Representing Knowledge about Norms to Reason on Texts

Norms are essential to extend inference: inferences based on norms are far richer than those based on logical implica-tions. In the recent decades, much effort has been devoted to rea-son on a domain, once its norms are represented. How to extract and express those norms has received far less attention. Extraction is difficult: as the readers are supposed to know them, the norms of a domain are seldom made explicit. For one thing, extracting norms requires a language to represent them, and this is the topic of this paper. We apply this language to represent norms in the do-main of driving, and show that it is adequate to reason on the causes of accidents, as described by car-crash reports.

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Additional Info

Field Value
Source Proc. 16th European Conference on Artificial Intelligence
Author Kayser, Daniel, Nouioua, Farid
Maintainer CCSD
Last Updated May 7, 2026, 21:19 (UTC)
Created May 7, 2026, 21:19 (UTC)
Identifier hal-00091633
Language en
contributor Laboratoire d'Informatique de Paris-Nord (LIPN) ; Université Paris 13 (UP13)-Institut Galilée-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS)
creator Kayser, Daniel
date 2004-05-07T00:00:00
harvest_object_id 7007a23d-0e16-4a16-bc1d-625e22defbe1
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2024-11-29T00:00:00
set_spec type:COMM