=Paper=
{{Paper
|id=Vol-2400/paper-09
|storemode=property
|title=The CRIKE Data-Science Process for Legal Knowledge Extraction
|pdfUrl=https://ceur-ws.org/Vol-2400/paper-09.pdf
|volume=Vol-2400
|authors=Silvana Castano,Mattia Falduti,Alfio Ferrara,Stefano Montanelli
|dblpUrl=https://dblp.org/rec/conf/sebd/CastanoFFM19
}}
==The CRIKE Data-Science Process for Legal Knowledge Extraction==
https://ceur-ws.org/Vol-2400/paper-09.pdf
The CRIKE Data-Science Process for Legal
Knowledge Extraction
Discussion Paper
Silvana Castano1 , Mattia Falduti1 , Alfio Ferrara1 , and Stefano Montanelli1
Università degli Studi di Milano
DI - Via Celoria, 18 - 20135 Milano
{silvana.castano,mattia.falduti,alfio.ferrara,stefano.montanelli}@unimi.it
Abstract. In this paper, we present CRIKE, a data-science approach
to automatically detect concrete applications of legal abstract terms in
case-law decisions. To this purpose, CRIKE relies on the use of the LATO
ontology where legal abstract terms are properly formalized as concepts
and relations among concepts. Using LATO, CRIKE aims at discovering
how and where legal abstract terms are applied by judges in their legal
argumentation. Moreover, we detect the terminology used in the text of
case-law decisions to characterize concrete abstract-term instances.
Keywords: legal ontology, legal-term extraction, case-law analysis
1 Introduction
Law is general and abstract by definition. On the opposite, court case law deci-
sions are specific and concrete, in that they provide a peculiar interpretation of
law applied to the considered single cases. Legal interpreters, such as for example
judges and lawyers, are daily involved in analysis and evaluation of court case
law with the aim to extract/derive possible suggestions for incoming case appli-
cations by relying on the experience of past applications that can be considered
as a sort of consolidated legal knowledge.
According to the Italian law, the legal terminology can be distinguished into
three main categories, that are i) statutory terms, i.e., terms directly or indirectly
defined by law; examples of statutory terms are public officer, illicit drug, and
consumer; ii) descriptive terms, i.e., terms featuring actions, human activities,
and any real-life object; examples of descriptive terms are escape, car, and year;
iii) abstract terms, i.e., terms featuring something indeterminate that requires a
concrete application for being really defined; examples of abstract terms are good
faith, long-term cohabitation, and dangerous driving. Consider the abstract schema
of a legal action provided in Figure 1. When a new case law is received for
Copyright c 2019 for the individual papers by the papers authors. Copying permit-
ted for private and academic purposes. This volume is published and copyrighted by
its editors. SEBD 2019, June 16-19, 2019, Castiglione della Pescaia, Italy.
New case law
to judge
Courthouse
judge
Law
History of Case-Law Decisions
(CLDs)
Decision/Verdict
on new case law
(concrete law
interpretation)
Fig. 1. The abstract schema of a legal action
judgement, the expected evaluation process has to take into account i) the law,
for understanding the terms, either statutory, descriptive, or abstract, that can
be relevant for the current case, and ii) the history of case-law decisions, for
detecting possible relevant interpretations and concrete applications of abstract
terms that can be useful to support the decision/verdict to eventually deliver.
In this paper, we present CRIKE (CRIme Knowledge Extraction), a data-
science approach to detect concrete applications of legal abstract terms in large
case-law decisions. To this purpose, CRIKE relies on the use of LATO (Legal
Abstract Term Ontology) where legal terms are properly formalized as concepts
and relations among concepts. Using LATO, CRIKE aims at discovering how and
where legal abstract terms are applied by judges in their legal argumentation.
The paper is organized as follows. In Section 2, the CRIKE approach is in-
troduced. The LATO ontology and the CRIKE techniques for legal knowledge
extraction are discussed in Section 3 and 4, respectively. Related work are dis-
cussed in Section 5. Concluding remarks are provided in Section 6.
