=Paper=
{{Paper
|id=Vol-2896/RELATED_2021_paper_4
|storemode=property
|title= Finding Implicit Links Between Norms Using HONto
|pdfUrl=https://ceur-ws.org/Vol-2896/RELATED_2021_paper_4.pdf
|volume=Vol-2896
|authors=Sabine Wehnert,Ernesto William De Luca
}}
== Finding Implicit Links Between Norms Using HONto==
https://ceur-ws.org/Vol-2896/RELATED_2021_paper_4.pdf
Finding Implicit Links Between Norms Using HONto
Sabine Wehnert1,2 , Ernesto William De Luca1,2
1
Georg Eckert Institute Leibniz Institute for International Textbook Research, Germany
2
Otto von Guericke University Magdeburg, Germany
Abstract
Linking legislation is an important task in knowledge modeling for the legal domain. This also involves
implicit links which are not apparent from mere content analysis of the respective norms. Current
systems mostly rely on ontologies or classifiers to enrich the norm content and thereby detect implicit
links. The drawback of this approach is that the required concepts and their relations may not already be
present in publicly available ontologies, requiring manual effort in conceptualizing and integrating new
knowledge. In contrast, we employ a general approach called HONto to automatically extract knowledge
about explicitly and implicitly related norms from textbooks. Having a textbook source, we can refer
to the individual text passage where we extracted the relation from and thus offer evidence for each
implicit link. Therefore, the HONto approach can assist knowledge engineers in finding implicit links
and automate a part of the engineering process. We intend to implement HONto in an information
retrieval scenario to detect relevant changes in law. With its in-built mechanism to incorporate implicit
relationships between norms during similarity scoring, HONto aims for providing high-recall results.
Keywords
implicit norm links, information extraction, bottom-up knowledge base
1. Introduction
Most systems in the field of Legal AI base their knowledge on the content of legal texts by
extracting relevant information regarding entities or concepts and integrating it into the re-
spective knowledge representation. In general, regulatory texts are not seen as stand-alone
documents, they have many relationships and dependencies among each other. Some of them
are mentioned explicitly in the text by referring to another article or document with a citation.
Other relationships - which we refer to by implicit links - are not directly encoded in the content
of the regulatory document. Those implicit links may not apply in all contexts, thus they are
only valid if a certain situation is given. In earlier work [1], we presented requirements for
linking legal documents, and one of them is particularly relevant for implicit links: topical
relevance. Hence, the conditions under which such a link is formed need to be also considered
during the knowledge modeling process. In rule-based systems, this may require a dedicated
classifier for the condition types and an intelligent mechanism to decide when topical relevance
is given.
In our work called HONto we focus on information extraction for a retrieval system to
determine norm changes which are relevant to a user. The user has to explore the knowledge
RELATED - Relations in the Legal Domain Workshop, in conjunction with ICAIL 2021, June 25, 2021, São Paulo, Brazil
Envelope-Open sabine.wehnert@gei.de (S. Wehnert); deluca@gei.de (E. W. De Luca)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
base and then mark relevant concepts or norms, for the system to determine the similarity to
upcoming (changes in) regulatory documents [2]. For all of this to happen though, the knowledge
base needs to be populated with legal documents, their relationships and the concepts related
to them. In this aspect, HONto can be distinguished from other related work: We extract
this domain knowledge automatically from textbooks [3]. This also includes implicit relations
between legal documents, which are the main subject of this work.
Hence, our contributions are:
• We show how our system forms links between legal texts.
• We discuss under which circumstances an implicit link shall be made.
The remainder of this work is organized as follows: Section 2 contains our concept for linking
legal documents based on topical relevance. Section 3 is about a preliminary experiment we
made on implicit link detection on the German Civil Code. In Section 4, we collect related
work for automated knowledge extraction in regulatory monitoring and for linking implicit
references to legal documents. With the final section we conclude this research and mention
future work.
