#  1 INTRODUCTION

This document contains the initial version of the Data Management Plan (DMP).
The final version of this document will be available as “D2.4 Data Management
Plan” in M18. This document is complemented by “D7.2 IPR and Data Protection
Management”, to be delivered by M6 as well.

The Data Management Plan adheres to and complies with the _H2020 Data
Management Plan – General Definition_ given by the EC online, where the DMP is
described as follows:

“A DMP describes the data management life cycle for the data to be collected,
processed and/or generated by a Horizon 2020 project. As part of making
research data findable, accessible, interoperable and reusable (FAIR), a DMP
should include information on:

  * the handling of research data during and after the end of the project 

  

  * what data will be collected, processed and/or generated 

  

  * which methodology and standards will be applied 

  

  * whether data will be shared/made open access and 

  

  * how data will be curated and preserved (including after the end of the project)” 

  

Section 2 follows the template proposed by the EC  1  . Lynx adopts policies
compliant with the official FAIR guidelines [1] (findable, accessible,
interoperable and re-usable).

Lynx participates Open Research Data Pilot (ORDP) and is obliged to deposit
the produced research data in a research data repository. For such effect, the
Zenodo repository has been chosen, which exposes the data to OpenAIRE granting
its long term preservation. The description of the most relevant datasets for
compliance have been published in a Lynx Data Portal, using CKAN technology.
Metadata is provided for every relevant dataset, and data is selectively
provided whenever it can be republished without license restrictions and
relevance for the project is high. This deliverable also describes a catalogue
of relevant legal and regulatory data models and a strategy for the
homogenisation of the data sources.

Finally, the document describes the concept of a Multilingual Legal Knowledge
Graph for Compliance, or Legal Knowledge Graph for short (Section 5), which is
the backbone on when the Lynx services will rest (Figure 1).

**European Directives**

General legal goals for every

European Member State

**National Legislation**

Every Member State has

different national and

regional legislation in force

**European Regulations**

Legislative act binding in every

**Industry standards**

Technical documents in

occasions necessary to

achieve certification

**Case law**

Judgements,

sentences

European Member State

**Figure 1.** Schematic description of the Multilingual Legal Knowledge Graph
for Compliance

#  2 DATA MANAGEMENT PLAN

This Section is the Initial Data Management Plan. It follows the template
proposed by the EC and is applicable to the data used in or generated by Lynx,
with the sole exception of pilot-specific data, whose management may be
further specified in per-pilot DMPs. If the implementation of the pilots
required a different DMP, either new DMP documents or new additions to this
document shall be defined by the pilot leaders and the resulting work included
in D2.4.

##  2.1 DATA SUMMARY

**Purpose** . The main objective of Lynx is “to create an ecosystem of smart
cloud services to better manage compliance, based on a legal knowledge graph
(LKG) which integrates and links heterogeneous compliance data sources
including legislation, case law, standards and other aspects”. In order to
deliver these smart services, data will be collected and integrated into a
Legal Knowledge Graph, to be described in more detail in Section 3.

**Formats** . The very nature of this project makes the number of formats too
high as to be foreseen in advance. However, the project will be keen on
gathering data in RDF format or producing RDF data itself. RDF will be the
format of choice for the meta model, using standard vocabularies and
ontologies as data models. More details on the initially considered data
models are given in Section 5.

**Data reuse** . The core part of the LKG will be created by reusing existing
datasets, either copying them into the consortium servers (only if strictly
needed) or using them directly from the sources.

**Data origin** . Although Lynx will be greedy in gathering and linking as
much compliance-related data as possible from any possible source, it can be
foreseen that the Eur-Lex portal will be the principal data source. Users of
the Pilots may contribute their own data (e.g. private contracts, paid
standards), which will be neither included into the LKG nor made publicly
available.

**Data size** . The strong reliance of Lynx in external open data sources
minimizes the amount of data that Lynx will have to physically store. No
massive data storage infrastructure is foreseen.

**Data utility** . Data will be useful for SMEs and EU citizens alike through
different portals.

##  2.2 FAIR DATA

###  2.2.1 Making data findable, including provisions for metadata

**Discoverability** . Data will be discoverable through a dedicated data
portal (http://data.lynxproject.eu), further described in Section 4. Data
assets will be identified with a harmonized policy to be defined in the
forthcoming months.

**Naming convention** . A specific URI minting policy will be used to identify
data assets. The policy will be specified in the forthcoming months after the
publication of this deliverable.

**Search keywords** . Open datasets described in the Lynx data portal are
findable through standard forms including keyword search.

**Versioning** . Versioning is an intrinsic part of the URI strategy to be
devised.

**Metadata** . Metadata records describing each dataset will be downloadable
as DCAT-AP entries.

###  2.2.2 Making data openly accessible

**Open data** : **data in the LKG** .

The adopted approach is “as open as possible, as closed as necessary”. Data
assets produced during the project will preferably be published as open data.
Nevertheless, during the project some datasets will be created from existing
private resources (e.g. dictionaries by KDictionaries), whose publication
would irremediable damage their business model. These datasets will not be
released as open data.

Datasets in the LKG will be in any case published along with a license. This
license will be specified as a metadata record in the data catalog, which can
also be exported as RDF using the appropriate vocabulary terms (dtc:license)
and eventually using machine readable licenses.

**Open data: research data.**

In December 2013, the EC announced their commitment to open data through the
Pilot on Open Research Data, as part of the Horizon 2020 Research and
Innovation Programme. The Pilot’s aim is to “improve and maximise access to
and reuse of research data generated by projects for the benefit of society
and the economy”. In the frame of this Pilot on Open Research Data, results of
publicly-funded research should be disseminated more broadly and faster, for
the benefit of researchers, innovative industry and citizens.

The Lynx project chose to participate in the Open Research Data Pilot (ORDP).
Consequently, publishing as “open” the digital research data generated during
the project is a contractual obligation (GA Art. 29.3). This provision does
not include the pieces of data which are derivative of private data of the
partners. Their openness would endanger their economic viability and
jeopardize the Lynx project itself (which is sufficient reason not to open the
data as per GA Art. 29.3).

Every Lynx partner will ensure Open Access to all peer-reviewed scientific
publications relating to its results. Lynx will use Zenodo as the online
repository (https://zenodo.org/communities/lynx/) to upload public
deliverables and possibly part of the scientific production. Zenodo is a
research data repository created by OpenAIRE to share data from research
projects. Records are indexed immediately in OpenAIRE, which is specifically
aimed to support the implementation of the EC and ERC Open Access policies.
Nevertheless, in order to avoid fragmentation, the Lynx webpage will act as
the central information node.

The following categories of outputs require Open Access to be provided free of
charge by Lynx partners, to related datasets, in order to fulfil the H2020
requirements of making it possible for third parties to access, mine, exploit,
reproduce and disseminate the results contained therein:

  * _Public deliverables_ will be available both at Zenodo and the Lynx website at http://lynxproject.eu/publications/deliverables. See Figure 1 and Figure 2. 

