=Paper=
{{Paper
|id=Vol-1105/paper6
|storemode=property
|title=Modeling Legal Documents as Typed Linked Data for Relational Querying
|pdfUrl=https://ceur-ws.org/Vol-1105/paper6.pdf
|volume=Vol-1105
|dblpUrl=https://dblp.org/rec/conf/jurix/Mimouni13
}}
==Modeling Legal Documents as Typed Linked Data for Relational Querying
==
https://ceur-ws.org/Vol-1105/paper6.pdf
Modeling Legal Documents as Typed Linked
Data for Relational Querying
Nada Mimouni
LIPN, Paris 13 University – Sorbonne Paris Cité & CNRS (UMR 7030)
F-93430, Villetaneuse, France
nada.mimouni@lipn.univ-paris13.fr
Abstract. Access to legal knowledge is particularly challenging to infor-
mation retrieval systems. Not only is legal knowledge usually expressed in
linguistically complex forms, but it is also structurally sophisticated (e.g
pieces of legislation applicable to a case, version in force of a legal doc-
ument, other related sources). Modeling the collection of documents in
such complex domains requires taking into account the semantic content
of the documents as well as their relational structure since documents
are usually related to each other by various types of links. In this paper
we describe two approaches for modeling and querying a collection of
interlinked legal documents. The first approach is based on Formal Con-
cept Analysis and Relational Concept Analysis to model and query the
collection of documents. The second approach uses semantic web tech-
niques (RDF, OWL and SPARQL). Different types of relational queries
are discussed.
Keywords: Information Retrieval, Linked documents, Relational queries,
Formal Concept Analysis, Relational Concept Analysis, Ontology, Se-
mantic web.
1 Introduction
A collection of documents is usually represented as a set of documents. This is
a very simplified view since in reality documents are get in a set of intertextual
relations that condition their interpretation : a document should not be inter-
preted solely but with reference to the texts it cites, from which it derives or
which derive from it. In the legal domain, documents are linked to each other
with amendment, transposition, complementation, jurisprudence relations, etc.
These links are not only made for documentary purposes. They also determine
the legal validity of documents. For example, in French law, codification is the
strong process of structuration of information and the links between documents
must be explicitly expressed [1]. Consolidation requires that a legal act makes
explicit reference to its successive amendements. Legal information access tools
should take into account this complexity of legal material.
XML based standards have been defined to normalize the structure of legal
texts, in order to facilitate the access and management of these data. The trend
2 N. Mimouni
is to use those standards in the process of legal drafting so as to solve the
interoperability issue, which usefulness is obvious. In parallel, open government
data initiatives are increasing (e.g. UK Government Linked Data) and many
legal information access portals offer querying and search features on this data.
However, the data made available are often underused.
Accessing such complex data, characterized by the extra abundance of cross
references between legal texts (regulations, laws), requires a querying model inte-
grating both semantic features and intertextual links. Our requirement analysis
showed that the need for relational querying is critical from a legal point of view
(”find by which texts a given order have been applied?”, ”what are the local
texts that talk about noise that are valid at a given date?”, ”what are the texts
that modify a given text?”).
In this work, we propose two approaches which allow representing and query-
ing in a unified manner the semantic content of documents, their structure and
their intertextual relations. The proposed approaches are based on Formal and
Relational Concept Analysis (FCA, RCA), and on semantic web techniques ap-
plied to documentary objects.
The paper first reviews the existing solutions (Sec. 2) and explains the re-
quirements for relational querying (Sec. 3). Sections 4.1 and 4.2 describe the
proposed approaches and show how the collections and queries are modeled.
Those approaches are finally discussed in Section 5.
2 Related Work
In most specialized domains, documents, such as regulations or laws in the legal
domain, must not be interpreted in isolation but in relation with other doc-
uments, with which they form ”a collection of documents”. Legal documents
are linked to each other through various types of relations (e.g. amendment,
transposition, implementation, etc.) and these links often determine their legal
validity. We define a collection as a set of documents with semantic descriptors,
associated metadata and various types of semantic links between them. Law
corresponds thus to a large and highly interconnected network of documents.
IR systems should make full use of the afforded richness when processing such
complex data, thus exploiting the links, the documents structure as well as their
semantic content.
Many efforts have been made to take intertextual links into account in an IR
process. Semantic and relational search is handled by both general search engines
and specialized legal IR systems in different ways: classic IR on semantic content
then navigation with hyperlinks, boolean IR on semantic content then filtering
according to links or semantic and intertextual queries.
2.1 Intertextuality in Existing IR Systems
Suppose we have a relational query of the form ”what are documents (d’) having
a given type of link (l) with a document (d) talking about a given subject (s)?”.
