=Paper=
{{Paper
|id=Vol-1296/paper2
|storemode=property
|title=Legal Interpretations in LegalRuleML
|pdfUrl=https://ceur-ws.org/Vol-1296/paper2.pdf
|volume=Vol-1296
|dblpUrl=https://dblp.org/rec/conf/jurix/AthanGPPW14
}}
==Legal Interpretations in LegalRuleML==
https://ceur-ws.org/Vol-1296/paper2.pdf
Legal Interpretations in LegalRuleML
Tara Athan1 , Guido Governatori2 , Monica Palmirani3 ,
Adrian Paschke4 and Adam Wyner5
1 Athan Services
2 NICTA Queensland, Australia?
3 CIRSFID, University of Bologna, Italy
4 Corporate Semantic Web, Freie Universität Berlin, Germany
5 University of Aberdeen, United Kingdom
Abstract. Legislative documents are by their own nature subject to interpretation,
and interpretations of one document can diverge. In this paper we discuss the
mechanism proposed by LegalRuleML to capture alternative interpretations or
renderings of a legal source. LegalRuleML allows for mutually incompatible
renderings (or interpretations) of a legal source to coexist in the same LegalRuleML
document, and provides facilities to identify the interpretations and to select them.
The mechanism is illustrated with an example form Italian Jurisprudence.
1 Introduction
A common trait of legal reasoning and practice is that it is often possible to have multiple
interpretations of one and the same textual provision, where there is no true interpretation.
Such alternative interpretations might be mutually incompatible. This is often the case in
legal disputes where the parties involved put forward their interpretations and where the
judge has to select one of them or propose another interpretation.
In this paper we report on the efforts of the OASIS LegalRuleML Technical Commit-
tee to capture the phenomenon of multiple interpretations in the LegalRuleML standard
[21, 1]. The key intuition is that an interpretation is modelled by a set of LegalRuleML
statements (e.g., rules) and a norm or textual provision can be modelled by several
alternatives where each alternative has enough metadata to determine its context and
provenance. The paper outlines LegalRuleML components and illustrates them with
examples, e.g. a real life case from Italian Jurisprudence where the topic of discussion
of the case was on different interpretations of a textual provision.
The work on interpretation in LegalRuleML is set in the more general context of
interpretation in Linguistics in general and Forensic Linguistics in particular. In Linguis-
tics, issues about interpretation have long been of central concern (from [9] to [18]),
where the need for interpretation arises given that the meanings (broadly construed) of
“linguistic signs”, e.g. words, sentences, and discourses, can vary depending on partici-
pants, context, purpose, and other parameters. Interpretation is, then, giving the meaning
of the linguistic signs for a given set of parameters. While the relationship between signs
? NICTA is funded by the Australian Government through the Department of Communications
and the Australian Research Council through the ICT Centre of Excellence Program.
and meaning is arbitrary in principle [9], most contemporary linguistic research has
attempted to identify principles and constraints around interpretation in order to account
for a range of consistent, widespread, and observable linguistic patterns. After all, what
is truly arbitrary can only be catalogued and not made the object of deeper scientific
scrutiny. Research efforst have focussed on syntactic ambiguity, reference, vagueness,
semantic scope, and other phenomena. It is worth noting that high performance, statisti-
cally based, contemporary parsers (e.g. Stanford’s Dependency Parser [8]) or a parser
with associated semantic representation, (e.g. C&C/Boxer [7]) do not exclude alternative
parses or semantic representations.
In Forensic Linguistics, such considerations are applied to legal texts [22, 24, 4],
though with legal specific considerations. Laws prescribe behaviour, so knowing the
interpretation of a law in a context matters in terms of guiding conduct. Laws can be
challenged, thus opening presentation of alternative interpretations. Laws are composed
for social consumption, leading to issues bearing on who composed them, for what
purpose, to apply to what other parties, and so on. In addition, laws and circumstances
change over time, requiring active maintenance of interpretation. Finally, the practice of
law over time has developed its own catalogue of hermeneutical principles, a range of
techniques to interpret the law, such as catalogued and discussed in [23].
LegalRuleML endeavours not to account for how different interpretations arise, but
to provide a mechanism to record and represent them. The main novelty of this paper
is the presentation of a formal representation of legal interpretation. In Section 2 some
of the relevant background literature is reviewed. The running example is presented
in Section 3. The case study is formalised in LegalRuleML in Section 4. The paper
concludes with Section 5.
