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Mining, Representation and
Reasoning with Temporal
Expressions in the Legal
Domain
María Navas-Loro
mnavas@fi.upm.es
RuleML+RR Doctoral Consortium - July 14, 2017
(Víctor Rodríguez-Doncel, Asunción Gómez-Pérez)
Universidad Politécnica de Madrid (Spain)
THE IDEA
Context
2
Legal documents everywhere
3
Solution: Q&A about time
We want to make these documents understandable,
answering questions about them.
4
?
!
We focus on judgments
and queries related to time.
STATE OF THE ART
Unifing fields
5
NLP
We need to detect temporal expressions in text.
(Tarsqi, HeidelTime)
6
Representation
Then we have to properly represent them.
(Ontologies, TimeGraphs, T-Prolog…)
(OASIS-LegalRuleML, ODRL…)
7
Reasoning
Finally, reasoning will
be needed to find the
answer to the queries.
8
? !
(Allen’s Interval Temporal Logic, Temporal Languages,
N3Logic, Temporal Description Logics …)
Thesis
Goal
Develop and study an integrated framework that
parses judgments, extracts and represents
temporal expressions and reasons upon them.
Main hypothesis
Expressing temporal rules able to accommodate
the findings of the NLP algorithms and enabling
temporal reasoning in an integrated manner would
unleash new possibilities in temporal legal
reasoning.
9
THESIS
Scope and framework
10
Scope: judgments
11
Framework
Resources for dealing with temporal expressions in
the legal domain (rules, semantic frames…)
Temporal extensions for
ODRL and SHACL
Temporal and legal reasoner
QUERY
legal
documents
REASONER
N
L
P
(current date,
location…)
context
ANSWER
12
Framework
Resources for dealing with temporal expressions in
the legal domain (rules, semantic frames…)
Temporal extensions for
ODRL and SHACL
Temporal and legal reasoner
QUERY
legal
documents
REASONER
N
L
P
(current date,
location…)
context
ANSWER
13
Framework
Resources for dealing with temporal expressions in
the legal domain (rules, semantic frames…)
Temporal extensions for
ODRL and SHACL
Temporal and legal reasoner
QUERY
legal
documents
REASONER
N
L
P
(current date,
location…)
context
ANSWER
14
Framework
Resources for dealing with temporal expressions in
the legal domain (rules, semantic frames…)
Temporal extensions for
ODRL and SHACL
Temporal and legal reasoner
QUERY
legal
documents
REASONER
N
L
P
(current date,
location…)
context
ANSWER
15
Evaluation
• Each part has its own validation.
• Statistical performance measurements for NLP annotation
(such as precision and recall)
• Quality and completeness for the designed representation.
• Rate of true answers by
the reasoner.
16
• Final framework will
be tested by end
users: the system
must be able to
answer to real users queries.
AN EXAMPLE
Real case
17
Example (NLP)
We have a piece of a judgment in English
extracted from Eur-Lex.
18
14
On 5 March 2010, Mr Costeja González, a Spanish national
resident in Spain, lodged with the AEPD a complaint against La
Vanguardia Ediciones SL, which publishes a daily newspaper
with a large circulation, in particular in Catalonia (Spain) (‘La
Vanguardia’), and against Google Spain and Google Inc. The
complaint was based on the fact (…)
16
By decision of 30 July 2010, the AEPD rejected the complaint in
so far as it related to La Vanguardia (…)
Example (Representation)
Possible representation of a complaint including
temporal information.
19
Example (Reasoning)
20
When was the complaint against
Google Inc made?
Google Inc
05.03.2010
Google Inc
Example (Reasoning)
21
When was the complaint against
Google Inc made?
Google Inc
05.03.2010
Google Inc
Example (Reasoning)
22
When was the complaint against
Google Inc made?
Google Inc
05.03.2010
Google Inc
Example (Reasoning)
23
When was the complaint against
Google Inc made?
Google Inc
05.03.2010
Google Inc
05.03.2010
Example (Reasoning)
24
When was the complaint against
Google Inc made?