2 The CRIKE approach
The CRIKE approach (see Figure 2) is conceived to support extraction of legal
knowledge from a (possibly large) dataset of Case-Law Decisions (CLDs) com-
ing from different, official sources, such as for example First Grade and Court
of Appeal judgements. CRIKE embeds the LATO ontology where relevant law
concepts of a given domain of interest are properly formalized. To enforce knowl-
edge extraction, CRIKE exploits a given dataset of CLDs in input by adopting
a conventional data-science process where each CLD is indexed and stored in
a digital format. In particular, the CLDs of our dataset are acquired from the
Court and the Court of Appeal of Milan and they are usually provided in im-
age format with highly heterogeneous quality. The indexing and storage activity
exploits data cleaning and tokenization techniques to obtain a pure textual ver-
sion of each CLD as well as a focused set of metadata. By exploiting the indexed
CLDs metadata, knowledge extraction is enforced with the aim at classifying a
CLD with respect to the LATO ontology knowledge. In particular, extraction
expert-based indexing/storage of
ontology design CLDs
Law Dataset of
Case-Law Decisions
(CLDs)
legal
knowledge extraction
LATO ontology
(abstract term specification)
concrete applications
of abstract terms
Fig. 2. The CRIKE approach
is focused on detecting the concrete applications of legal abstract terms in the
text of the considered CLDs. The crucial idea of CRIKE is that the detection
of a given abstract term AT is not only concerned with the recognition of single
terms featuring AT , but also with the recognition of terms associated with the
ancillary concepts related to AT , that we call abstract-term context.
Motivating example. Consider the Italian law about drugs and related drug
offenses, as reported in [11]. According to the Italian criminal order, “the Con-
solidated Law, adopted by Presidential Decree No 309 on 9 October 1990 and
subsequently amended, provides the legal framework for trade, treatment and
prevention, and prohibition and punishment of illegal activities in the field of
drugs and psychoactive substances. Drug use in itself is not mentioned as an
offense. [...] The threshold between personal possession and trafficking is deter-
mined by the circumstances of the specific case (e.g., the act, possession of tools
for packaging, different types of drug possessed, number of doses in excess of av-
erage daily use, means of organization). The penalty for supply-related offenses,
such as production, sale, transport, distribution or acquisition, depends on the
type of drug. However, when the offenses are considered minor because of
the means, modalities or circumstances, the terms of imprisonment are lower.
Evaluating whether or not the offense is minor should take into account a set
of “ancillary” elements such as the mode of action, possible criminal motives,
quality and quantity of drug possessed, the character of the offender, conduct
during or subsequent to the offense, and the family and social conditions of the
offender”. The notion of minor offense is an example of abstract term in the
above law quotation. A precise definition of circumstances and related threshold
quantities to associate with the notion of minor offense is not available/possible
in the (abstract) law. Given a specific criminal charge of drug possession, the
final decision/verdict is based on the specific interpretation of the abstract term
“minor offense” where the specific circumstances and quantities of the considered
case represent a concrete application of the corresponding abstract term.
3 Legal knowledge representation
To formalize the knowledge related to abstract terms and their interpretation,
we introduce LATO in CRIKE. LATO is a legal ontology where relevant law
terms to exploit knowledge extraction in CLDs are defined; it contains concepts
to represent general law terms, either abstract, statutory and descriptive terms.
LATO is manually defined by domain experts and implemented according
to the SKOS formalism. In particular, the concept hierarchy is based on a root
concept Term with three main subconcepts, namely AbstractTerm, DescriptiveTerm,
and StatutoryTerm (see Figure 3(a)). In addition to general law terms, the LATO
ontology contains concepts that represent the Italian legislative structure, such
as for example the concepts Law, LawArticle, and LawParagraph. Furthermore, the
concepts Conviction and Discharge are also specified in LATO to represent the
possible Court decisions (i.e., the verdict) of a given case law. In particular, the
concept Conviction denotes a verdict in which the Court judges the defendant
guilty, while the concept Discharge denotes a verdict in which the facts have a
penalty relevance, but no punishment is finally delivered. Finally, the concepts
Quantity and UnitOfMeasure are defined in LATO for allowing to represent the
quantitative estimation of substances that can appear in legal documents.