2. Topical Relevance in HONto for Linking Laws
The HONto system is based on a knowledge base containing many concept hierarchies, where
each hierarchy is extracted from one textbook in the corpus. Those hierarchies need to be
connected, however we do not intend to perform any extensive manual effort in terms of
concept integration or merging. Instead, each hierarchy stays intact, having its own logical
structure from the author’s point of view. This means that HONto is based on the concept of
a lightweight ontology, a shallow meaning representation [4] from section titles, keywords
and legal document entity relations in the way that they are expressed in the text. The only
modification we make on those concept hierarchy structures is to add links between them.
Those links can be established between concepts or between legal text references made in the
textbooks which share the same context. Adding more links between those concepts can help
bring similar contexts closer in retrieval settings for query expansion. For instance, we can take
into account the newly formed graph structure from the linked concept hierarchies and use
its node encoding [5] for or in addition to other similarity computation to perform similarity
scoring on HONto.
We already introduced the concept of entity linking in a previous work [1], where we
resolved references to different types of legal texts: Norms, Court Decisions, EU-Directives,
EU-Regulations, and combinations of the aforementioned types. However, we did not make
the distinction between implicit and explicit references yet because until now, we did not use
the original content of the referenced legal texts. In this research, we showcase the linking
mechanism for explicit and implicit relationships between norms based on the knowledge we
can extract from textbooks.
Figure 1 illustrates our approach for explicit and implicit linking of the norms. In this example,
§ 823 BGB is the article of interest from the German Civil Code (BGB). We see three concept
hierarchies which reference this article. The references §§ 823, 253 II BGB and § 823 BGB are
Immaterial Damages Compensation by
the organizer for
Discrimination + Crime shortcomings
(e.g., insult) Discrimination during a holiday
§§ 823, 253 II BGB § 823 BGB §§ 823, 651n BGB
§ 823 BGB Node citing § 823 BGB Implicit Link
Concept
Other Node from the Hierarchy Link
§ 829 BGB Concept Hierarchy
Reference Link
Node for BGB Article Explicit Link
Figure 1: Linking explicitly and implicitly related norms.
both made in the same context of damages for discrimination, whereas the third reference
is about compensation for shortcomings during a holiday, thus not topically relevant in the
discrimination context. Therefore, we establish an implicit link between the first two references,
but not with the third one. There is a reference link between all three reference nodes and the
node for § 823 BGB. Considering the actual article text of § 823 BGB, there is no other article
referenced. Also the articles § 253 II BGB and § 651n BGB do not contain any citations of other
norms. Therefore, those article texts do not generate any explicit links and we can only form a
link between them via implicit links generated from the extracted textbook knowledge. While
searching in the remaining articles of the German Civil Code, we find another article (§ 829
BGB) that references § 823 BGB. Between those two articles, we can therefore establish an
explicit link.
To summarize, we only form implicit links among references if they share the same context
and only if no explicit link is present. Explicit links are made among the nodes of the legal texts
directly. In this way, explicit links are intentionally forming a shorter path between related
norms than than implicit links.
Our whole process of node linking is set up as a knowledge graph, where node distance is
determined by path length and node features. The node features are obtained by vectorizing
the strings we extracted from the textbook. The vectorization can be done with different
methods, such as a bag-of-words approach with TF-IDF scoring or embedding the sequence
with a BERT model [6]. The choice of the encoding model depends on the user’s individual
preference for explainability. Nowadays, there is often a trade-off between performance and
explainability (e.g., between TF-IDF scoring, topic models and BERT for the Statute Retrieval
Task of the Competition on Legal Information Extraction/Entailment [7]). Comparing those
different encoding methods in the context of legal norm linking is subject to future research.
In the following, we present a small experiment on our knowledge base to show the linking
process and the amount of implicit references in the German Civil Code that we can extract
from our textbook corpus.
