., Maffi, A., Mariani, S., Omicini, A.: Smart contracts are more than
objects: Pro-activeness on the blockchain. In: BLOCKCHAIN (2019)
13. Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann,
T., Sun, S., Zhang, W.: Knowledge vault: a web-scale approach to probabilistic
knowledge fusion. In: KDD (2014)
14. Doubleday, K.: Flureedb is production-ready and live. https://medium.com/
fluree/86bce82665dc (2018), [Online; accessed 2020-01-16]
15. ErgoBTC: Tracking the plustoken whale: Attempted bitcoin mixing and its impact
on wasabi wallet. https://medium.com/@ErgoBTC/787c0d240192 (2019), [Online;
accessed 2020-01-07]
16. Ethereum Foundation: A next-generation smart contract and decentralized appli-
cation platform. https://github.com/ethereum/wiki/wiki/White-Paper (2015),
[Online; accessed 2019-02-24]
17. European Parliament: Directive (eu) 2018/843. https://eur-lex.europa.eu/
eli/dir/2018/843/oj (2018), [Online; accessed 2020-01-12]
18. Frantz, C., Nowostawski, M.: From institutions to code: Towards automated gen-
eration of smart contracts. In: FAS*W@SASO/ICCAC. IEEE (2016)
19. Haslhofer, B., Karl, R., Filtz, E.: O bitcoin where art thou? insight into large-scale
transaction graphs. In: SEMANTiCS (2016)
20. Lecue, F.: On the role of knowledge graphs in explainable ai. Semantic Web Journal
(2019)
21. Liao, K., Zhao, Z., Doupé, A., Ahn, G.: Behind closed doors: measurement and
analysis of cryptolocker ransoms in bitcoin. In: eCrime (2016)
22. Möser, M., Böhme, R., Breuker, D.: Towards risk scoring of bitcoin transactions.
In: Financial Cryptography Workshops (2014)
23. Naim, B.A., Klas, W.: Knowledge graph-enhanced blockchains by integrating a
graph-data service-layer. In: 2019 Sixth International Conference on Internet of
Things: Systems, Management and Security (IOTSMS) (2019)
24. OXT: Bitcoin addresses annotations. https://oxt.me/notes (2019), [Online; ac-
cessed 2020-01-07]
25. Patel, D.: 6 red flags of an ico scam. https://techcrunch.com/2017/12/07/6-
red-flags-of-an-ico-scam/ (2017), [Online; accessed 2020-01-16]
26. Ron, D., Shamir, A.: Quantitative analysis of the full bitcoin transaction graph.
In: Financial Cryptography (2013)
27. Sopek, M., Gradzki, P., Kosowski, W., Kuziski, D., Trójczak, R., Trypuz, R.:
Graphchain: A distributed database with explicit semantics and chained rdf graphs.
In: Companion Proceedings of the The Web Conference 2018 (2018)
28. Spagnuolo, M., Maggi, F., Zanero, S.: Bitiodine: Extracting intelligence from the
bitcoin network. In: Financial Cryptography (2014)
29. Taskar, B., Wong, M.F., Abbeel, P., Koller, D.: Link prediction in relational data.
In: NIPS (2003)
30. Vo, H.T., Kundu, A., Mohania, M.K.: Research directions in blockchain data man-
agement and analytics. In: EDBT (2018)
31. Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: A survey of
approaches and applications. IEEE TKDE (2017)
32. Wang, S., Huang, C., Li, J., Yuan, Y., Wang, F.: Decentralized construction of
knowledge graphs for deep recommender systems based on blockchain-powered
smart contracts. IEEE Access (2019)