AbstractTerm is the core concept of the LATO ontology since it represents the
target of the knowledge extraction functionalities of CRIKE. The related con-
struct of SKOS is exploited to enrich the specification of an abstract term AT
by formalizing the ontology relationships between AT and the other concepts
of the LATO ontology composing its context. In particular, given a considered
abstract term AT , related is used to connect AT to ancillary concepts of LATO
representing i) an objective judgment element OBJ usually expressed through
the connection of AT with a descriptive/statutory concept; ii) a subjective quan-
titative evaluation SU BJ usually expressed through a relationship between AT
and Quantity/UnitOfMeasure concepts; and iii) a legislative reference LREF usu-
ally denoted with a connection of AT with a specific law or regulation (i.e., Law,
LawArticle, and LawParagraph concepts). According to SKOS, each LATO con-
cept is associated with a preferred label (prefLabel) as well as with one or more
alternative labels (altLabel) and hidden labels (hiddenLabel) to enrich the concept
definition with a label-set of literal descriptions that is very useful for subsequent
knowledge extraction, to capture possible synonyms, acronyms, and abbrevia-
tions in the text of CLDs.
Example. An example of SKOS definition for the abstract term AT = MinorOf-
fense is shown in Figure 3(b) according to the Italian drug-trafficking law. Mino-
rOffense is related to the concepts Drug and DrugTraffickingVerb that represent the
OBJ relationships since they are subconcepts of StatutoryTerm and DescriptiveT-
erm, respectively. The relationships with the concepts Quantity and UnitOfMeasure
represent the subjective judge evaluations SU BJ. The concepts Par5, Art73, and
DPR309/1990 are subconcepts of the LawParagraph, LawArticle, and Law, respec-
Term Law
AbstractTerm DPR309/1990
MinorOffense PenalCode
DrugMinorOffense PrivacyCode
DescriptiveTerm Weapon
DrugTraffickingVerb LawArticle
StatutoryTerm Art648
LREF
Drug Art73 Art73 DPR309/1990 Par5
Cannabis LawParagraph
Cocaine Par3
Drug Quantity
Heroin Par5
MinorOffence
UnitOfMeasure Conviction Drug
Unit
Trafficking
Discharge Measure
Quantity Verbs OBJ SUBJ
(a) (b)
Fig. 3. (a) Excerpt of the LATO concept hierarchy; (b) Example of concept definition
for the abstract term MinorOffense
tively, and they express the legal references LREF of MinorOffense in the Italian
criminal code where the drug trafficking crime is defined.
4 Knowledge extraction in CRIKE
Knowledge extraction in CRIKE is based on the idea to exploit text analysis
techniques for detecting the concrete applications of legal abstract terms be-
longing to LATO throughout the stored/indexed case-law decisions CLDs. To
this end, for a given abstract term AT , we introduce the notion of abstract-term
context CtxAT containing, besides the AT term, all the concepts of LATO that
are ancillary to AT , namely OBJ, SU BJ, or LREF concepts:
CtxAT = {Ci | r(AT, Ci )}
where r(AT, Ci ) denotes a SKOS related relationship between the abstract term
AT and the concept Ci .
For each concept C ∈ CtxAT , we define the concept label set LC that contains
the whole set of labels, either preferred, alternative, or hidden, associated with
C. Furthermore, based on the notion of LC , we define the extended label set LC
where the concept label set of C is enriched by including the concept label set
of the concepts Cj subsumed by C:
LC = LC ∪ LCj | Cj ⊆ C
Consider the goal to detect the concrete applications of a certain abstract
term AT in a dataset of case-law decisions CLDs. CRIKE knowledge extrac-
tion is enforced by exploiting the extended label sets LC of the concepts in the
context CtxAT . For each document d ∈ CLDs, we define a vector representa-
tion d where each element corresponds to a concept in the context CtxAT . The
value d[i] ∈ d is set to 1 when a label hit is detected, meaning that at least one
occurrence of a label in LCi is found in d for the concept Ci ∈ CtxAT , and 0
otherwise (i.e., label miss). A threshold based mechanism is defined to specify the
minimum number of label hits required to consider that a concrete application
of the abstract term AT is detected in the document d.
DPR 309/90
kg. art. 73 L. 309/1990
kilogram article73 public safety
Kilo A. 73 Act
Deal Kilo par. 5
Quantity
deal DPR Par. five
amount Art73
To deal 309/1990 paragr. V
Weight
cocaine
Deal Crack
Quantity Unit crystal
Par5
OfMeasure Cocaine
Drug
Trafficking
verbs Drug
MinorOffense
The police officers, during a routine inspection, discovered four bags of white powder
onto the toilet floor (weighing 3.6 kg. in total). The powder tested positive for cocaine.
[…] the deal.
Both males were arrested. For instance, considered matters, mode of action, quality
and quantity of the substance, the penal relevance is clear, but the offence is minor.
For the foregoing reasons, the court applies the art. 73, DPR 309/90, par. 5 […].
