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|title=LawSampo Portal and Data Service for Publishing and Using Legislation and Case Law as Linked Open Data on the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-3257/paper5.pdf
|volume=Vol-3257
|authors=Eero Hyvönen,Minna Tamper,Esko Ikkala,Mikko Koho,Rafael Leal,Joonas Kesäniemi,Arttu Oksanen,Jouni Tuominen,Aki Hietanen
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==LawSampo Portal and Data Service for Publishing and Using Legislation and Case Law as Linked Open Data on the Semantic Web==
https://ceur-ws.org/Vol-3257/paper5.pdf
LawSampo Portal and Data Service for Publishing and
Using Legislation and Case Law as Linked Open Data
on the Semantic Web
Eero Hyvönen1,2 , Minna Tamper1,2 , Esko Ikkala1 , Mikko Koho1 , Rafael Leal1 ,
Joonas Kesäniemi1 , Arttu Oksanen1 , Jouni Tuominen1,2 and Aki Hietanen3
1
Semantic Computing Research Group (SeCo), Aalto University, Finland
2
Helsinki Centre for Digital Humanities (HELDIG), University of Helsinki, Finland
3
Ministry of Justice, Finland, Finland
Abstract
This paper argues for the idea of publishing legislation and case law as Linked Open Data (LOD) on the
Semantic Web, to cater several user groups, including the general public, legislators, lawyers, researchers
of legal informatics, and application developers. To support the argument, the proof-of-concept system
LawSampo – Finnish Legislation and Case Law on the Semantic Web is introduced, including a semantic
portal and a LOD service. Based on the Sampo Model, the main novelty of LawSampo is the provision of
heterogenous distributed legal data through multiple application perspectives for faceted searching and
exploring the data and for data analysis in legal informatics.
Keywords
Linked data, Case law, Legislation, Semantic portal
1. Introduction
Legislation and case law are widely published online by governments to make jurisdiction
transparent and freely accessible to the public, organizations, and lawyers [1]. The Web
provides a promising medium for publishing such big data. There are, e.g., portals, such as
legislation.gov.uk for the legislation for the UK, Scotland, Wales, and Northern Ireland1 , and EU
level systems, such as the EU Cellar2 and the ECLI Search Engine3 for the case law.
However, legal documents are often available only as texts for the humans to read with little
metadata available, which makes them hard to use in applications of legal informatics4 [2], e.g.,
in computational law5 . To address the problem, this paper argues that legislation and case law
IJoint Proceedings of ISWC2022 Workshops: the International Workshop on Artificial Intelligence Technologies for Legal
Documents (AI4LEGAL) and the International Workshop on Knowledge Graph Summarization (KGSum) (2022)
0000-0003-1695-5840 (E. Hyvönen); 0000-0002-3301-1705 (M. Tamper); 0000-0002-9571-7260 (E. Ikkala);
0000-0002-7373-9338 (M. Koho); 0000-0001-7266-2036 (R. Leal); 0000-0002-3770-0006 (J. Kesäniemi);
0000-0003-2327-6942 (A. Oksanen); 0000-0003-4789-5676 (J. Tuominen)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
https://www.legislation.gov.uk
2
https://data.europa.eu/euodp/en/data/dataset/sparql-cellar-of-the-publications-office
3
https://e-justice.europa.eu/content_ecli_search_engine-430-en.do
4
https://en.wikipedia.org/wiki/Legal_informatics
5
https://law.stanford.edu/2021/03/10/what-is-computational-law/
41
should be published and used as Linked Open Data (LOD) on the Semantic Web. To support the
argument, a case study based on Finnish legislation and case law is overviewed as the system
LawSampo – Finnish Legislation and Case Law on the Semantic Web that consists of a LOD
service and a semantic portal, extending our earlier short paper [3]. LawSampo is based on
the more general Sampo Model [4] for collaborative LOD publication that has been applied to a
series of portals6 in Digital Humanities.
In the following, we first describe the LOD underlying LawSampo. After this using the portal
and data service are explained. In conclusion, related works are discussed and contributions
and lessons learned are summarized.
2. LawSampo Linked Open Data
Primary Data Finnish legislation and case law decisions have been published as web documents
since 1997 in the Finlex Data Bank7 . Although this service is widely used, it does not provide
machine-readable legal information as open data. To address this, we published a selection
of Finlex data as the Semantic Finlex [5] LOD service that currently contains ca. 28 million
triples. We transformed this data into a simplified data model suitable for the portal, and the
data was enriched by data linking and knowledge extraction techniques.
Data Model LawSampo represents legislation and case law using a simple data model. The
legislation data consists of statutes and their sections, whereas the case law data includes court
decisions with language versions. Metadata about the instances are given using various classes
and properties, mostly aligned with DCMI Metadata Terms8 . The data model schema is available
and documented at the namespace URI http://ldf.fi/schema/lawsampo/.