  

  * _Conference and Workshop presentations_ may be published at Slideshare under the account https://www.slideshare.net/LynxProject. 

  

  * _Conference and Workshop papers and articles for specialist magazines_ may be also reproduced at: http://lynx-project.eu/publications/articles. 

  

  * _Research data and metadata_ are also available. Metadata and selected data is available in the CKAN data portal, http://data.lynx-project.eu, produced research data at Zenodo. 

  

Information will be also given about tools and instruments at the disposal of
the beneficiaries and necessary for validating the results.

**Figure 2.** Lynx public deliverable at Zenodo.

**Figure 3.** Deliverables on the Lynx website

**Accessibility** . Data descriptions (metadata) will be accessible through a
dedicated data portal, hosted in Madrid and available under http://data.lynx-
project.eu. Eventually, data from small datasets will be also made available
from the web server –where _small_ means a file size that does not compromise
the web server availability. Eventually the metadata descriptions will be
uploaded into other repositories, such as Retele  2  resources in Spanish
language, ELRC-SHARE  3  in general and others to be identified. In addition,
the cooperation with the CEF eTranslation  4  TermBank project will be
considered, in view of sharing terminological domain-specific resources.

**Necessary methods and tools to access the data and its documentation** .
Relevant datasets whose license is liberal will be available as downloadable
files. Eventually, a SPARQL endpoint will be set in place for those dataset in
RDF form. Also, the CKAN technology in which the portal is based on, offers an
API using standard JSON structures to access the data. The CKAN platform
provides the documentation on how to use the API
(http://docs.ckan.org/en/ckan-2.7.3/api/).

**Publication of software** . Some of the software to be developed in Lynx is
expected to be published as Open Source. Other software to be developed in
Lynx will be derived from private or non-open source code and, thus, not be
made publicly accessible.

**Data and code repositories and arrangement** . Lynx uses a private source
code repository

(https://gitlab.com/superlynx). Open data will be deposited in the Lynx open
data portal; consortiuminternal data within the project intranet. The choice
of Nextcloud is justified as the information resides within UPM secured
servers in Madrid, avoiding third parties and granting the privacy and
confidentiality of the data. Gitlab, as a major provider and host of code
repositories, is a common choice among developers but if necessary code might
be also hosted at UPM.

**Data Access Committee** . As of today, there is no need for a Data Access
Committee  5  .

**Conditions for access** . Description of data assets include a link to well-
known licenses, for which machine readable versions exist. Either Creative
Commons Attribution International 4.0 (CC-BY) or Creative Commons Attribution
Share-Alike International 4.0 (CC-BY-SA) will be the recommended licenses.

**Access control** . The Lynx intranet (Nextcloud) provides standard access
control functionalities. The servers are located in a secured data centre at
UPM. The access point is https://delicias.dia.fi.upm.es/lynx-nextcloud/.
Access is secured by asymmetric keys or passwords and communications use SSL.

###  2.2.3 Making data interoperable

**Interoperability** . The LKG preferred format is RDF, granting
interoperability between institutions, organisations and countries. This
choice optimally facilitates re-combinations with different datasets from
different origins.

**Data and metadata vocabularies** . Specific data and metadata vocabularies
will be defined throughout the entire project. An initial collection has
already been edited and will be soon published at  http://lynx-
project.eu/data/data-models  (see also Figure 3).

**Standard vocabularies and inter-disciplinarity** . Standard vocabularies
will be used inasmuch as possible, like the ECLI ontology, the Ontolex model
and other vocabularies similarly spread. These choices grant inter-
disciplinary collaboration. For example, Ontolex  6  is standard in the
language resources and technologies communities, whereas the ELI ontology  7
(European Law Identifier) is standard in the European legal community.

**Mappings of vocabularies and ontologies developed by Lynx** . If
vocabularies or ontologies are further defined, they will be published online,
documented and mapped to other standard ontologies. Figure 3 illustrates a
possible visualization for the data models.

**Figure 4.** A catalogue of relevant ontologies and vocabularies

###  2.2.4 Increase data reuse

**Embargoes** . No data embargoes are foreseen. Public data will be published
as soon as possible, but private data will remain private as long as the
interested parties, rightsholders of the data, decide.

**Data after the project** . Lynx aims at building a LKG towards compliance.
In the long term, the LKG may be repurposed and the data portal may become a
reference entry point to find open, linguistic legal information as RDF.

**Data validity after time** . Some of the datasets require maintenance (e.g.
legislation and case law must be kept up to date). Whereas a core of
information may still be of interest even with no maintenance, those datasets
directly used by services under exploitation will be maintained. In any case,
metadata records describing the datasets will include a field informing on the
last modification date.

**Data quality assurance** . Only formal aspects of data quality are expected
to be assured. In particular, the 5-stars  8  will be considered, and the data
portal will describe this quality level in due time.

##  2.3 ALLOCATION OF RESOURCES

**Costs** . The cost of publishing FAIR data includes (a) maintenance of the
physical servers; (b) time devoted to the data generation and (c) long term
preservation of the data.

**Coverage of the costs** . Resources to maintain and generate data are
covered by the project. Long term preservation of data is free by uploading
the research data at Zenodo.

**Responsibility of the Data Management** . UPM is responsible for managing
data in the data portal, and for managing private data in the intranet. UPM is
not responsible of keeping personal data collected to provide the pilot
services but the directly involved partners (openlaws, Cuatrecasas, DNV GL).

**Long term preservation** . Public deliverables and research data will be
uploaded to Zenodo, which grants the long term preservation. A specific
community has been created in Zenodo  9  . Alternatively, if difficulties are
found with Zenodo, datasets may also be uploaded to Figshare  10  or B2Share
11  where a permanent DOI is retrieved. Other sites such as META-SHARE, ELRC-
SHARE or the European Language Grid may be considered in addition to grant
long term preservation and maximize the impact and dissemination.

##  2.4 DATA SECURITY

###  2.4.1 Data Security

**Data security** . UPM is physically storing data on their servers: webpage,
files and data in the Nextcloud system, the CKAN data catalogue and mailing
lists. These pieces of data are both digitally and physically secured in a
data centre. Backups are made of these systems, to external hard disks or
other machines. In principle, no personal data will be kept at UPM, and the
pilot leaders will define specific DMP with specific data protection
provisions and specific data security details.

**Long term preservation** . Relevant data which is open, shall be uploaded to
Zenodo. In addition, relevant language datasets produced in the course of Lynx
will be uploaded to catalogues of language resources.