Let’s consider how the above types of systems deal with such a query.
Modeling Linked Legal Documents 3
– Generalist IR systems such as Google use the most trivial way to deal with
intertextuality. The query is treated into two steps: a simple query on the
semantic content (s) returns the document (d) and the user can then nav-
igating the hyperlinks according to the type of link (l) to find the set of
answers (d’). This category of systems do not allow for relational queries.
– In the second category we classify all systems that allow relational queries via
attributes in the query such as XML native databases (queried with XPath,
XQuery) and RDF data (queried with SPARQL). The query is treated in a
first step as a boolean query on the semantic content (s) to find the set of d,
then a filtering step is performed according to the XML elements specified
in the query (for XML native repositories) or the set of constraints (in the
case of SPARQL queries).
– The third category of systems consists of relational systems such as rela-
tional databases and relational concept analysis. Both types of systems al-
low encoding the references between documents in the model level and also
formulating relational queries. The originality of this approach is that the
documents collection is structured prior to being queried. For instance, in
the case of RCA, a set of conceptual structures (called a relational lattice
family) is build upon the semantic content of the documents and the links
they hold between them. Then the query is executed against these relational
structures to find relevant answers. The advantage of this approach is to al-
low for navigating within the created lattices to specialize or generalize the
query if no exact answer is found.
2.2 Legal IR Systems
Legislative portals or legal access systems (e.g. Legifrance1 ) exist in most coun-
tries to enable a large and public access to the law. Based on XML standards,
they offer rich functionalities such as hyperlink navigation, point-in-time access
to historical and repealed documents versions2 , interactive generation of user-
oriented up-to-date information3 .
However, so far, legal links between documents have been exploited in a lim-
ited way by IR systems. For instance, in Legifrance, explicit links are mainly
dealt with manually. Some of them are included in the content of the data base
(hyperlinks) and others are implemented as document attributes when the data
base content is managed4 . The UK Legislation site allows to search for changes
made in the legislation since 20025 . The user can query the database either by
specifying the modified legislation or the legal source that introduces the change.
Whereas the system treats the general link ”modifies/modified-by” as a relation
1
www.legifrance.fr
2
e.g. UK legislation (http://www.legislation.gov.uk/search/point-in-time)
3
e.g. New South Wales legislation website (http://www.legislation.nsw.gov.au).
4
Force (V), With force term (VT), Delayed effect (VD), Repealed (Ab), Canceled
(A), Disjoint (D), Modified (M), Implied repeal (P), Substituted (S), Transferred
(T).
5
http://www.legislation.gov.uk/changes
4 N. Mimouni
between documents, more specific types of modifications are represented as doc-
ument attributes. The Italian website Normattiva enables point-in-time access
to legislation too6 , allowing thus to retrieve versions of a document in force at
different dates.
The analyzed systems do not exploit explicit links between legal documents to
their full potential. In order to illustrate this point we can think of a continuum
from less to more operational representations of links in legal IR systems:
– Links are represented as strings in the text of the document: usually they
appear in the final part of the document and are added manually (by an
editorial team).
– Hyperlinks between documents: links are references that point to objects in
the collection (other legal documents or fragments of those documents).
– Links are queryable as attributes: legal relations between documents are
represented as attributes of the linked documents.
– Relational query: links are modeled as relations between documents in the
collection. This allow for relational querying.
If we compare this continuum to the categories presented in the section 2.1,
we notice that systems of the first and the second items belong to the first cat-
egory, the third item systems belong to the second category and the last one
corresponds to the third category. Our goal is to exploit the further end of this
continuum, namely, the representation of various types of legal links as relations
between documents in the collection. It is our assumption that such represen-
tation mirrors more precisely the way legal professionals conceive the network
of legal provisions and will thus enable a more natural interaction between the
user and the system.
3 Requirements for Relational Querying
Legal expert common queries show that it is important to distinguish and exploit
different types of inter-document links. The query may deal with the case of
application of a law text (for example: ”find all application cases of a given
order”), a validity date (for example : ”which local texts deal with noise and are
valid in a given date?”) or modification links (for example: ”which are the texts
that modify another text?”). Table 1 give more examples of relational queries. To
overcome such limitations, legal IR system should deal with the rich typology of
relations linking the documents of a legal collection in order to enable relational
querying.
4 Proposed Approaches
To meet these requirements, we propose two different approches for relational
modeling and querying. The proposed approaches allow answering simple and
6
http://www.normattiva.it/ricerca/avanzata/vigente
Modeling Linked Legal Documents 5
What conventions implement the recommendations that talk about termination ?
Which recommandation about benzene are implemented by conventions on occupa-
tional Cancer ?
