Towards a Linked Open Data Cloud of Language Resources in the Legal Domain - Law Via Internet Patricia Martín-Chozas, Elena Montiel-Ponsoda, Víctor Rodríguez-Doncel 10th October 2018

1. Building the Legal Knowledge Graph
for Smart Compliance Services in Multilingual Europe
http://lynx-project.eu/
Towards a Linked Open Data
Cloud of Language Resources
in the Legal Domain
Patricia Martín-Chozas, Elena Montiel-Ponsoda,
Víctor Rodríguez-Doncel
10th October 2018

2. OVERVIEW
 Lynx Goal: Legal Knowledge Graph (LKG)
 What, why and how?
 Contribution: Linguistic Linked Legal Open Data cloud
(LLLOD)
 Linguistic foundation of the LKG
 Current status
 First approach of the LLLOD
 Future Work
 Improvements and next steps

3. LYNX PROJECT http://lynx-project.eu/
 European project to build smart services towards compliance
 Goal: Legal Knowledge Graph
Figure 1. From Lynx Document of Work

4. WHAT IS A KNOWLEDGE GRAPH?
Knowledge graph:
Set of connected pieces
of information
Figure 2. From Ambiverse website

5. WHY DO WE NEED THE LKG?
 Smart Services: multijurisdictional open legal knowledge in
one single platform
 Access to multilingual information
 Customised recommendations
 Alerts
 Easier compliance for SMEs and large enterprises

6. WHY DO WE NEED THE LKG?
 Smart Services: multijurisdictional open legal knowledge in
one single platform
 Access to multilingual information
 Customised recommendations
 Alerts
 Easier compliance for SMEs and large enterprises
Law for everybody

7. HOW DO WE BUILD THE LKG?
Figure 3. From Lynx website

8. HOW DO WE BUILD THE LKG?
Legal Language Resources
Figure 3. From Lynx website

9. LEGAL LANGUAGE RESOURCES
 Language resources: structured linguistic data in machine
readable forms
Glossaries
Terminologies
Databases
Thesauri
Dictionaries
Lexicons

10. LEGAL LANGUAGE RESOURCES
 Language resources: structured linguistic data in machine
readable forms
Glossaries
Terminologies
Databases
Thesauri
Dictionaries
Lexicons
Legal domain

11. LEGAL LANGUAGE RESOURCES
Figure 4. Entry example from Eurovoc

12. LEGAL LANGUAGE RESOURCES
Figure 5. Entry example from Eurovoc in RDF

13. LINGUISTIC LINKED OPEN DATA CLOUD
 Legal domain is
underrepresented
 Need for a Linguistic Linked
Open Data cloud in the legal
domain
Figure 6. From Linked Open Data website

14. Figure 7. From Lynx website

15. CONTRIBUTION
Identification
• Identification of existing Legal
Language Resources
Creation
• Creation of new Legal
Language Resources
Conversion • Transformation of such resources
into RDF
Linking • Linking with other
resources

16. IDENTIFICATION
 36 linguistic datasets
 General and legal domain
 Various formats
 Multiple languages
 Different textual typology
Available in RDF 16 datasets (44%)
Available in other formats 6 datasets (17%)
Archived resources 14 datasets (39%)
36 datasets
First results

17. CREATION
Labour Law
Glossary (TBX)
Data Protection
Glossary (TBX)
Industrial Standards
Glossary (TBX)
Labour Law
Corpora (PDF)
Data Protection
Corpora (TXT)
Industrial Standards
Corpora (PDF)
Term Extraction
Resulting language resources
Labour Law
Glossary (ES)
Data Protection
Glossary (EN)
Industrial Standards
Glossary (EN)
102 terms 98 terms 109 terms

18. TRANSFORMATION
Figure 8. Example of RDF transformation

19. LINKING
EuroVoc
BabelNet
DBpedia
Linking process
Industrial
Stand.
Data
Protect.
Labour
Law
Termcat
ES
Termcat
EN

20. Figure 9. First approach to the LLLOD

21. FUTURE WORK
 Further transformation of resources into RDF
 Automatically enrich glossaries with:
• Translations
• Definitions
• Usage contexts
 Automatically link glossaries with other resources

22. CONTACT
CONSORTIUM
Patricia Martín-Chozas
PhD. Student
Ontology Engineering Group, UPM
pmchozas@fi.upm.es

23. EXTRA SLIDES

24. DATA PORTAL
Figure 10. Lynx Data Portal

25. OPENREFINE (I)
Figure 11. OpenRefine Skeleton Example (1)

26. OPENREFINE (II)
Figure 12. OpenRefine Skeleton Example (2)

27. SKETCH ENGINE
Figure 13. Example of candidate terms extracted by Sketch Engine

28. LR COMPARISON
Table 1. Language resource comparison

29. AVAILABLE RESOURCES
Table 2. Available resources

30. ARCHIVED RESOURCES
Table 3. Archived resources