##  2.5 LEGAL, ETHICAL AND SOCIETAL ASPECTS

###  2.5.1 Legal framework

EU citizens are granted the rights of privacy and data protection by the
Charter of Fundamental rights of the EU. In particular, Art. 7 states that “
_everyone has the right respect for private and family life, home and
communications_ ”, whereas Art. 8 regulates that “ _everyone has the right to
the protection of personal data concerning him or her_ ” and that processing
of such data must be “ _on the basis of the consent of the person concerned or
some other legitimate basis laid down by law_ .”

These rights are developed in detail by the General Data Protection Regulation
(GDPR), Regulation 2016/679/EC, which is in force in every Member State since
25  th  of May of 2018. This regulation imposes obligations to the Lynx
consortium, which is also reminded by Art. 39 of the Lynx Grant Agreement
(GA): “ _the beneficiaries must process personal data under the Agreement in
compliance with applicable EU and national law on data protection_ ” The same
GA also reminds that beneficiaries “ _may grant their personnel access only to
data that is strictly necessary for implementing, managing and monitoring the
Agreement_ ” (GA Art. 39.2).

_Personal data_ is, according to GDPR art. 4.1 “ _any information relating to
an identified or identifiable natural person (‘data subject’); an identifiable
natural person is one who can be identified, directly or indirectly, in
particular by reference to an identifier such as a name, an identification
number, location data, an online identifier or to one or more factors specific
to the physical, physiological, genetic, mental, economic, cultural or social
identity of that natural person_ ”, whereas _data processing_ is (art. 4.2): “
_any operation or set of operations which is performed on personal data or on
sets of personal data, whether or not by automated means, such as collection,
recording, organisation, structuring, storage, adaptation or alteration,
retrieval, consultation, use, disclosure by transmission, dissemination or
otherwise making available, alignment or combination, restriction, erasure or
destruction_ ”. With these definitions, Pilot 1 (Compliance Assurance Services
in Data Protection) will most likely have to collect and process personal
data, and possibly other Pilots as well.

The purposes for which personal data will be collected are justified in
compliance with art.5.b, and the processing of personal data is legitimate in
compliance with art. 6. The implementation of the Pilot 1 and other pilots
processing personal data will have to implement the necessary legal provisions
to respect the rights of the data subjects.

Several internal communication channels have been established for Lynx:
mailing lists, a website and an intranet. The three servers are hosted at UPM
and comply with the Spanish legislation.

The Lynx web site (http://lynx-project.eu) is compliant regarding the
management of cookies with _Ley 34/2002, de 11 de julio, de servicios de la
sociedad de la información y de comercio electrónico_ . Lynx will most likely
handle datasets with personal data (Pilot 1), as users will be registered in
the Lynx platform to enjoy personalised services and to upload contracts with
personal data. The consortium will adopt any measure to comply with the
current legislation.

###  2.5.2 Ethical aspects

The ethical aspect of greatest interest is the processing of personal data.
The processing of personal data may become a possibility in the framework of
Pilot 1. GA Article 34 “Ethics and research integrity” is binding and shall be
respected. Ethical and privacy related concerns are fully addressed in Section
3.2 of Deliverable 7.2 “ _IPR and Data Protection management documents_ ”.

Besides, the ethics issues identified are already being handled by the pilot
organisations during their daily operation activities, as they confront with
national laws and EU directives regarding the use of information in their
daily services, as clearance for the processing, storing methods, data
destruction, etc. has been provided to such organisation a priori and is not
case specific. The research to be done during Lynx does not raise any other
issues, and the project will make sure that it will follow the same patterns
and rules used by the pilot organisations, that will guarantee the proper
handling of ethical issues and the adherence to national, EU wide and
international law and directives that do not violate the terms of the
programme.

###  2.5.3 Societal impact

The societal impact of this project is expected to be positive, enhancing the
access of EU citizens to legislation and contributing towards a fairer Europe.
In addition to the best effort made by the project partners, members of the
Advisory Board may be requested to issue a statement on the ethical and
societal impact of the Lynx project.

#  3 CATALOGUE OF DATASETS

This section describes a catalogue of relevant legal, regulatory and
linguistic datasets. Datasets in the Legal Knowledge Graph are those necessary
to provide compliance related services that also meet the requirement of being
published as linked data. The purpose of Lynx Task 2.1 is twofold:

  1. Identify as many as possible open dataset possibly relevant to the problem in question (either in 

  

  

RDF or not)

  2. Build the Legal Knowledge Graph by identifying existing linked data resources or by transforming existing datasets into linked data whenever necessary 

  

  

Figure 5 represents the Legal Knowledge Graph as a collection of dataset
published as linked data. The LKG lies amidst another cloud of datasets, in
various formats either structured or not (such as PDF, XLS or XML). The
section contains: (a) the methodology followed to describe datasets of
interest; (b) the methodology to transform existing resources into LKG
datasets; (c) a description of the Lynx data portal and the related technology
and (d) an initial list of relevant datasets.

Legal Knowledge Graph

(

RDF

)

Other datasets of interest

(

PDF, XLS, XML…

)

**Figure 5.**

Datasets in

the LKG and out of it

##  3.1 METHODOLOGY FOR CATALOGUING DATASETS

Data assets potentially relevant to the Lynx project are those that might help
providing multilingual compliance services. They might be referenced by
datasets in the LKG as external references.

The identification and description of these datasets is being made during the
project in a cooperative way, during the entire project lifespan. The
methodology has consisted of the following steps:

  1. _Identification of datasets of possible interest_

  

  

 Identification of relevant datasets by the partners;

 Discovery of relevant datasets by browsing data portals, reviewing
literature and making general searches;

  2. _Description of resources_

  

  

 Description of the resources identified in Step 1 using an agreed template
(spreadsheet) with metadata records (see Section 4.2.1).

  3. _Publication of dataset descriptions_

  

  

 Publication of the dataset description in the CKAN Open Data Portal via CKAN
form

 Transformation of the metadata records to RDF using the vocabulary DCAT-AP
(to be an automated task from the spreadsheet)

This process is being iteratively carried out throughout the project.

###  3.1.1 Template for data description

Every partner of Lynx, within their domain of expertise, has described an
initial list of data sources of interest for the project. In order to
homogeneously describe the data assets, a template with metadata records has
been created with the due consensus among the partners.

The template for data description contains two main blocks: one with general
information about the dataset and another with information about the resource.
Within this context, “dataset” makes reference to the whole asset, while
“resource” defines each one of the different formats in which the dataset is
published. For instance, the UNESCO thesaurus is a single dataset which can be
found as two different resources: as a SPARQL Endpoint and as a downloadable
file in RDF.

Thereby, the metadata records in Table 1 describe information about the
dataset as a whole.  
  
<table>  
<tr>  
<th>

**Field Description**

</th> </tr>  
<tr>  
<td>

Title

</td>  
<td>

the name of the dataset given by the author or institution that publishes it.

</td> </tr>  
<tr>  
<td>

URI

</td>  
<td>

identifier pointing to the dataset.

</td> </tr>  
<tr>  
<td>

Type in the LKG

</td>  
<td>

type of dataset in the legal knowledge graph (language, data, etc.).