3. Experiment
We choose the German Civil Code for this experiment because the citation pattern for its
articles is the most uniform one (compared to the other legal document types we extracted)
and therefore, we expect the most accurate results in terms of reference extraction and the
subsequent reporting of established links. Furthermore, we found earlier [1]1 that norms are
the most prevalent reference type in the type of textbooks we use, with 83,661 norm references
over our corpus of 193 textbooks. The similarity computation of the previous work among
those candidates has been performed with the Latent Dirichlet Allocation (LDA) topic model.
We note that the choice of LDA was based on its relatively good performance and explainability
on the textbook corpus, but this choice can be made differently depending on user preferences.
We analyze the same corpus also in this work. The implicit link candidates we consider in this
work are based on jointly cited legal texts, of which we have 122 instances. For the sake of
the following experiment we avoid any uncertainty stemming from links between multiple
references that are formed under similarity computation, instead we use norms that are already
linked together via a reference link and see how meaningful the link between the cited references
is. Of course, in a real-world setting there will be an error from the similarity computation for
implicit link creation.
We consider the knowledge gained from those jointly cited implicit references as meaningful,
if there is no ingoing or outgoing explicit citation from the affected norms towards each other.
Jointly cited norms usually apply to very specific contexts, so that in this case, we can treat the
involved articles as instances to be linked. The question we want to answer in this experiment
is: How many instances may receive explicit or implicit links?
Out of the 122 instances, there are 39 instances referring to the German Civil Code (BGB),
as shown in Table 1. Since this number of references is manageable, we manually checked
how many of the 39 combinations are obtainable from the original article texts (i.e., explicit
links), compared to how many of those relationships we could only get from the textbook
knowledge (i.e., implicit links). We consider two different search spaces: one called “BGB-only”
for combinations only within the BGB, and “External” with relations to articles in other laws
than the German Civil Code. For “BGB-only”, we have 13 references, for the “External” category
26 references. For the search direction, we can search for an explicit reference in either both
articles we compare (=“Bidirectional”), or in case of the “External” category, we also distinguish
between “Outbound” for explicit references from the BGB article to other laws and “Inbound”
for explicit references from other laws to the corresponding article in the BGB. The “Outbound”
and “Inbound” sets in the “External” category overlap in several parts, however we only find 11
explicit links if we search in both directions for each reference, otherwise we obtain fewer links.
In total, from 39 references, we are able to detect 15 of those references with the bidirectional
search method, such that those cases become explicit links in our system. The remaining 24
references are candidates for implicit links. We call those instances “candidates” because it
depends on the similarity scoring we employ to detect topical relevance whether an implicit
link between two references can be formed. Before forming implicit links, we need to perform
a user study and obtain relevance feedback for different scoring methods in the final HONto
1
Code for reference extraction: https://github.com/anybass/HONto/tree/master/reference_linking
Table 1
Amount of explicit and implicit relationships based on BGB combinations identified by HONto.
Search Space Search Direction References Explicit Links Implicit Link Candidate
BGB-only Bidirectional 13 4 9
External Outbound 26 9 17
External Inbound 26 7 19
External Bidirectional 26 11 15
Total Bidirectional 39 15 24
recommender system. In this work, we focus more on the nature of implicit links once they
have been formed, regardless of the scoring method. Characterizing the types of implicit links
may serve as a basis to justify future research on several scoring methods for detecting those
links.
Upon closer inspection, we find several, mutually not exclusive properties of the implicit link
candidates, which we refer to as follows:
• default link
• situational relationship
• implicit mention
The most obvious category of implicit links in our dataset are those with default links, which
are norms that are always cited together as a default setting. For instance, we find a reference to
§ 195 BGB in connection to § 199 I BGB, where the former denotes the duration of a limitation
period and the latter its beginning at the end of the year as a default setting. Exceptions in which
the start of the limitation period is determined differently, belong to situational relationships.