Google Inc
05.03.2010
Google Inc
05.03.2010
It was lodged on 5 March 2010.
CONCLUSION
Expected contributions
25
Conclusions
• An framework that parses judgments, extracts
and represents temporal expressions and
reasons upon them, in an integrated manner.
• Expected resources and innovation for each of
the three parts:
• Tagged corpora, expressions, semantic frames, synonyms,
new NLP techniques...
• Representation extension of existing languages / reuse of
existing ontologies.
• Rules for temporal legal reasoning.
26
Thank you for your attention
27
Bibliography
Tarsqi Toolkit: http://www.timeml.org/tarsqi/toolkit/index.html
HeidelTime: https://github.com/HeidelTime
LegalRuleML: Athan, T., Governatori, G., Palmirani, M., Paschke, A., &
Wyner, A. (2015, July). LegalRuleML: Design principles and
foundations. In Reasoning Web International Summer School (pp. 151-
188). Springer International Publishing.
ODRL: https://www.w3.org/community/odrl/
Time Ontology: https://www.w3.org/TR/owl-time/
Timegraphs: Gerevini, A., Schubert, L., & Schaeffer, S. (1993).
Temporal reasoning in Timegraph I–II. ACM SIGART Bulletin, 4(3), 21-
25.
T-Prolog: Futo, I., & Szeredi, J. (1980). T-Prolog user manual. Institute
for Coordination of Computer Techniques, Budapest.
Allen’s TL: Allen, J. F. (1983). Maintaining knowledge about temporal
intervals. Communications of the ACM, 26(11), 832-843.
N3Logic: Berners-Lee, T., Connolly, D., Kagal, L., Scharf, Y., &
Hendler, J. (2008). N3logic: A logical framework for the world wide
web. Theory and Practice of Logic Programming, 8(3), 249-269.
EUR-Lex: http://eur-lex.europa.eu/homepage.html
28
Mining, Representation and
Reasoning with Temporal
Expressions in the Legal
Domain
María Navas-Loro
mnavas@fi.upm.es
RuleML+RR Doctoral Consortium - July 14, 2017
(Víctor Rodríguez-Doncel, Asunción Gómez-Pérez)
Universidad Politécnica de Madrid (Spain)

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Mining, Representation and Reasoning with Temporal Expressions in the Legal Domain

  • 1. Mining, Representation and Reasoning with Temporal Expressions in the Legal Domain María Navas-Loro mnavas@fi.upm.es RuleML+RR Doctoral Consortium - July 14, 2017 (Víctor Rodríguez-Doncel, Asunción Gómez-Pérez) Universidad Politécnica de Madrid (Spain)
  • 4. Solution: Q&A about time We want to make these documents understandable, answering questions about them. 4 ? ! We focus on judgments and queries related to time.
  • 5. STATE OF THE ART Unifing fields 5
  • 6. NLP We need to detect temporal expressions in text. (Tarsqi, HeidelTime) 6
  • 7. Representation Then we have to properly represent them. (Ontologies, TimeGraphs, T-Prolog…) (OASIS-LegalRuleML, ODRL…) 7
  • 8. Reasoning Finally, reasoning will be needed to find the answer to the queries. 8 ? ! (Allen’s Interval Temporal Logic, Temporal Languages, N3Logic, Temporal Description Logics …)
  • 9. Thesis Goal Develop and study an integrated framework that parses judgments, extracts and represents temporal expressions and reasons upon them. Main hypothesis Expressing temporal rules able to accommodate the findings of the NLP algorithms and enabling temporal reasoning in an integrated manner would unleash new possibilities in temporal legal reasoning. 9
  • 12. Framework Resources for dealing with temporal expressions in the legal domain (rules, semantic frames…) Temporal extensions for ODRL and SHACL Temporal and legal reasoner QUERY legal documents REASONER N L P (current date, location…) context ANSWER 12
  • 13. Framework Resources for dealing with temporal expressions in the legal domain (rules, semantic frames…) Temporal extensions for ODRL and SHACL Temporal and legal reasoner QUERY legal documents REASONER N L P (current date, location…) context ANSWER 13
  • 14. Framework Resources for dealing with temporal expressions in the legal domain (rules, semantic frames…) Temporal extensions for ODRL and SHACL Temporal and legal reasoner QUERY legal documents REASONER N L P (current date, location…) context ANSWER 14
  • 15. Framework Resources for dealing with temporal expressions in the legal domain (rules, semantic frames…) Temporal extensions for ODRL and SHACL Temporal and legal reasoner QUERY legal documents REASONER N L P (current date, location…) context ANSWER 15
  • 16. Evaluation • Each part has its own validation. • Statistical performance measurements for NLP annotation (such as precision and recall) • Quality and completeness for the designed representation. • Rate of true answers by the reasoner. 16 • Final framework will be tested by end users: the system must be able to answer to real users queries.