</td> </tr>  
<tr>  
<td>

Type

</td>  
<td>

type of dataset (term bank, glossary, vocabulary, corpus, etc.).

</td> </tr>  
<tr>  
<td>

Domain

</td>  
<td>

topic covered by the dataset (law, education, culture, government, etc.).

</td> </tr>  
<tr>  
<td>

Identifiers

</td>  
<td>

other type of identifiers assigned to the dataset (ISRN, DOI, Standard ID,
etc.).

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

a brief description of the content of the dataset.

</td> </tr>  
<tr>  
<td>

Availability

</td>  
<td>

if the dataset is available online, upon request or not available.

</td> </tr>  
<tr>  
<td>

Languages

</td>  
<td>

languages in which the content of the dataset are available.

</td> </tr>  
<tr>  
<td>

Creator

</td>  
<td>

author or institution that created the dataset.

</td> </tr>  
<tr>  
<td>

Publisher

</td>  
<td>

institution publishing the dataset.

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

license of the dataset (Creative Commons, or others).

</td> </tr>  
<tr>  
<td>

Other rights

</td>  
<td>

if the dataset contains personal information.

</td> </tr>  
<tr>  
<td>

Jurisdiction

</td>  
<td>

jurisdiction where the dataset applies (if necessary).

</td> </tr>  
<tr>  
<td>

Date of this entry

</td>  
<td>

date of registration of the dataset in the CKAN.

</td> </tr>  
<tr>  
<td>

Proposed by

</td>  
<td>

Lynx partner or Lynx organisation proposing the dataset.

</td> </tr>  
<tr>  
<td>

Number of entries

</td>  
<td>

number of terms, triplets or entries that the dataset contains.

</td> </tr>  
<tr>  
<td>

Last update

</td>  
<td>

date in which the last modification of the dataset took place.

</td> </tr>  
<tr>  
<td>

Dataset organisation

</td>  
<td>

</td> </tr>  
<tr>  
<td>

name of the Lynx organisation registering the dataset.

</td> </tr> </table>

**Table 1.** Fields describing a data asset

The second block of metadata (whose fields are listed in Table 2) gives
additional information about the resource in which the metadata can be
accessed. This section is repeated as many times as needed (depending on the
number of formats of the metadata).  
  
<table>  
<tr>  
<th>

**Field**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Description

</td>  
<td>

description of the type of resource (i.e. downloadable file, SPARQL endpoint,
website search application, etc.).

</td> </tr>  
<tr>  
<td>

Data format

</td>  
<td>

the format of the resource (RDF, XML, SKOS, CSV, etc.).

</td> </tr>  
<tr>  
<td>

</td>  
<td>

technology used to expose the resource (relational database, API, linked data,
etc.).

</td> </tr>  
<tr>  
<td>

Data access

</td> </tr>  
<tr>  
<td>

Open format

</td>  
<td>

if the format of the resource is open or not.

</td> </tr>  
<tr>  
<td>

URI

</td>  
<td>

the URI pointing to the different resources.

</td> </tr> </table>

**Table 2.** Fields describing a resource associated to a data asset

The template was materialized as a spreadsheet distributed among the partners.

###  3.1.2 Lynx Data Portal

With the aim of publishing the metadata of the harvested datasets, a data
portal has been made available under http://data.lynx-project.eu.

This data portal uses the technology of CKAN. The Comprehensive Knowledge
Archive Network (CKAN) is a web-based management system for the storage and
distribution of open data. The system is open source  12  , and it has been
deployed on the UPM servers using containerization technologies –Rancher  13
, a leading solution to deploy Docker containers in a Platform as a Service
(PaaS).

The CKAN open data portal gives access to the resources gathered by all the
members of the Lynx project. In the same way, members are able to register and
describe their harvested resources to jointly create the Lynx Open Data
Portal. To correctly display the relevant information about the datasets, CKAN
application uses the metadata described in Section 4.2.1. As a result, each
dataset presents the interface as shown by Figure 5 .

**Figure 6.** Screenshot of the Lynx Data Portal

The “Data and Resources” section corresponds to the “Resource information”
metadata block and “Additional Info” contains the metadata of the “Dataset
information” table.

The CKAN data portal allows faceted browsing, with filters such as language,
format and jurisdiction. At this moment, there are 26 datasets classified in
the CKAN, but this number will grow. For the metadata records to be correctly
displayed on the website, it was required to establish a correspondence
between the metadata in the spreadsheet and the structure in the JSON file
that gives shape to the CKAN platform.

In the Lynx Data Portal, each dataset can be accessed through their own URI,
that is built by using the ID of each resource. Datasets IDs are shown in
Table 3, contained in the next section. As a result, dataset URIs look like
the example below, where the ID would be unesco-thesaurus:

http://data.lynx-project.eu/dataset/unesco-thesaurus

The CKAN API enables a direct access to the metadata records. The API is
intended for developers who want to write code that interacts with CKAN sites
and their data, and it is documented online  14  . For example, the method:

http://data.lynx-project.eu/api/rest/dataset/unesco-thesaurus

will return the following answer:

{"license_title": null, "maintainer": null, "private": false,
"maintainer_email": null, "num_tags": 0, "id":

"efaf72c9-f8da-4257-b77e-c1f90952d71a", "metadata_created":
"2018-04-11T08:35:41.813169", "relationships": [],

"license": null, "metadata_modified": "2018-04-11T08:39:59.429186", "author":
null, "author_email": null,

"download_url": "http://skos.um.es/sparql/", "state": "active", "version":
null, "creator_user_id": "3b131ddc-4bbf-

42ff-9c33-ee1c4f7adb5c", "type": "dataset", "resources": [{"Distribuciones":
"SPARQL endpoint", "hash": "",

"description": "SPARQL endpoint", "format": "SKOS", "package_id":
"efaf72c9-f8da-4257-b77e-c1f90952d71a",

"mimetype_inner": null, "url_type": null, "formatoabierto": "", "id":
"2a610dc8-15cd-4f17-aee0-149201c427cd",

"size": null, "mimetype": null, "cache_url": null, "name": "SPARQL endpoint",
"created": "2018-04-

11T08:39:13.979840", "url": "http://skos.um.es/sparql/", "cache_last_updated":
null, "last_modified": null,

"position": 0, "resource_type": null}, {"Distribuciones": "Downloadable
files", "hash": "", "description":

"Downloadable files in RDF and Turtle.", "format": "RDF", "package_id":
"efaf72c9-f8da-4257-b77e-c1f90952d71a",

"mimetype_inner": null, "url_type": null, "formatoabierto": "", "id":
"81ddd071-4018-4850-b5d8-04b4f5badd7d",

"size": null, "mimetype": null, "cache_url": null, "name": "Downloadable
files", "created": "2018-04-