Default links are rather simple to find, either by spatial norm article proximity in the same part
of the law book, or they may be modeled in domain ontologies where they could constitute
a rule. Also in textbook sources, we can identify the standard combinations, since they occur
comparatively often.
Some situational relationships are particularly hard to obtain for a non-domain expert,
especially if there is no relevant term overlap or even paraphrases among the texts to be linked.
Such an example is the entitlement to continued employment as a claim of an employee who is
in dispute with his employer about the employment relationship, until the existence or non-
existence of the employment relationship has been clarified. The involved norms for this case do
not contain any mention about the entitlement itself; instead we find § 611a BGB (employment
contract), § 613 BGB (non-transferable duties of service) and § 242 BGB (performance in good
faith) cited. Our dataset also suggests a relationship between § 134 BGB and § 203 Abs. 1 StGB in
specific situations of a void practice purchase agreement due to a violation of private secrets (§
203 Abs. 1 StGB) for transferring patient data to the buyer. Consequently, the legal transaction
that violated a statutory prohibition is void (§ 134 BGB). Another example for a situational
relationship found in the textbooks is tort liability of a money courier § 823 Abs. 2 BGB (liability
in damages) in connection to § 261 StGB (money laundering). Those situational relationships
have in common that they only apply to a very narrow sense, hence those combinations may
be irrelevant, if retrieved without determining prior semantic similarity to a given user context.
On the other hand, the knowledge about those relationships is valuable when the user faces the
exact same situation, so that a retrieval system from the textbooks shall be able to return those
relationships in exactly these situations and offer proof of the relationship by pointing to the
textbook source.
Implicit mentions can consists of keyphrases or references to other norms. Keyphrases in that
regard are known by experts to be defined by a norm that is not always explicitly cited along
(such as the above example of “performance in good faith”). Implicit mentions may also be a
reference to another norm, which is not following the standard format or is just an approximate
pointer. For instance, the combination of § 75c I 2 HGB and § 343 I 1 BGB is intended for a
reduction of a disproportionately high penalty when acting against the non-competition clause.
§ 75c I 2 HGB contains an implicit mention of the other involved law, but instead of citing §
343 I 1 BGB explicitly, we find a subsumption of multiple articles under the section: “Where
the commercial employee has promised to be subject to a penalty in the event of failing to perform
the obligation undertaken in the agreement, the principal may assert claims only in accordance
with the provisions of section 340 of the Civil Code. The provisions of the Civil Code concerning
reduction of a disproportionately high contractual penalty shall remain unaffected.”. Such cases
require reference resolution (to detect “provisions of section 340”) and relevance scoring thereof
for the given situation to obtain the most relevant section in the given context (§ 343 I 1 BGB).
In view of the aforementioned properties we find in examples of implicit references, we
see potential in further research in this regard. Overall, we find in this experiment that our
textbook corpus offers domain knowledge about implicit links, which we cannot obtain from
the law texts directly. This finding exposes a general necessity of methods to find those implicit
links to improve performance in Legal AI systems, especially in our legal norm retrieval use
case. By using the HONto system, this knowledge is automatically extracted and inserted
into a knowledge base. Depending on the similarity threshold that is set, the HONto system
connects laws not only based on their explicit links, but also based on their context-dependent
relationships via implicit links between references to those laws.
4. Related Work
In this section, we first describe related research regarding automatic knowledge extraction for
finding compliance-relevant norm changes and second, we collect other methods for implicit
relationship extraction. The work by Schönhof et al. [8] presents an approach for automatically
extracting entity information from natural language text for automatic ontology creation. They
also apply their method on the German Civil Code, however, they do not use textbooks as
a knowledge source but other ontologies and wikidumps. Our HONto system differs from
their approach because our knowledge base explicitly models implicit links between norms.
