Towards a Linked Open Data Cloud of Language Resources in the Legal Domain

Transcription

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

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 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 Thesauri Terminologies Dictionaries Databases Lexicons

10 LEGAL LANGUAGE RESOURCES Language resources: structured linguistic data in machine readable forms Glossaries Thesauri Terminologies Dictionaries Databases 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 First results 36 linguistic datasets General and legal domain Multiple languages Different textual typology Various formats Available in RDF 16 datasets (44%) Available in other formats 6 datasets (17%) Archived resources 14 datasets (39%) 36 datasets

17 CREATION Labour Law Corpora (PDF) Term Extraction Labour Law Glossary (TBX) Data Protection Corpora (TXT) Data Protection Glossary (TBX) Industrial Standards Corpora (PDF) Industrial Standards Glossary (TBX) 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 Industrial Stand. DBpedia Termcat ES Labour Law Linking process BabelNet Termcat EN Data Protect. EuroVoc

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 Patricia Martín-Chozas PhD. Student Ontology Engineering Group, UPM CONSORTIUM

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