11T08:39:59.170137", "url": "http://skos.um.es/unescothes/downloads.php",
"cache_last_updated": null,

"last_modified": null, "position": 1, "resource_type": null}],
"num_resources": 2, "tags": [], "groups": [],

"license_id": null, "organization": {"description": "", "title": "OEG",
"created": "2018-04-05T08:10:35.821305",

"approval_status": "approved", "is_organization": true, "state": "active",
"image_url": "", "revision_id":

"66f3c9c3-9bdf-4ebe-8ed2-54b4aea30375", "type": "organization", "id":
"d4250a6e-d1d4-4a2d-8e40-b663271d8404", "name": "oeg"}, "name": "unesco-
thesaurus", "isopen": false, "notes_rendered": "<p>The UNESCO Thesaurus is a
controlled and structured list of terms used in subject analysis and retrieval
of documents and publications in several fields.</p>", "url": null,
"ckan_url": "http://data.lynx-project.eu/dataset/unesco-thesaurus", "notes":
"The UNESCO Thesaurus is a controlled and structured list of terms used in
subject analysis and retrieval of documents and publications in several
fields.\r\n", "owner_org": "d4250a6e-d1d4-4a2d-8e40-b663271d8404",
"ratings_average": null, "extras": {"lkg_type": "language", "domain":
"Education, Science, Culture, Politics, Countries, Information",
"total_number": "4408 (skos concepts)", "language": "en, es, fr, ru",
"creator": "Research group of Information

Technology (University of Murcia)", "publisher": "UNESCO", "jurisdiction": "",
"other_rights": "no", "last_update": "2015", "licence": "Creative Commons 3.0,
https://creativecommons.org/licenses/by-nc-sa/3.0/deed.es_ES", "date":

"11/04/18", "partner": "UPM", "identifier": "", "availability": "online"},
"ratings_count": 0, "title": "UNESCO Thesaurus", "revision_id":
"67553ea8-aa13-4dfe-905d-eb499d2d78e9"}

##  3.2 TRANSFORMATION OF RESOURCES

The minimum content of the LKG is the collection of datasets necessary for the
execution of the Lynx pilots that are published as linked data. Whereas
transformation of resrouces to linked data is not a central activity of Lynx,
the project foresees that some resources will exist but not as linked data,
and a transformation process will be necessary.

The cycle of activities usually made when publishing linked data (see Figure
7) is

**Figure 7.** Usual activities for publishing linked data. Figure taken from
[25].

Whereas the specification is derived from the pilots and the use case needs,
the modelling process will lean on existing data models, to be harmonized as
described in Section 4.2. The generation of linked data will be the
transformation of existing resources. These transformation will be different
depending on the source format:

 From unstructured text, extraction tools (PoolParty, OpenCalais,
SketchEngine etc.) will be used, before creating the entities.

 From relational databases, technologies such as R2RML exist, but no relation
database is expected to be necessary.

 For tabular data, Open Refine and similar tools will be used.

The publication means is to be decided but it will be made using either
PoolParty or Open Link Virtuoso in local servers.

##  3.3 INITIAL CATALOGUE OF DATASETS

This section contains only preliminary information, and it will be completed
by M18 with D2.4.

###  3.3.1 Datasets in the regulatory domain

These are the initially identified datasets in the regulatory domain:

 Eur-Lex: Database of legal information containing: EU law (EU treaties,
directives, regulations, decisions, consolidated legislation, etc.)
preparatory acts (legislative proposals, reports, green and white papers,
etc.), EU case-law (judgments, orders, etc.), international agreements, etc. A
huge database updated daily with some texts dating back to 1951.

 Openlaws: Austrian laws (federal laws and of the 9 regions) and rulings
(from 10 different courts), German federal laws, European laws (regulations,
directives) and rulings (general court, European Court of Justice). It
includes Eur-Lex, 11k national acts and 300k national cases in a neo4j graph.
 DNV-GL: Standards, regulations and guidelines to the public, usually in PDF.

###  3.3.2 Datasets in the language domain

Using the methodology described in Section 4.2, several sites and repositories
have been surveyed. One of the sources of most interest for linguistic open
data is the Linked Open Data Cloud  15  or LOD cloud, due to its open nature
and its adequate format as linked data or RDF. In particular, the Linguistic
Linked Open Data Cloud  16  is a subset of the LOD cloud which provides
exclusively linguistic resources sorted by typology. Different types of
datasets in the Linguistic Linked Open Data Cloud are:

 Corpora

 Terminology, thesauri and Knowledge Bases

 Lexicons and Dictionaries

 Linguistic Resource Metadata

 Linguistic Data Categories

 Typological Databases

Within this project, the three first types of resources have been shortlisted
as the most useful.

Besides consuming linked data or RDF in general, other valuable non-RDF
resources can be included in the graph, possibly once converted to RDF. Many
non-RDF resources of interest in this context can be found in data portals
like the European Data Portal, the Library of Congress or the Termcoord public
portal, which is of particular interest for the multilingual glossaries in the
domain of law.

Due to the huge amount of information and open data available nowadays, it is
essential to establish these limits to gather only the relevant resources. In
the case that more types of datasets are required, they will be harvested at a
later stage. Thus, some of the resources already published as linked data and
that have been identified as of interest for Lynx are listed below:

 STW Thesaurus for Economics: a thesaurus that provides a vocabulary on any
economic subject. It also contains terms used in law, sociology and politics
(monolingual in English) [30].

 Copyright Termbank: a multilingual term bank of copyright-related terms that
has been published connecting WIPO definitions, IATE terms and definitions
from Creative Commons licenses

(multilingual) .

 EuroVoc: a multilingual and multidisciplinary thesaurus covering the
activities of the EU. It is not specifically legal, but it contains pertinent
information about the EU and their politics and law (multilingual).

 AGROVOC: a controlled vocabulary covering all the fields of the Food and
Agriculture Organization (FAO) of the United Nations. It contains general
information and it has been selected since it shares many structures with
other important resources (multilingual).

 IATE: a terminological database developed by the EU which is constantly
being updated by translators and terminologists. Amongst other domains, the
terms are related with law and EU governments (multilingual). A transformation
to RDF was made in 2015.

Resources published in other formats have been considered as well. Structured
formats include TBX (used for term bases), CSV and XLS. Exceptionally,
resources published in non-machine-readable formats might be considered.

Consequently, the following resources published by the EU have also been
listed as usable, although they are not included in the Linguistic Linked Open
Data Cloud:

 INSPIRE Glossary: a term base developed by the INSPIRE Knowledge Base of the
European Union. Although this project is related with the field of spatial
information, the glossary contains general terms and definitions that specify
the common terminology used in the INSPIRE Directive and in the INSPIRE
Implementing Regulations (monolingual, en).

 EUGO Glossary: a term base addressed to companies and entrepreneurs that
need to comply with administrative or professional requirements to perform a
remunerated economic activity in Spain. This glossary is part of a European
project and contains terms about regulations that are valuable for Lynx
purpose (monolingual in Spanish).