HONto’s main use case is information retrieval and regulatory change recommendation based on
concept similarity from many not-integrated knowledge sources. Thereby, HONto circumvents
issues related to integration conflicts between individual concept hierarchies at the cost of
not providing any reasoning capabilities. Some examples for systems which offer reasoning
capabilities for compliance checking are CARiSMA [9], the works by Amantea [10], as well as
Palmirani and Governatori [11].
In their case study on EU legislation, Sulis et al. [12] extract implicit relationships between
recitals and articles using stemming and use word overlaps to assign a weight to the relationships
between both nodes in their graph. They also further categorize the implicit relationships, but
experiments with two human annotators show that there may be no easy agreement regarding
the category assignment. The advantage of the way we evaluated the implicit links is that we
base our analysis on evidence of such a relationship in terms of jointly cited norms in published
textbooks. On the other hand, our evaluation method may only apply for a subset of possible
implicit relationships. Our definition of topical relevance for implicit relationships fits most to
their category “Conceptually Similar”, although we do not only consider word or stem overlap,
but also semantic similarity (e.g., from contextual word embeddings). While deep learning
approaches and pre-trained models become increasingly available for the legal domain, Nanda
et al. [13] report from a similar use case that nowadays still combining TF-IDF scoring and
cosine similarity can achieve the best performance. Devyatkin et al. [14] employ deep learning
techniques for detecting implicit relations between fragments of legal documents and achieve
good results, but it is not clear if a BM25 or TF-IDF-based model with cosine similarity may
perform better on that dataset, as well. A further category for implicit links between norms are
also hierarchical dependencies, as mentioned by Opiła and Pełech-Pilichowski [15]. For future
experiments, we may check if this category also is covered at least partially by our textbook
knowledge, since this certainly depends on the books or commentary included in the corpus.
Therefore, we conclude the related work section by noting that there are systems which
are similar to HONto in their use case, while HONto’s extraction of domain knowledge from
textbooks and the knowledge modeling suited to implicit links has not been done before for
retrieving compliance-relevant changes in law.
5. Conclusion
This work focuses on the formation of links between implicitly related legal documents in
the HONto knowledge base. We condition the link formation on topical relevance which is
determined by the contextual similarity between a reference to legislation in textbooks. We find
that textbooks offer valuable knowledge about implicit relationships between norms, which
cannot be extracted from the norm content itself. In future research, we intend to enhance
our textbook corpus by further open knowledge from online commentary, Wikipedia and
ontologies. This work describes design decisions which are still part of an ongoing research
process for the HONto system. Therefore, a final evaluation of alternative methods to determine
topical relevance, as well as a user study to obtain multiple relevance assessments for HONto’s
recommendations is subject to future work.
Acknowledgments
This work is supported by Legal Horizon AG.
References
[1] S. Wehnert, G. C. Durand, G. Saake, ERST: leveraging topic features for context-aware
legal reference linking, in: M. Araszkiewicz, V. Rodríguez-Doncel (Eds.), Legal Knowl-
edge and Information Systems - JURIX 2019: The Thirty-second Annual Conference,
Madrid, Spain, December 11-13, 2019, volume 322 of Frontiers in Artificial Intelligence
and Applications, IOS Press, 2019, pp. 113–122. URL: https://doi.org/10.3233/FAIA190312.
doi:1 0 . 3 2 3 3 / F A I A 1 9 0 3 1 2 .
[2] S. Wehnert, W. Fenske, G. Saake, Context selection in a heterogeneous legal ontology, in:
H. Meyer, N. Ritter, A. Thor, D. Nicklas, A. Heuer, M. Klettke (Eds.), Datenbanksysteme
für Business, Technologie und Web (BTW 2019), 18. Fachtagung des GI-Fachbereichs
„Datenbanken und Informationssysteme” (DBIS), 4.-8. März 2019, Rostock, Germany, Work-
shopband, volume P-290 of LNI, Gesellschaft für Informatik, Bonn, 2019, pp. 129–134. URL:
https://doi.org/10.18420/btw2019-ws-13. doi:1 0 . 1 8 4 2 0 / b t w 2 0 1 9 - w s - 1 3 .