Data Transformation The LawSampo data transformation process is presented in Fig. 1.
Semantic Finlex data is first transformed and filtered with SPARQL Construct queries. The
Legislation RDF data contains only the latest versions of the consolidated legislation. Next,
keyword extraction and document classification is employed to the textual contents to link them
to corresponding subject keywords and life situations, respectively. After this, the entities are
further linked to facet ontologies of time and EU legislation—The LawSampo portal is based on
faceted search. The third step involves applying Named Entity Linking to the textual contents
of Legislation and Case Law RDF. The facet ontologies are transformed from CSV format into
RDF.
Internal linking The data was linked internally to improve the references to other documents.
The links to legal documents needed more processing as the statutes for instance may refer to
more concrete part of the statute in a specific version. Unfortunately, the Semantic Finlex
data is not complete and requires some human interpretation. A challenge in the data is that
the court decisions only have the judgment date but not the dates for the events that are under
investigation in the document. However, the judgment is based on legislation that was valid
during the time of the events that are being evaluated. Therefore, the linking from decisions to
statutes cannot be done to a specific statute version but only to the current consolidated version
6
https://seco.cs.aalto.fi/applications/sampo/
7
http://www.finlex.fi
8
https://www.dublincore.org/specifications/dublin-core/dcmi-terms/
42
Figure 1: LawSampo data transformation process from Semantic Finlex
of it that doesn’t take into account what version of the statute was in force at the time of the
judged event.
External linking The LawSampo dataset has been linked to external data sources including
EU Cellar and the Finlex service. The EU Cellar links have been extracted from the original
Finlex statute documents [5] and included in the LawSampo data with their descriptive texts.
The statutes and the court decisions were linked to the Finlex service to show the original
source data.
Terminological linking The legal terms occurring in the texts were linked to their explana-
tions in order to make the texts more readable to the layman by a contextual reader [6]. The
Nelli [7] and ARPA tools [8] are used to identify term instances in legal documents and link
them to vocabularies with terminological definitions: the Combined Legal Concept Ontology [9],
Finnish DBpedia, and the Helsinki Term Bank for the Arts and Sciences9 . A term instance of a
document contains information about its literal representation, links to its definitions in the
vocabularies and the document, and a count telling how many times the term in mentioned
in the document. The count information is needed for generating tag clouds summarizing the
contents of the documents. The instances are represented in RDF and added to the LawSampo
dataset.
Keyword extraction and document classification The subject indexing tool Annif10 [10],
developed by the National Library of Finland, is used to perform keyword extraction for all
the legislative documents. Annif is capable of using different algorithms in order to return
suggested keywords and their respective weights for a given input text. The developers also
provide a REST API containing various pre-trained projects that combine different algorithms.
LawSampo uses the yso-fi pre-trained project, which integrates TF-IDF, Maui and Parabel. The
first two are lexical algorithms, so they directly match terms to a vocabulary, whereas Parabel
uses an associative approach that is able to also find indirect correlations between words [10].
This mix provides results that are not only grounded on the text of the documents but also able
9
https://tieteentermipankki.fi
10
https://annif.org
43
to extrapolate their specific wording. Yso-fi is trained on bibliographical metadata from Finnish
museums, archives and libraries. Since the training data is labeled with terms from the General
Finnish Ontology (YSO)11 , the API returns keywords identified by unique YSO URIs.
A zero-shot classification system based on the extracted keywords [11] is also used. It works
by first transforming the documents into vectorial representations via the word-embedding
algorithm fastText [12], using a pre-trained Finnish language model offered by the fastText
developers [13]. The document representations are then calculated as the average embedding
of their respective keywords. A similar treatment involving Annif and fastText is given to
a list of category labels representing different life situations, such as asuminen, kiinteistö
‘housing, real state’, ihmisoikeudet, perusoikeudet ‘human rights, basic rights’ or omaisuus,
kaupankäynti, kuluttajansuoja ‘property, commerce, consumer protection’. This results in
vectorial representations for each category as well. The classification is then carried out by
comparing the document vectors with the category vectors via cosine similarity. Each document
is assigned the 5 best-fitting categories whose weight is within 95% of their top category.
Keywords and categories are used in two different ways in LawSampo: they are used to
label the respective legislative documents as metadata (respectively as subject keywords and
life situation/topic), and they also form the basis for a semantic search system which will be
explained later in this paper.