 GEMET: a general thesaurus, conceived to define a common general language to
serve as the core of general terminology for the environment. This glossary is
available in RDF and it shares terms and structures with EuroVoc
(multilingual).

 Termcoord: a portal supported by the European Union that contains glossaries
developed by the different institutions. These glossaries cover several fields
including law, international relations and government. Although the resources
are available in PDF, at some point these documents could be treated and
transformed into RDF if necessary (multilingual).

In the same way, the United Nations also counts with consolidated
terminological resources. Given their intergovernmental domain, the following
resources have been selected:

 UNESCO Thesaurus: a controlled list of terms intended for the subject
analysis of texts and document retrieval. The thesaurus contains terms on
several domains such as education, politics, culture and social sciences. It
has been published as a SKOS thesaurus and can be accessed through a SPARQL
endpoint (multilingual).

 InforMEA Glossary: a term bank developed by the United Nations and supported
by the European Union with the aim of gathering terms on Environmental Law and
Agreements. It is available as RDF and it will be upgraded to a thesaurus
during the following months (multilingual).

 International Monetary Fund Glossary: a terminology list containing terms on
economics and public finances related with the European Union. It is available
as a PDF downloadable file; however, it may be transformed as a future work
(multilingual).

On the other hand, other linguistic resources (not supported by the EU nor the
UN) have been spotted. Some of them are already converted into RDF:

 Termcat (Terminologia Oberta): a set of terminological databases supported
by the government of Catalonia. They contain term equivalents in several
languages. Part of these terminological databases were converted into RDF
previously and are part of the TerminotecaRDF project. They can be accessed
through a SPARQL endpoint (multilingual).

 German Labour Law Thesaurus: a thesaurus that covers all main areas of
labour law, such as the roles of employee and employer; legal aspects around
labour contracts. It is available through a SPARQL endpoint and as RDF
downloadable files (monolingual, de).

 Jurivoc: a juridical thesaurus developed by the Federal Supreme Court of
Switzerland in cooperation with Swiss legal libraries. It contains juridical
terms arranged in a monohierarchic structure (multilingual).

 SAIJ Thesaurus: a thesaurus that organises legal knowledge through a list of
controlled terms which represent concepts. It is available in RDF and intended
to ease users’ access information related to the argentine legal system that
can be found in a file or in a documentation centre (monolingual, es).

 CaLaThe: a thesaurus for the domain of cadastre and land administration that
provides a controlled vocabulary. It is interesting because it shares
structures and terms with AGROVOC and the GEMET thesaurus, and it can be
downloaded as an RDF file (monolingual, en).

 CDISC Glossary: a glossary contains definitions of terms and abbreviations
that can be relevant for medical laws and agreements It is available in
several formats, including OWL (monolingual, en).

Finally, one last resource available in other PDF has also been considered due
to different facts:

 Connecticut Glossary: a glossary that contains legal terms published by the
Judicial Branch of the State of Connecticut. It can be transformed into a
machine-readable format and from there into RDF since it provides with
equivalences of legal terms from English into Spanish (bilingual).

Table 3 lists all the resources as a review of the information presented
above. On the other hand, the set of the identified linguistic resources has
also been represented in an interactive graph, in which each dataset is
coloured as per the domain it covers (Figure 8). A second version of the graph
has also been created in order to make a distinction between those datasets in
RDF (green) and those in different formats (grey) (Figure 9). The graph also
represents the relations between each asset, since most of those in RDF share
structures and terms.

**ID Name Description Language**  
  
<table>  
<tr>  
<th>

**iate**

</th>  
<th>

IATE

</th>  
<th>

EU terminological database.

</th>  
<th>

EU languages

</th> </tr>  
<tr>  
<td>

**eurovoc**

</td>  
<td>

Eurovoc

</td>  
<td>

EU multilingual thesaurus.

</td>  
<td>

EU languages

</td> </tr>  
<tr>  
<td>

**eur-lex**

</td>  
<td>

EUR-Lex

</td>  
<td>

EU legal corpora portal.

</td>  
<td>

EU languages

</td> </tr>  
<tr>  
<td>

**conneticutlegal-glossary**

</td>  
<td>

Conneticut Legal

Glossary

</td>  
<td>

Bilingual legal glossary.

</td>  
<td>

en, es

</td> </tr>  
<tr>  
<td>

**unescothesaurus**

</td>  
<td>

UNESCO Thesaurus

</td>  
<td>

Multilingual multidisciplinary thesaurus.

</td>  
<td>

en, es, fr, ru

</td> </tr>  
<tr>  
<td>

**library-ofcongress**

</td>  
<td>

Library of Congress

</td>  
<td>

Legal corpora portal.

</td>  
<td>

en

</td> </tr>  
<tr>  
<td>

**imf**

</td>  
<td>

International Monetary Fund

</td>  
<td>

Economic multilingual terminology.

</td>  
<td>

en, de, es

</td> </tr>  
<tr>  
<td>

**eugo-glossary**

</td>  
<td>

EUGO Glossary

</td>  
<td>

Business monolingual dictionary.

</td>  
<td>

es

</td> </tr>  
<tr>  
<td>

**cdisc-glossary**

</td>  
<td>

CDISC Glossary

</td>  
<td>

Clinical monolingual

</td>  
<td>

en

</td> </tr>  
<tr>  
<td>

**stw**

</td>  
<td>

STW Thesaurus for Economics

</td>  
<td>

Economic monolingual thesaurus.

</td>  
<td>

en

</td> </tr>  
<tr>  
<td>

**edp**

</td>  
<td>

European Data

Portal

</td>  
<td>

EU datasets.

</td>  
<td>

EUlanguages

</td> </tr>  
<tr>  
<td>

**inspire**

</td>  
<td>

INSPIRE Glossary

(EU)

</td>  
<td>

General terms and definitions in

English.

</td>  
<td>

en

</td> </tr>  
<tr>  
<td>

**saij**

</td>  
<td>

SAIJ Thesaurus

</td>  
<td>

Controlled list of legal terms.

</td>  
<td>

es

</td> </tr>  
<tr>  
<td>

**calathe**

</td>  
<td>

CaLaThe

</td>  
<td>

Cadastral vocabulary

</td>  
<td>

en

</td> </tr>  
<tr>  
<td>

**gemet**

</td>  
<td>

GEMET

</td>  
<td>

General multilingual thesauri.

</td>  
<td>

en, de, es, it

</td> </tr>  
<tr>  
<td>

**informea**

</td>  
<td>

InforMEA Glossary (UNESCO)

</td>  
<td>

Monolingual glossary on environmental law.