[3] S. Wehnert, D. Broneske, S. Langer, G. Saake, Concept hierarchy extraction from legal
literature, in: A. Cuzzocrea, F. Bonchi, D. Gunopulos (Eds.), Proceedings of the CIKM
2018 Workshops co-located with 27th ACM International Conference on Information and
Knowledge Management (CIKM 2018), Torino, Italy, October 22, 2018, volume 2482 of CEUR
Workshop Proceedings, CEUR-WS.org, 2018. URL: http://ceur-ws.org/Vol-2482/paper33.pdf.
[4] G. Ferraro, H. Lam, S. C. Tosatto, F. Olivieri, M. B. Islam, N. van Beest, G. Governa-
tori, Automatic extraction of legal norms: Evaluation of natural language process-
ing tools, in: M. Sakamoto, N. Okazaki, K. Mineshima, K. Satoh (Eds.), New Fron-
tiers in Artificial Intelligence - JSAI-isAI International Workshops, JURISIN, AI-Biz,
LENLS, Kansei-AI, Yokohama, Japan, November 10-12, 2019, Revised Selected Papers,
volume 12331 of Lecture Notes in Computer Science, Springer, 2019, pp. 64–81. URL:
https://doi.org/10.1007/978-3-030-58790-1_5. doi:1 0 . 1 0 0 7 / 9 7 8 - 3 - 0 3 0 - 5 8 7 9 0 - 1 \ _ 5 .
[5] A. Grover, J. Leskovec, Node2vec: Scalable feature learning for networks, in: Proceedings
of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining, KDD ’16, Association for Computing Machinery, New York, NY, USA, 2016, p.
855–864. URL: https://doi.org/10.1145/2939672.2939754. doi:1 0 . 1 1 4 5 / 2 9 3 9 6 7 2 . 2 9 3 9 7 5 4 .
[6] J. Devlin, M. Chang, K. Lee, K. Toutanova, BERT: pre-training of deep bidirectional trans-
formers for language understanding, in: J. Burstein, C. Doran, T. Solorio (Eds.), Proceedings
of the 2019 Conference of the North American Chapter of the Association for Computa-
tional Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN,
USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Association for Computational
Linguistics, 2019, pp. 4171–4186. URL: https://doi.org/10.18653/v1/n19-1423.
[7] J. Rabelo, M.-Y. Kim, R. Goebel, M. Yoshioka, Y. Kano, K. Satoh, Coliee 2020: Methods for
legal document retrieval and entailment, https://sites.ualberta.ca/~rabelo/COLIEE2021/
COLIEE2020_summary.pdf, 2020. Accessed: 2021-05-09.
[8] R. Schönhof, A. Tenschert, A. Cheptsov, Practical aspects of ontology-based analysis
and reasoning for law information represented in textual form, International Journal On
Advances in Intelligent Systems 8 (2015) 45–55.
[9] T. Humberg, C. Wessel, D. Poggenpohl, S. Wenzel, T. Ruhroth, J. Jürjens, Using on-
tologies to analyze compliance requirements of cloud-based processes, in: M. Helfert,
F. Desprez, D. Ferguson, F. Leymann (Eds.), Cloud Computing and Services Science -
Third International Conference, CLOSER 2013, Aachen, Germany, May 8-10, 2013, Re-
vised Selected Papers, volume 453 of Communications in Computer and Information
Science, Springer, 2013, pp. 36–51. URL: https://doi.org/10.1007/978-3-319-11561-0_3.
doi:1 0 . 1 0 0 7 / 9 7 8 - 3 - 3 1 9 - 1 1 5 6 1 - 0 \ _ 3 .