Linked Open Data Service The LawSampo data service adopts the 5-star Linked Data
model12 , extended with two more stars, as suggested in the Linked Data Finland model and
platform [14]. The 6th star is obtained by providing the dataset schemas and documenting
them. The LawSampo schema can be downloaded from the service13 and the data model is
documented using the LODE service14 . The 7th star is achieved by validating the data against
the documented schemas to prevent errors in the published data. LawSampo attempts to obtain
the 7th star by applying different means of combing out errors in the data within the data
conversion process. The LawSampo data model and its integrity constraints are presented in a
machine-processable format using the ShEx Shape Expressions language15 [15]. We have made
initial validation experiments with the PyShEx16 validator. Based on the experiments, we have
identified errors both in the schema and the data, and a full-scale ShEx validation phase for the
data conversion is underway.
The Linked Data service is powered by the Linked Data Finland17 publishing platform that
along with a variety of different datasets provides tools and services to facilitate publishing and
re-using Linked Data. All URIs are dereferenceable and support content negotiation by using
HTTP 303 redirects. The data is available as an open SPARQL endpoint18 . As the triplestore,
Apache Jena Fuseki19 is used as a Docker container, which allows efficient provisioning of
11
https://finto.fi/yso/en/
12
https://www.w3.org/DesignIssues/LinkedData.html
13
https://www.ldf.fi/dataset/lawsampo
14
https://essepuntato.it/lode/
15
https://shex.io
16
https://github.com/hsolbrig/PyShEx
17
http://ldf.fi
18
https://ldf.fi/lawsampo/sparql
19
https://jena.apache.org/documentation/fuseki2/
44
resources (CPU, memory), portability, and scaling. Varnish Cache web application accelerator20
is used for routing URIs, content negotiation, and caching.
3. Using LawSampo Portal
This section overviews how the LawSampo portal and the underlying LOD service are used
in practise. The system is based on the Sampo Model [4] that is an informal collection of six
principles for 1) LOD publishing and 2) designing semantic portal user interfaces (UI), supported
by the Sampo-UI framework [16].
The landing page of the LawSampo portal depicted in Fig. 2 offers five different application
perspectives. Semantic faceted semantic search is used for filtering data of interest out after
which the data can be either browsed or analyzed using a set of seamlessly integrated data-
analytic tools. The five application perspectives are explained below.
Figure 2: LawSampo landing page with five application perspectives
1. Statutes Perspective By clicking on the Statutes perspective box, a faceted search interface
for searching and browsing statutes is opened. The facets on the left include document type
(with seven subtypes), statute type, year, and related EU regulation. After filtering out a set of
documents (or a particular document) of interest, the user is provided with two options. First,
the user can select a document from the result list and a “homepage” of the document opens,
showing not only the document but also linked contextual information related to it such as the
referred EU regulations linked to EU Cellar or other documents from Semantic Finlex referring
to it. The LawSampo application utilizes enriched data and shows annotated statute documents.
The annotations are highlighted in the text and by hovering over the annotation, the user can
see the explanation to the term and links to external portals such as Wikipedia or the Helsinki
Term Bank for the Arts and Sciences to learn more about the term. The terms are also used to
create tag cloud visualizations to give the user an idea what the text is about.
20
https://varnish-cache.org
45
2. Sections Perspective The Sections perspective operates in the same way, but here it is
possible to search and explore consolidated legislation on a more focused section level.
3. Case Law Perspective In the Case Law perspective, a similar faceted search interface
opens for searching and browsing court decisions. In this case, the facets include court, judge,
and keywords characterizing the subject matter of the judgment. Similarly to statutes, the
case law view shows the results based on the facet selections as a list for the user. From this
point on the user can view the court decision details at its “homepage”. Similarly to statutes,
the court decision’s page contains the annotated text document, a tag cloud, and more related
information about it. The court decisions also have been enriched in the portal with related
case law documents based on Semantic Finlex Case Law Finder. It retrieves documents that are
textually similar to the selected document and the results are listed in the table tab.
In addition to the court decision listing and homepages, the user’s choices also influence the
other tabs in the case law perspective, for example, the statistics, such as the facet’s pie charts
or the by year bar charts for the court decisions. By selecting a value from a facet, all other
facets and results update and the distribution of court decisions by year or by facet (e.g., by
court, in the court facet) show the updated results. With these statistical tools the user can
view and study the case law data. Fig. 3 is a screenshot from the Case Law perspective’s plot
depicting the number of court decisions by year. The plot shows the number of court decisions
with the judgment date information on a timeline.
Figure 3: Number of court decisions in 1990–2019 in Case Law Perspective
The Case Law perspective also enables the user to export the faceted search SPARQL query
into the Yasgui21 tool on a separate tab EXPORT. The data can then be explored further by
editing the SPARQL query, and the results can be downloaded for further study, e.g., into a
spreadsheet program in CSV form.