Does a law text has been applied? and in which cases (give examples of case law) ?
What recommandations are implemented by conventions on air pollution ?
Given an order, what are the legal texts that it develops ?
Table 1. A sample of relational queries expressed by legal experts
relational queries on a collection of linked legal documents. This work is part of
the LEGILOCAL project 7 . The collections of legal documents we are dealing
with are characterized by:
– Different types of documents (laws, codes, editorial documents, etc.).
– A specific internal structure for each document type (sections, paragraphs,
etc.).
– Various types of links between the different types of documents.
– Semantic descriptors annotating the documents w.r.t a semantic resource.
The first approach [2], based on FCA and RCA, creates classes of documents
using their semantic contents and the links between them. Despite its consistency
from a formal point of view, a major limit of this solution is the size of the
created conceptual structures when applied to a big collection of documents. To
tackle this problem, we proposed a second solution [3, 4], based on semantic web
techniques (RDF, OWL, SPARQL), which is scalable and nevertheless adresses
the problem of relational querying.
4.1 Conceptual Classification based on FCA and RCA
Figure 1 gives an overview of our approach, composed of four main steps:
Semantic content modeling: the semantic content of the documents is annotated
and binary contexts are extracted based on those annotations allowing formal
concept lattices to be build.
Intertextual structure modeling: the links between documents are identified
and relational contexts are extracted based on those links allowing enriched
relational lattices to be build.
Relational querying: the user creates a query, possibly as a combination of
semantic descriptors and cross-references constraints.
Search and results: the search algorithm analyses the query and looks for rel-
evant answers on the lattices. The user can get traditional list or graphs
of result documents. Alternatively, he can directly visualize results in the
lattice structure which can be further explored to get approximated results.
7
LEGILOCAL is an FUI project 2010-13. See http://www.mondeca.com/fr/R-
D/Projets/LegiLocal-Projet-FUI-9-Cap-digital-2010-2013.
6 N. Mimouni
Fig. 1. Overview of the relational IR approach
The semantic content of documents is first modeled as a formal context which
describes a binary relation between a set of objects and a set of attributes (object
x attributes). The objects correspond to documents. The attributes correspond
to the semantic descriptors characterizing the content of these documents. In an
information retrieval (IR) perspective, the lattice built by the FCA on binary
contexts gathers all possible combinations of documents attributes. These com-
binations are represented by the intentions of concepts having as extensions all
the documents sharing these properties. To answer a query, the search algorithm
identifies the class of documents sharing the maximum number of attributes with
the query.
We use RCA, the relational extension of FCA, to take into account the cross-
references dimension in the modeling of the collection. The approach builds a
family of relational contexts, from binary contexts (documents × semantic de-
scriptors) and a relation represented separately in a new context defining a
type of relation between documents (documents × documents). This family of
contexts forms the starting point for the creation of corresponding conceptual
structures called Relational Lattice Family [5]. RCA is able to take into con-
sideration different types of links, which are represented by different relational
contexts.
Simple Queries We call ”simple queries” the queries that are expressed as a
set of semantic descriptors. For example ”Which orders talk about abnormally
annoying noise (bag) and sound disturbance (ns)?”. The key words ”abnormally
annoying noise” and ”sound disturbance” are considered as semantic descriptors
Modeling Linked Legal Documents 7
annotating the documents which type is ”orders”. The initial lattice built with
FCA represents the set of all the simple queries based on semantic descriptors
combination which are satisfiable, i.e. return orders (all descriptors combinations
associated to a non null extension). If the query corresponds to the intension of
a concept having an extension, documents of this extension are returned as an
answer to the query. If the query corresponds to an intension without a proper
extension, we can propose a specialization or generalization of the query: this is
the advantage of the proposed approach of relational information retrieval.
Relational Queries Our model allows also to answer relational queries. Rela-
tional queries contain not only a set of semantic descriptors but also relational
indicators between documents. The relational indicators express one or different
types of cross references between one or more types of documents. For example
”Which orders talk about abnormally annoying noise (bag) and make reference
to decrees talking about soundproofing (ip)?”. The key words ”noise” and ”acous-
tic pollution” are considered as the semantic descriptors annotating respectively
the documents which are of type ”orders” and ”decrees”. Different types of re-
lational queries can be handled: legal text to legal text relational query, legal
text to semantic category relational query and semantic category to semantic
category relational query. Answers to these types of queries are graphs of linked
documents.