</td>  
<td>

en

</td> </tr>  
<tr>  
<td>

**copyrighttermbank**

</td>  
<td>

Copyright Termbank

</td>  
<td>

Multi-lingual term bank of copyrightrelated terms

</td>  
<td>

en, es, fr, pt

</td> </tr>  
<tr>  
<td>

**gllt**

</td>  
<td>

German labour law thesaurus

</td>  
<td>

Thesaurus with labour law terms.

</td>  
<td>

de

</td> </tr>  
<tr>  
<td>

**jurivoc**

</td>  
<td>

Jurivoc

</td>  
<td>

Juridical terms from Switzerland.

</td>  
<td>

de, it, fr

</td> </tr>  
<tr>  
<td>

**termcat**

</td>  
<td>

Termcat

</td>  
<td>

Terms from several fields including law.

</td>  
<td>

ca, en, es, de,

fr, it

</td> </tr>  
<tr>  
<td>

**termcoord**

</td>  
<td>

Termcoord

</td>  
<td>

Glossaries from EU institutions and bodies.

</td>  
<td>

EU languages

</td> </tr> </table>

**agrovoc** Agrovoc Controlled general vocabulary. 29 languages

**Table 3.** Initial set of resources gathered.

**Figure 8.** Datasets represented by domain.

**Figure 9.** Datasets represented by format.

#  4 DATA MODELS

##  4.1 INTRODUCTION

###  4.1.1 Data models in the regulatory domain

A number of vocabularies and ontologies for documents in the legal domain has
been published in the last few years. Núria Casellas surveyed 52 legal
ontologies in 2011 [18], and in the meantime many other new ontologies have
appeared, but in practice, only a few of them have direct interest for the
LKG, as not every published legal ontology is created with the intention of
supporting data models. Some ontologies had the intent of formalizing abstract
conceptualizations. For example, ontology design patterns in the legal domain
have been explored [17] –but these works have little interest for supporting
data publication.

The XML schema Akoma Ntoso  17  was initially funded by the United Nations to
become some years later an OASIS specification as Legal RuleML  18  . MetaLex
[12] was an XML vocabulary for the encoding of the structure and content of
legislative documents, which included in newer versions functionality related
to timekeeping and version management. The European Committee for
Standardization (CEN) adopted MetaLex and evolved the schema to an OWL
ontology. MetaLex was extended in the context of the FP6 ESTRELLA project
(2006-2008) which developed a network of ontologies known as Legal Knowledge
Interchange Format (LKIF). The LKIF ontologies are still available and a
reference in the area  19  [14]. Licenses used for the publication of
copyrighted work have been modelled with the ODRL (Open Digital Rights
Language) language [27].

The European Legislation Identifier (ELI) is a system to make legislation
available online in a standardised format, so that it can be accessed,
exchanged and reused across border [13]. ELI describes a new common framework
to unify and link national legislation with European legislation. ELI, as a
framework, proposes a URI template for the identification of legal resources
on the web and it also provides an OWL ontology for supporting the
representation of metadata of legal events and documents. The European Case
Law Identifier (ECLI), much like ELI, was introduced recently for modelling
case laws. The BO-ECLI project, funded under the Justice Programme of the
European Union (2015-2017), aimed to broaden the use of ECLI and to further
improve the accessibility of case law.

###  4.1.2 Data models in the linguistic domain

Similarly, a large amount of language resources can already be found across
the Semantic Web. Such datasets are represented with various schemas,
depending on given factors such as the inner structure of the dataset,
language, content or the objective of its publication, to mention but a few.
_Simple Knowledge Organization System_ ( _SKOS_ ) is aimed to represent the
structure of organization systems such as thesauri and taxonomies, since they
share many similarities. It is widely used within the Semantic Web context,
since it provides an intuitive language and can be combined with formal
representation languages such as the Web Ontology Language (OWL). _SKOS XL_
works as an extension of SKOS to represent lexical information [23].

With regard to multilingualism in ontologies, _Linguistic Information
Repository_ ( _LIR_ ) was proposed as model for ontology localisation: it
grants the localisation of the ontology terminological layer, without
modifying the ontology conceptualisation. LIR allows enriching ontology
entities with the linguistic information necessary for the localisation and
cultural adaptation of the ontology [24].

Another model intended for the representation of linguistic descriptions
associated to ontology concepts is _Lexinfo_ [20]. It contains a complete
collection of linguistic categories. Currently, it is used in combination with
other models such as Ontolex (described in the next paragraph), to describe
the properties of the linguistic objects that describe ontology entities.
Other repositories of linguistic categories are ISOcat  20  , OLiA  21  or
GOLD  22  .

The _Lexicon Model for Ontologies_ or _lemon_ [26] was especially created to
represent lexical information in the Semantic Web, covering some needs that
previous models did not. This model has evolved in the context of a W3C
Community Group into _lemon-Ontolex_ first, now better known as _Ontolex_ 23
. In this model, linguistic descriptions are as well separated from the
ontology, and point to the corresponding concept in the ontology. The
structure of this model is divided into a core set of classes and different
modules containing various types of linguistic information that range from
morpho-syntactic properties of lexical entries, lexical and terminological
variation and translation, decomposition of phrase structures, syntactic
frames and mappings to the ontological predicates, and morphological
decomposition of lexical forms. Linguistic annotations such as data categories
and linguistic descriptors are not captured in the model but referred to by
pointing to models that contain them (see LexInfo model above).

##  4.2 STRATEGY FOR THE HARMONISATION OF DATA MODELS IN LYNX

The LKG needs a uniform collection of data models in order to integrate
heterogeneous resources. The definition of these data models will be provided
in Deliverable 2.4.

In order to select the data models, a simultaneous top down and bottom up
approaches will be conducted, as illustrated by Figure 11. A parallel work is
carried out, where in the one hand a top down approach is conducted,
extracting a list of formats, vocabularies and ontologies which can be chosen
to satisfy the functional requirements of the pilots, whereas in the other
hand a bottom up approach is followed, exploring every possible format,
vocabulary or ontology of interest, with special attention to the most widely
spread ones.

Identification of vocabularies

and ontologies in the domain

Generation of minimal

metadata description

Publication in the Lynx web as

a catalogue of vocabularies

Analysis of functional

requirements

Analysis of technical

requirements

Identification of vocabularies

and formats necessary

Selection of vocabularies

and ontologies

Top down approach

An analysis of the functional and

technical requirements of the pilots

determines a list of vocabularies and

ontologies of choice

Bottom up approach

A survey of ontologies and vocabularies

tries to comprehensively identify the

most widely spread formats

**Figure 10.** Strategy for the selection of data models in Lynx

#  5 THE MULTILINGUAL LEGAL KNOWLEDGE GRAPH

As stated in the introduction, a secondary goal of this document is to define
the Legal Knowledge Graph that will be developed during the Lynx project with
a linguistic regulatory Linked Open Data Cloud.