4. Contextual Search Perspective The fourth perspective, named Contextual Search,
allows for searching legal documents based on the end user’s life situation at hand (e.g., divorce).
This search system employs the Relevance Feedback Search (RFBS) paradigm, which works
21
https://yasgui.triply.cc/
46
by refining the search parameters iteratively, with input from the user, in order to improve its
results. In LawSampo this is done by offering the user keyword and category suggestions based
on the results of the previous search round: by activating or deactivating these suggestions,
the user gradually redirects the query towards a more satisfying result. This kind of search
is argued to be useful in situations where it is difficult for the user to formulate a traditional
search query. This application is described in [11] in more detail.
5. Similarity-based Case Law Search The landing page also provides a link to an application
for searching court decisions, based on the assumption that textually similar cases are relevant for
the information need. The user is able to input a text document as a query, either by uploading a
file or by writing text directly into the form. This application can be helpful when, e.g., someone
has received a court decision and is interested to see whether the verdict is fair in comparison
with other similar cases. Hopefully, such possibility could lessen unnecessary appeals to higher
courts in the future. In this application several methods for finding similar cases were tested
when implementing this application including TF-IDF, Latent Dirichlet Allocation, Word2Vec,
and Doc2Vec [17].
In addition to the ready-to-use statistical applications integrated into the LawSampo portal,
the underlying open SPARQL endpoint can be used for querying, analyzing, and visualizing the
data in flexible ways and external tools. For example, the visualization in Fig. 4 shows how the
number of court decisions of different kinds in the data change in time during 1980–2020. This
graph has been created using Jupyter notebooks. The figure shows how the number of civilian
and criminal cases decreases in time, and that the cases of the Administrative court dominate
the dataset. There is also a number of court decisions without type of the matter described.
Also their number is decreasing but slowly.
Figure 4: Number of court decisions by type of the matter
47
4. Discussion
Related Works Our work on legal Linked Data services was influenced by the MetaLex
Document Server22 [18] and related national online services for legal documents in Greece,
Luxemburg23 , France, Norway24 , and the U.S. [19]. EU Cellar publishes EU legislation as LOD.
Companies provide legal services for searching and exploring legislation and case law, and
Google Scholar has a specific search application for cases in the various courts of the states25 in
the U.S.
Contributions and Challenges This paper applied the Sampo Model, developed originally
for Digital Humanities research, to a novel use case in legal informatics. Legislation and case
law data are provided through multiple end user groups and purposes through application
perspectives. The documents are automatically enriched with contextual linked data, and the
end user is provided with ready-to-use faceted search and data-analytic tooling for analyzing
the documents and their relations.
However, extracting and linking references of legal documents requires still more work. The
references to legal documents can be made in various ways and the labels we currently have
in our databases are not enough to identify all the ways in which the references are made in
texts. There are references made using the official names or nick names that exist in the Finlex
database, but some references are made with unidentified acronyms or by twisting the order of
words in the names, which may produce unidentifiable wordings for different statute names.
It would be much easier to add metadata about related documents manually when indexing
the documents than trying to extract the links from unstructured texts afterwards. The biggest
semantic challenge we encountered in our work was that the statutes are not stable but their
sections are dynamically added, cancelled, and modified in time by other statutes. In the Finnish
legislation system, systematic time series of consolidated versions of legislation are not available,
but only the initial versions of the statuses and series of changes made to them afterwards. The
court decisions are, however, always made based on the legislation in force at the time of the
judicial offense, which makes the linking between legislation and case law difficult. LawSampo
has access only to the latest versions of manually consolidated statues available in Finlex, and
the problem of finding out how the statutes may have changed in time is left to the end user. In
many cases, the court decision does not even tell when the judged offence was made, but may
only refer to a lower court decision where the date information may be available. From data
publishing point of view this information should be added to the decision metadata already at
the courts.
Usability of the LawSampo Portal has not been evaluated yet. However, the Sampo model
has been evaluated in some other Sampo portals [20] suggesting feasibility of the model in
general. An empirical evidence of this is also that Sampo portals are widely used on the Web by
up to millions of users [4].
In spite of the challenges and complexities of the underlying data, we are confident that that
proposed LOD approach is feasible and usable in practice, and plan to make the LawSampo
22
http://doc.metalex.eu
23
http://legilux.public.lu/editorial/eli
24
http://lovdata.no/eli
25
https://scholar.google.com/scholar_courts
48
prototype publicly available in the future.
Acknowledgments We thank Tiina Husso, Risto Talo and Jari Linhala for collaborations.
Funding was provided by the Ministry of Finance, the Academy of Finland, the EU project
InTaVia26 , and action Nexus Linguarum27 on linguistic data science. CSC – IT Center for Science
provided computational resources.
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