2 Related Work and OASIS LegalRuleML
Norms, guidelines and rules are found in a variety of legal texts. As text, the semantic
content (including meta-data) is difficult to exchange between parties or to otherwise
process or reuse by automated applications. Yet it is essential to eGoverment and
eCommerce services that such content has a machine-readable form such that appli-
cations can be deployed. The LegalRuleML TC, which was set up inside of OASIS
(www.oasis-open.org), aims to produce a rule interchange language for the legal do-
main. Using the representation, developers can provide applications to process legal
contents for data interchange, comparison, evaluation, and reasoning.
Over the last twenty years, the Artificial Intelligence (AI) and Law communities
have converged on modelling legal norms and guidelines using logic and other formal
techniques. With existing methods, a Legal Knowledge Engineer analyses the text,
scopes the analysis, extracts the norms and guidelines, applies models and a theory
within a logical framework, and finally represents the norms using a particular formalism.
In the last decade, several Legal XML standards have been proposed to describe legal
texts [19] with XML-based rules (RuleML, SWRL, RIF, LKIF, etc.) [13, 12]. At the
same time, the Semantic Web, in particular Legal Ontology research combined with
semantic norm extraction based on Natural Language Processing (NLP) [11], has given
a strong impetus to the modelling of legal concepts [5, 2, 6]. We discuss some of these
below.
Legal Knowledge Interchange Format (LKIF) is a specification that includes a
legal core ontology and a legal rule language that closely represents legal knowledge
and reasoning [10, 17, 12]. LKIF does not provide mechanisms to handle concurrent
interpretations of a legal source; more specifically, while it might be possible to represent
the content of the individual (alternative) interpretations, it is not possible to specify that
these representations are mutually exclusive.
RuleML is a family of languages, whose modular system of schemas for XML
permits high-precision web rule interchange (http://wiki.ruleml.org/index.php/
RuleML_Home). LegalRuleML is part of this family of languages. RuleML distinguishes
deliberation from reaction rules. Deliberation rules include modal and derivation rules,
e.g. facts, queries, and Horn rules. Reaction rules include Complex Event Processing,
Knowledge Representation, Event-Condition-Action, and Production. RuleML rules can
combine derivation and reaction rules, allowing uniform XML serialization across rules.
RIF (http://www.w3.org/2005/rules/wiki/RIF_Working_Group) is a W3C rec-
ommendation for a standard Web Rule Interchange Format to exchange rule sets among
different rule systems. It makes use of Internationalized Resource Identifiers and supports
XML Schema data types. The RIF architecture is conceived as a family of languages. A
RIF dialect is a rule-based language with an XML syntax and a well-defined semantics.
RIF does not provide direct support for adequate representation of legal rules and legal
reasoning since they do not support e.g. logic-based negation, non-monotonic reasoning,
events and temporal metadata, among other relevant features.
The Semantics of Business Vocabulary and Business Rules (SBVR) [20] provides
a controlled natural language [25] of fixed and finite vocabulary and syntactic form
for the expression of the terminology, facts, and rules for business documents across a
range of business activities and organisations. SBVR has an associated XML Metadata
Interchange (XMI), which supports the interchange of documents across businesses.
SBVR and LegalRuleML are closely related in that both provide XML encodings of the
semantics of terminology, facts, and rules. SBVR bears on business rules, which may
or may not have legal standing; LegalRuleML represents statements of legal standing.
LegalRuleML’s temporal notions of enforceability, efficacy, and applicability are not
provided in SBVR. LegalRuleML enables the expression of defeasibility, a rich range of
deontic concepts, and associated concepts of penalty and reparation.
Given this context, the LegalRuleML Technical Committee has focused on three
specific needs [21, 1]:
1. To close the gap between natural language text description and semantic norm
modelling.
2. To provide an expressive XML standard for modelling normative rules that satisfies
legal domain requirements [15, 16].
3. To extend the Linked Open Data [3] approach to modelling from raw data (acts,
contracts, court files, judgements, etc.) to legal concepts and rules along with their
functionality and usage.
The main novelty of LegalRuleML and the contribution of this paper is that it provides a
formal representation for alternative interpretations of a legal source (textual provision),
which is not found in other formal modelling languages.
3 Case Study
In this section we propose a real life case (taken from the Italian legal system and jurispru-
dence, originally discussed in [14]) depending on multiple (alternative) interpretation
of a norm, and we show possible formalisations of the case and the interpretations. In
the next section we are going to use the formal representations developed in this section
to illustrate the LegalRuleML mechanisms to cope with the phenomenon of multiple
interpretations. The case is based on a dispute of Art. 1, Comma 2, Law 379/1990. The
article recites
The benefit referred to in comma 1 shall be paid in an amount equal 80 per
cent of five-twelfths of the income earned and reported for tax purposes by the
freelancer in the second year preceding the year of application.1
The case 18/96, Bologna Tribunal, Imola Section, concerns the interpretation of the
conjunction in the income earned and reported for tax purposes. . . .