Fig. 4. Example of knowledge extraction for the abstract term MinorOffense
Example. Consider the abstract term AT = MinorOffense and the correspond-
ing context CtxMinorOffense = {Drug, DrugT raf f ickingV erb, DP R309/1990,
Art73, P ar5, Quantity, U nitOf M easure}. Moreover, consider the extended la-
bel set LDrug = LDrug ∪ {LCocaine , LHeroin , LCannabis }. In Figure 4, we show
an example of knowledge extraction based on the concepts and corresponding
extended label sets in the context CtxMinorOffense . An example of vector-based
document representation for the abstract term AT = MinorOffense is shown in
Figure 5. If we consider a threshold of 80% of label hits, we have that a concrete
Drug DrugTraffickingVerb DPR309/90 Art73 Par5 Quantity UnitOfMeasure
d1 1 0 1 1 1 1 1
d2 1 1 1 0 0 1 0
Fig. 5. Example of label hits for the abstract term MinorOffense
application of MinorOffense is detected in document d1 since 6 hits are found over
the available 7 concepts in CtxMinorOffense .
5 Related work
Work related to the issues addressed in CRIKE regards legal argumentation
mining and legal ontology design. Legal argumentation mining refers to the ca-
pability to automatically detect and classify the role of possible argumentative
units within a considered legal case text [1]. In [10], authors propose to mine
statutory texts by using natural language processing and supervised machine
learning techniques. More recently, the LUIMA approach has been proposed to
focus on extraction of evidential reasoning from a court decision dataset [5].
Moreover, a particularly relevant contribution is provided in [9] about extrac-
tion of case law sentences for argumentation of statutory terms.
A survey on legal ontology design is presented in [1], where a special focus
is given to representation of legal concepts in type systems. In [4], the notion
of mutual consensus is introduced to support the specification of concepts and
relations about contract formation. An application example based on a corpus
of Italian legal texts is presented in [7], where the results of exploiting a learning
system are provided. A further specification of a legal ontology using ONTOLIN-
GUA is presented in [13]. Furthermore, in [12], authors present the LOIS project
(Lexical Ontologies for Legal Information Sharing), and discuss a methodology
for building a multilingual semantic lexicon for law able to be used both as a
source of semantic metadata and as an external tool for cross lingual retrieval.
On that topic, in [8], a methodology to automatically create an OWL ontology
from a set of legal documents is presented. In [3], an automated approach based
on statistical analysis is described, for identification of core concepts and rela-
tions in a corpus of legal texts. Natural Language Processing (NLP) techniques
are proposed in [6], to extract concepts and relations among legal concepts, with
the aim to build an ontology for legal information retrieval.
Original contribution of the proposed CRIKE approach is related to the
enforcement of a data-science process with the support of an expert-based law
ontology to extract knowledge from CLDs. A further peculiar feature of CRIKE
is related to the formalization of an abstract term as a legal ontology con-
cept with a corresponding context of related concepts. Ontology concepts with
associated contexts are used to drive the identification of concrete applica-
tions/interpretations of corresponding abstract terms in the text of CLDs.
6 CRIKE support to practices and concluding remarks
In this paper, we presented the CRIKE approach for legal knowledge extrac-
tion. We envisage the following main practices that can be supported by using
CRIKE i) knowledge-assisted verdict writing, where the concrete termi-
nology extracted for abstract terms can support the judge in the preparation
of new case-law decisions; ii) history-based verdict prediction, where the
knowledge extracted by CRIKE is used to train a machine learning mechanism
with the aim to predict the possible decision on a new incoming case-law to
judge; and iii) legal analytics, where the results of knowledge extraction are
exploited to detect possible trends and common abstract-term interpretations.
A preliminary experimentation of CRIKE has been performed based on a
dataset provided by the Courthouse of Milan, Italy, whose results are described
in [2]. The goal of the experimentation was to analyze the effectiveness of CRIKE
in recognizing the concrete applications of the abstract term MinorOffense.
Different research directions are currently being investigated. On the one side,
we are working on a bootstrapping approach to enforce enrichment of the LATO
ontology, so that the context of abstract terms can be progressively augmented
with new relevant terms and literals as long as they are detected in CLDs during
extraction. On the other side, machine learning techniques are being developed to
enforce a supervised classification of CLDs based on abstract terms, by exploiting
a training set of CLDs manually annotated by domain experts.
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