4.2 An Ontology of Legal Documents Collection
In the second approach we propose an ontology based document model to sup-
port the sharing of documents of French local administrations. This ontology has
been designed on the basis of Legilocal requirements analysis. It allows to repre-
sent all information on legal documents: 1) the structure of a document (sections,
paragraphs, etc.), 2) the time frame in which it is registered, 3) the semantic
description of its content using concepts or entities in the considered domain, 4)
its type (law, decree, etc.) and 5) its relationships with other documents (mod-
ification, repeal, transposition, etc.). Our document ontology is structured into
three main modules which allow to model the above properties : document mod-
ule (properties 1 and 2), the semantic module (property 3) and the collection
module (properties 4 and 5). Details of each module are given in the following.
Types and Structures of Documents In the Metalex ontology, resources
are typed according to the FRBR convention as work, expression, manifesta-
tion and item. In our model, we focus on the two upper levels, namely work
and expression, in order to represent the different versions of articles and docu-
ments. Moreover, those documents have different types (French legislation, court
decisions, local acts as well as editorial documents). These various documents
have different structures and are characterized by different metadata. Indeed,
to prepare a municipal act on a particular subject, local administrators have to
investigate national legislation and case law on the same subject. In order to
8 N. Mimouni
Fig. 2. Hierarchy of documents types
help them, our approach aims to provide semantic search in national legislation
and case law, as well as in local acts of other municipalities on the same subject
and even some editorial documents. These semantic search facilities require that
the documents be annotated with both topics and interdependencies.
As Metalex ontology was firstly designed to model legislation, we extend it
with a document typology (see figure 2) that enables us to describe specific
properties for each type of documents. For example, we want to be able to
specify the structures for certain local acts in order to check their conformance,
and some related properties such as the local organization and the person in
charge of the document which are specific for each local act. We propose a fine-
grained description for legislation text in which the basic unit is the article (which
has an independent life-cycle, and could be cited and returned as answer to a
user query). On the contrary, for local acts, we do not go through fine-grained
description and keep coarse decomposition.
Documents Relationships We want to answer queries such as: ”What are
the judgments that implement articles 4 and 5 of the law on minor work? ” or
”Which amendments are made to the article7 of law1955? ”). To reach this goal
we propose to model a collection of documents as a semantic network based on
a fine description of the types of citations. Our reference model differs from the
Metalex one in two respects. On the one hand, we refine the generic reference
notion. A broad distinction opposes the citations that refer to a textual object
and the semantic annotations that refer to non-textual objects, but we also
introduce various semantic citation subtypes (see Fig. 3). On the other hand,
we simplify the event-based model of Metalex by encoding references as direct
relations, that are directly exploitable for search and visualization purposes.
In our model, each type of reference property is associated to specific domain
and range, which allows to specify not only to which types and parts of texts (for
Modeling Linked Legal Documents 9
Fig. 3. Various types of links between different types of documents
citations) or semantic classes (for annotations) it refers but also in which types
of texts and parts of texts it may appear. Actually, we introduce an opposition
between document fragments and units to distinguish the document parts that
are citable (units or CitableBibliographicObject in Metalex ontology) from
those that are not (mere fragments). For instance, we consider whole documents
and articles as units but not the preamble of a law. The same opposition holds
for the search results: only graphs of document units can be returned to answer
a relational query. On the contrary, semantic annotations can be attached to any
fragment of text.
Semantic Annotations In this work, the term ”semantic annotation” denotes
the references that are not citations. We define semantic annotations as refer-
ences referring to ontological entities that do not represent documents or parts
of documents.
5 Discussion
In this work we have introduced a solution to the problem of the complexity of
legal sources. Using semantic content descriptors, documents typology and cross
references between documents, we have introduced two approaches to model and
search within a collection of interlinked legal documents. This allows to answer
relational queries and return graphs of linked documents. The first approach is
based on FCA and RCA to model the collection as conceptual structures. We
have experimented relational queries to explore and query this relational model
and return relevant documents or graphs of documents. A more operational
solution based on semantic technologies (RDF modeling and SPARQL querying)
is introduced as a second approach. We propose an ontology-based model to
tackle the complexity of legal sources and to model collections of interlinked
legal documents. Beyond traditional legal search, those models already support
fine-grained semantic and relational IR functionalities.
10 N. Mimouni
Adopting an integrated document model to encode the structure of the doc-
uments, their semantic annotations and the semantic structure of the collection
enables to process complex queries combining structural, intertextual and con-
tent search criteria. For instance, if a local administrator wants to find examples
of local acts dealing with ”rural roads” and based on a particular decree d, he can
express a query combining constraints on semantic annotation (refers to the
class chemin rural) and document references (cites the decree d). Our future
research will include conceiving user friendly interfaces, allowing to easily create
a relational query based on the collection characteristics (semantic descriptors,
documents types, references), and also to display results returned as graphs of
documents.
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