A fundamental and unalienable principle of legal language is its close connection
with natural language; in particular, the interpretation of a textual provision should be the
ordinary meaning conveyed by the text of the provision taking into account its context in
the act in which it appears and the purpose or object underlying the act. For example,
in the Italian legal systems this connection is prescribed by Article 12 of the Preleggi,
Italian Civil Code, stating
In applying a statute, the interpreter should not attribute to it a meaning differ-
ent from that made evident by the proper meaning of the words and by their
connection, as well as by the intention of the law maker.2
Accordingly, the literal interpretation of the norm is given by the rule
auxiliary=freelancer
f g
earned(x, y − 2) ∧ reported(x, y − 2) ⇒ OBLbearer=employer paybenefit( f (x), y) (1)
The arguments of the predicates earned and reported are the income x earned/reported
in the year in the second argument (y − 2). Similarly for paybenefit where the function f
encodes the computation of the value of the benefit based on the value of the income x.
However, according to the Italian taxation legislation in force at the time of the dispute
the income received in one year is reported for tax purpose the year after the year it has
1 L’indennità di cui al comma 1 viene corrisposta in misura pari all’80 per cento di cinque
dodicesimi del reddito percepito e denunciato ai fini fiscali dalla libera professionista nel
secondo anno precedente a quello della domanda.
2 Nell’applicare la legge non si può ad essa attribuire altro senso che quello fatto palese dal
significato proprio delle parole secondo la connessione di esse, e dalla intenzione del legislatore.
been earned. Thus, for example, the income earned in 1995 is reported in 1996. This
principle can be formulated as follows:
earned(x, y) → reported(x, y + 1) (2)
reported(x, y) → earned(x, y − 1) (3)
Consider now the Income constant obtained by applying the Russell’s definite description
operator (ι) on the conjunction in the left-hand side of (1).
Income = ιx(earned(x, y) ∧ reported(x, y)) (4)
The conclusion is that the constant Income is not denoting, i.e., the interpretation of
Income is ∅, thus there is no income “entity” that is earned and reported in one and the
same year. Hence, the left hand side of the rule in (1) never holds, and the rule never
fires, against the intentions of the legislator.
Based on the textual provision two possible interpretations are possible: in the
first interpretation the temporal expression “in the second year preceding the year of
application” refers to the income earned in the second year preceding the application,
while in the second interpretation it refers to the income reported for tax purposed
in the second year preceding the application. For example, for an application in year
1998, the first interpretation bases the computation on the income earned in 1996 (and
reported in 1997), while for the second interpretation, the value of the benefit is computed
starting from the income reported in 1996 (and earned in 1995). Accordingly, the first
interpretation, the interpretation proposed by the freelancer in the case, can be formalised
by the rule
auxiliary=freelancer
f g
earned(x, y − 2) ⇒ OBLbearer=employer paybenefit( f (x), y) (5)
Similarly the second interpretation, the interpretation proposed by the employer, can be
represented by the rule3
auxiliary=freelancer
f g
reported(x, y − 2) ⇒ OBLbearer=employer paybenefit( f (x), y) (6)
The task of the Judge was to decide which of the two interpretations has to be used for
the application of the norm. In the case the Judge argue in favour of the interpretation
advanced by the freelancer.
4 LegalRuleML Representation of the Case Study
In the previous section we presented three possible interpretations of the norm, the
literal interpretation, the interpretation of the freelancer and the interpretation of the
auxiliary=freelancer
3 Alternatively, we could use earned(x, y − 3) ⇒ OBLbearer=employer paybenefit( f (x)),
while, from a formal point of view, it is semantically equivalent to (6) it is less close in meaning
to the textual provision than its counterpart: the temporal reference in the argument would
“third year preceding the year of the application”.
employer. Here we are going to present the LegalRuleML fragments required to en-
code the formalisations corresponding to the three interpretations. The formalisations
of these three statements can be represented as prescriptive rules which are encoded
by blocks in LegalRuleML, each containing one
Template. The following fragment corresponds to the literal interpreta-
tion, i.e., (1)
income
year
2
income
year
2
Employer
Freelancer
income
year
Since LegalRuleML is built on top of RuleML we can reuse all RuleML facilities, in
particular we can use and to encode the computation of
the benefit to be paid to the freelancer.
The next snippet captures the interpretation of the freelancer, i.e., (5).
Notice that inside this statement we can use keyrefs to refer to the elements already
defined in the block corresponding to the literal interpretation. Similar considerations
apply to the block modelling (6), the employer’s interpretation, below.