[10] M. Araszkiewicz, I. A. Amantea, S. Chakravarty, R. van Doesburg, M. Dymitruk, M. Garin,
L. Gilpin, D. Odekerken, S. S. Salehi, ICAIL doctoral consortium, montreal 2019, Artif.
Intell. Law 28 (2020) 267–280. URL: https://doi.org/10.1007/s10506-020-09267-z. doi:1 0 .
1007/s10506- 020- 09267- z.
[11] M. Palmirani, G. Governatori, Modelling legal knowledge for GDPR compliance checking,
in: M. Palmirani (Ed.), Legal Knowledge and Information Systems - JURIX 2018: The Thirty-
first Annual Conference, Groningen, The Netherlands, 12-14 December 2018, volume 313
of Frontiers in Artificial Intelligence and Applications, IOS Press, 2018, pp. 101–110. URL:
https://doi.org/10.3233/978-1-61499-935-5-101. doi:1 0 . 3 2 3 3 / 9 7 8 - 1 - 6 1 4 9 9 - 9 3 5 - 5 - 1 0 1 .
[12] E. Sulis, L. Humphreys, F. Vernero, I. A. Amantea, L. D. Caro, D. Audrito, S. Montaldo,
Exploring network analysis in a corpus-based approach to legal texts: A case study, in:
A. Tagarelli, E. Zumpano, A. K. Latific, A. Calì (Eds.), Proceedings of the First International
Workshop ”CAiSE for Legal Documents” (COUrT 2020) co-located with the 32nd Interna-
tional Conference on Advanced Information Systems Engineering (CAiSE 2020), Grenoble,
France, June 9, 2020, volume 2690 of CEUR Workshop Proceedings, CEUR-WS.org, 2020, pp.
27–38. URL: http://ceur-ws.org/Vol-2690/COUrT-paper3.pdf.
[13] R. Nanda, L. Humphreys, L. Grossio, K. J. Adebayo, Multilingual legal information retrieval
system for mapping recitals and normative provisions, in: V. Serena, J. Harasta, P. Kremen
(Eds.), Legal Knowledge and Information Systems - JURIX 2020: The Thirty-third Annual
Conference, Brno, Czech Republic, December 9-11, 2020, volume 334 of Frontiers in Artificial
Intelligence and Applications, IOS Press, 2020, pp. 123–132. URL: https://doi.org/10.3233/
FAIA200856. doi:1 0 . 3 2 3 3 / F A I A 2 0 0 8 5 6 .
[14] D. Devyatkin, A. Sofronova, V. Yadrintsev, Revealing implicit relations in russian legal
texts, in: S. O. Kuznetsov, A. I. Panov, K. S. Yakovlev (Eds.), Artificial Intelligence -
18th Russian Conference, RCAI 2020, Moscow, Russia, October 10-16, 2020, Proceedings,
volume 12412 of Lecture Notes in Computer Science, Springer, 2020, pp. 228–239. URL:
https://doi.org/10.1007/978-3-030-59535-7_16. doi:1 0 . 1 0 0 7 / 9 7 8 - 3 - 0 3 0 - 5 9 5 3 5 - 7 \ _ 1 6 .
[15] J. Opiła, T. Pełech-Pilichowski, Visual analysis of similarity and relationships between
legal texts, in: M. Koricic, K. Skala, Z. Car, M. Cicin-Sain, V. Sruk, D. Skvorc, S. Ribaric,
B. Jerbic, S. Gros, B. Vrdoljak, M. Mauher, E. Tijan, T. Katulic, P. Pale, T. G. Grbac, N. F. Fijan,
A. Boukalov, D. Cisic, V. Gradisnik (Eds.), 43rd International Convention on Information,
Communication and Electronic Technology, MIPRO 2020, Opatija, Croatia, September 28 -
October 2, 2020, IEEE, 2020, pp. 1482–1487. URL: https://doi.org/10.23919/MIPRO48935.
2020.9245296. doi:1 0 . 2 3 9 1 9 / M I P R O 4 8 9 3 5 . 2 0 2 0 . 9 2 4 5 2 9 6 .