##  5.1 SCOPE OF THE LEGAL KNOWLEDGE GRAPH

The amount of legal data made accessible either in open or under payment
modalities by legal information providers can be hardly imagined. Lexis Nexis
claimed  24  to have 30 Terabytes of content, WestLaw accounted for more than
40,000 _databases_ . Their value can be roughly estimated: as of 2012, the
four big players (WestLaw, Lexis Nexis, Wolters Kluwer and Bloomberg Legal)
totalled about $10,000M in revenues. Language data (e.g. resources with any
kind of linguistic information) belongs to a much smaller domain, but still,
unmanageable as a whole.

The Lynx project is interested in a small fraction of the information
belonging to these domains. In particular, Lynx is in principle interested
only in using the data necessary to provide the compliance services described
in the pilots. Data of interest is regulatory data (legal and standards-
related) and language data (to cover the multilingual aspects of the
services). The intersection of these domains is of the utmost interest and
Lynx will try to comprehensively identify every possible open dataset in this
core category. These ideas are represented in Figure 4.

Language data

Legal data

Legal data for

compliance in

the Lynx pilots

Language data

for compliance in

the Lynx pilots

**Lynx core**

linguistic legal data.

Corpora

TerminologIcal databases

Thesauri, glossaries

Lexicons and dictionaries

Linguistic resource metadata

Typological databases

Law

Case law

Opinions, recommendations

Doctrine, books, journals

Standards, technical norms

Sectorial good practices

**Figure 11.** Scope of the multilingual Legal Knowledge Graph

The definitions of both _language data_ and _regulatory data_ are indeed
fuzzy, but flexible as to introduce data of many different kinds whenever
necessary (geographical data, user information, etc.). Because data in the
Semantic Web is indissociable from the data models, and data models are
accessed in the same manner as data is, ontologies and vocabularies are part
of the LKG as well. Moreover, any kind of metadata (describing documents,
standards etc.) is also part of the LKG, as well as the description of the
entities producing the documents (courts, users, jurisdictions). In order to
provide the compliance services, and with different degree of interest, both
primary and secondary law are of use, and any relevant document in a wide
sense may become part of the Legal Knowledge Graph. This is illustrated in
Figure 5.

**Figure 12.** Types of information in the Legal Knowledge Graph

##  5.1 KNOWLEDGE GRAPHS

In the realm of Artificial Intelligence, a knowledge graph is a data structure
to represent information, where entities are represented as nodes, their
attributes as node labels and the relationship between entities are
represented as edges. Knowledge graphs such as Google’s  25  , Freebase [2]
and WordNet [3] turn data into knowledge, and they have become important
resources for many AI and NLP applications such as information search, data
integration, data analytics, question answering or context-sensitive
recommendations.

Large knowledge graphs include millions of concepts and billions of
relationships. For example, DBpedia describes about 30M entities connected
through 10,000M relationships. Entities belong to classes described in
ontologies. There are different manners of representing knowledge graphs, not
the least important being the one using W3C specifications of the Semantic
Web: RDF, RDFS, OWL. RDF data is accessible online in different forms: as file
dumps, through a SPARQL endpoints or dedicated APIs or simply published online
as Linked Data [4].

###  5.1.1 Legal Knowledge Graphs

In the last few years, a number of Legal Knowledge Graphs have been created in
different applications. The MetaLex Document Server offers legal documents as
versioned Linked Data [10], including Dutch national regulations. Finnish [9]
and Greek [8] legislation are also offered as Linked Data.

The Publications Office of the EU maintains the central content and metadata
CELLAR repository for storing official publications and bibliographic
resources produced by the institutions of the EU [11]. The content of CELLAR,
which includes EU legislation, is made publicly available by the Eur-Lex
service and it offers also an SPARQL endpoint.

The FP7 EUCases project (2013-2015) offered European and national case law and
legislation linked in an open data stack (http://eucases.eu).

Finally, Openlaws offers a platform based on linked open data, open source
software and open innovation processes [5][6][7]. Lynx will benefit from the
expertise of Openlaws, which will be the preferred source for the data models,
methods and algorithms. New H2020 projects in the area of data protection are
also using semantic web technologies, such as the H2020 Special  26  , devoted
to ease the collection of user consents and represent policies as RDF or the
H2020 Mirel  27  (2016-2019), with a network of experts to define a formal
framework and to develop tools for mining and reasoning with legal texts, or
e-Compliance, an FP7 project (2013-2016), focused on using semantic web
technologies for regulatory compliance in the maritime domain.

###  5.1.2 Linguistic Knowledge Graphs

In the last few years, the language technology community has shaped the
Linguistic Linked Open Data Cloud: the graph with those language resources
available in RDF and published as Linked Data [16]. The graph represented in
Figure 6, resembles the one of the Linked Data Cloud, but limited to the
language domain.

**Figure 13.** Linguistic Linked Open Data Cloud  28

A major resource contained in this graph is _DBpedia_ , a vast network that
structures data from Wikipedia and links them with other datasets available on
the Web [3]. The result is published as Open Data available for the
consumption of both humans and machines. Different versions of DBpedia exist
for different languages.

Another core resource in the LOD Cloud is _BabelNet_ [15], a huge multilingual
semantic network, generated automatically from various resources and
integrating the lexicographical information of _WordNet_ and the encyclopaedic
knowledge of Wikipedia. BabelNet also applies Machine Translation to get
information from several languages. As a result, BabelNet is considered an
encyclopaedic dictionary that contains concepts and named entities connected
thanks to a great amount of semantic relations.

_Wordnet_ , is one of the best known Linguistic Knowledge Graphs, since it is
a large online lexical database that contains nouns, verbs, adjectives and
adverbs in English [3]. These words are organised in sets of synonyms that
represent concepts, known as _synsets_ . WordNet uses these synonyms to
represent word senses; thus, synonymy is WordNet’s most important relation.
Four additional relations are also used by this network: antonymy (opposing-
name), hyponymy (sub-name), meronymy (part-name), troponymy (manner-name) and
entailment relations. Other resources equivalent to WordNet have been
published for different languages, such as EuroWordNet [29].

However, there are other semantic networks (considered linguistic knowledge
graphs) that do not appear in the LOD Cloud but are also worth to mention.
This is the case of _ConceptNet_ [28], a semantic network designed to
represent common sense and support textual reasoning about documents in the
real word. It represents part of human experiences and tries to share this
common-sense knowledge with machines. ConceptNet is often integrated with
natural language processing applications to speed up the enrichment of AI
systems with common sense [4].

###  5.1.3 The Lynx Multilingual Legal Knowledge Graph

Building on these previous experiences, we are in the position to define the
Lynx Multilingual Legal Knowledge Graph.

The **Lynx Multilingual Legal Knowledge Graph (LKG)** is a knowledge graph
using W3C specifications with the necessary information to provide
multilingual compliance services. The Lynx LKG builds on previous initiatives
reusing open data and will evolve adding new resources whenever needed to
provide compliance services. The LKG preferred form of publication is Linked
Data, although other access mechanisms will be provided.