The following LegalRuleML Constitutive Statement represents the principle expressed
in (2), that earned income will be reported in the following year. Because a Constitutive
Statement defines concepts and does not prescribe behaviours, the consequent of its
Template does not contain deontic operators.
income
year
income
year
1
Similarly, the following fragment represents the principle that reported income was
earned in the previous year, as expressed in (3).
income
year
income
year
1
After the renderings of the alternative interpretations and the relationships between the
predicates earned and reported given by the three constitutive rules, we have to specify
that they are mutually exclusive formalisation of the same norm. This can be achieved by
following Alternatives block that represents a mutually-exclusive collection of renderings
of the Legal Norms from the Legal Source #ls1. The with key
#ls1, not shown in the text, contains the references to the actual text of the norm.
These alternatives are mutually
incompatible formalizations of the same legal source: keyref="#ls1".
A block is used to render a collection of Associations, e.g. the Asso-
ciation of a Legal Source with a rendering of it as a LegalRuleML Statement, or to
constrain other Contexts with respect to Alternatives. The following Context establishes
a constraint that at most one of the Alternatives from the collection #maternity-alts
may be selected by each Context:
The Context metadata, e.g. authorship, source, authority, temporal and jurisdictional
properties, are specified in an external (to the Context) Association block with identifier
asn-alts, not shown in the paper, which is referenced using keyref. Similarly other
Context blocks (also not shown in the paper) are given with the metadata about the
authors of the various Statements. This permits to establish the provenance of the
interpretations.
In the following fragment, a particular Alternative – that proposed by the freelancer –
is selected, leading to the generation of the corresponding from the rule
Template :freelancer-template.
Unlike the first Context block, this one contains an element. Such
Contexts render interpretations that select one or more Statements as their scope of
interpretation. When a Context is processed for presentation or inference, Legal Rules4
are generated from the Templates of in-scope Statements, annotated and
optionally modified semantically by the Associations of the Context.
In this example the external Association asn-adjudication links the metadata
for the adjudication of the case with a particular rendering of the norm, the render-
ing freelancer, corresponding to the interpretation proposed by the freelancer and
confirmed by the judge5.
5 Conclusions and Future Work
In this paper, we presented the mechanisms for the representation of (mutually incompat-
ible) alternative interpretations of legal sources (textual provisions) in a LegalRuleML
document. Specifically, we introduced the element that is to be
used to specify alternative formal renderings of a legal source, where the alternatives in a
block are mutually exclusive. The key idea is that each rendering corresponds to an inter-
pretation of the legal source. Using along with
blocks, we can specify the different formal renderings of a legal source and their meta-
data that associates them with other elements in a LegalRuleML document, e.g. the
interpreter of the legal sources, the time of such interpretations, and the context where
4 In this paper, we focus on Prescriptive and Constitutive Statements, which always lead to gen-
erated Legal Rules. However, in the general case, e.g. , something
other than a Legal Rule may be generated when a Statement is in scope.
5 The full example is available from https://tools.oasis-open.org/version-control/
browse/wsvn/legalruleml/trunk/examples/approved/maternity_alternatives_
compact.lrml
such interpretations apply. Furthermore, we presented a real life case that illustrates
how to use to model the different interpretations of an ambigu-
ous legal source such as arise in a legal dispute. An important advantage of the use
of is that it reduces redundancies in encoding the formalisation
of a legal document. Different interpretations of a legal document can occur for many
reasons (e.g., different readings of one norm by the parties involved in a legal dispute,
different granularity of representation required by different applications, different in-
terpretations with respect different (sub-)jurisdictions, the change of interpretation of
terms over time, . . . ). The mechanism we have proposed does not force the author of
a LegalRuleML document to duplicate and modify the document just to accommodate
every different interpretation of a legal source. All the author has to do is to create
an block in a single LegalRuleML document, add the various
alternatives in the block, and refer to it from blocks that associate meta-
data with sets of statements. Then by filtering with respect to the metadata associated
with an alternative, one can generate the manifestation of the document correspond-
ing to alternative interpretations selected by the filtering conditions, employing the
expression to ensure that mutually incompatible alternatives are
not simultaneously asserted in the same context.
The LegalRuleML syntax for the metadata collections, e.g. Alternatives, Jurisdic-
tions, LegalSources, was designed to facilitate the exposure of these relationships as
Linked Open Data (LOD). The RDF Collection structure is particularly appropriate for
this, because it can be closed, indicating that the collection contains only the entities
explicitly asserted to belong to it. An XSLT transformation will be developed to convert
the LegalRuleML XML into RDF, while an RDFS metamodel will capture additional
constraints. We envision that the major benefits of the RDF representation of Legal-
RuleML are the possibility to integrate the legal knowledge with information stored
in other Open Data repositories and triple stores, and the ability to use tools such as
SPARQL reasoners for preprocessing LegalRuleML documents before passing data to
specialised legal reasoners.
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