  • 18. Example (NLP) We have a piece of a judgment in English extracted from Eur-Lex. 18 14 On 5 March 2010, Mr Costeja González, a Spanish national resident in Spain, lodged with the AEPD a complaint against La Vanguardia Ediciones SL, which publishes a daily newspaper with a large circulation, in particular in Catalonia (Spain) (‘La Vanguardia’), and against Google Spain and Google Inc. The complaint was based on the fact (…) 16 By decision of 30 July 2010, the AEPD rejected the complaint in so far as it related to La Vanguardia (…)
  • 19. Example (Representation) Possible representation of a complaint including temporal information. 19
  • 20. Example (Reasoning) 20 When was the complaint against Google Inc made? Google Inc 05.03.2010 Google Inc
  • 21. Example (Reasoning) 21 When was the complaint against Google Inc made? Google Inc 05.03.2010 Google Inc
  • 22. Example (Reasoning) 22 When was the complaint against Google Inc made? Google Inc 05.03.2010 Google Inc
  • 23. Example (Reasoning) 23 When was the complaint against Google Inc made? Google Inc 05.03.2010 Google Inc 05.03.2010
  • 24. Example (Reasoning) 24 When was the complaint against Google Inc made? Google Inc 05.03.2010 Google Inc 05.03.2010 It was lodged on 5 March 2010.
  • 26. Conclusions • An framework that parses judgments, extracts and represents temporal expressions and reasons upon them, in an integrated manner. • Expected resources and innovation for each of the three parts: • Tagged corpora, expressions, semantic frames, synonyms, new NLP techniques... • Representation extension of existing languages / reuse of existing ontologies. • Rules for temporal legal reasoning. 26
  • 27. Thank you for your attention 27
  • 28. Bibliography Tarsqi Toolkit: http://www.timeml.org/tarsqi/toolkit/index.html HeidelTime: https://github.com/HeidelTime LegalRuleML: Athan, T., Governatori, G., Palmirani, M., Paschke, A., & Wyner, A. (2015, July). LegalRuleML: Design principles and foundations. In Reasoning Web International Summer School (pp. 151- 188). Springer International Publishing. ODRL: https://www.w3.org/community/odrl/ Time Ontology: https://www.w3.org/TR/owl-time/ Timegraphs: Gerevini, A., Schubert, L., & Schaeffer, S. (1993). Temporal reasoning in Timegraph I–II. ACM SIGART Bulletin, 4(3), 21- 25. T-Prolog: Futo, I., & Szeredi, J. (1980). T-Prolog user manual. Institute for Coordination of Computer Techniques, Budapest. Allen’s TL: Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843. N3Logic: Berners-Lee, T., Connolly, D., Kagal, L., Scharf, Y., & Hendler, J. (2008). N3logic: A logical framework for the world wide web. Theory and Practice of Logic Programming, 8(3), 249-269. EUR-Lex: http://eur-lex.europa.eu/homepage.html 28
  • 29. Mining, Representation and Reasoning with Temporal Expressions in the Legal Domain María Navas-Loro mnavas@fi.upm.es RuleML+RR Doctoral Consortium - July 14, 2017 (Víctor Rodríguez-Doncel, Asunción Gómez-Pérez) Universidad Politécnica de Madrid (Spain)