Representing Knowledge about Norms to Reason on Texts
type: Dataset
description: 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.
issued: 2026-05-07T21:19:31.258190
language: en
modified: 2026-05-07T21:19:31.258196
contact point: Organization
  fn: CCSD
keyword: causal-reasoning
keyword: conference-papers
keyword: infoeu-reposemanticsconferenceobject
keyword: infoinfo-aicomputer-science-csartificial-intelligence-csai
keyword: natural-language-understanding
keyword: non-monotonic-logic
keyword: norms
landing page: Proc. 16th European Conference on Artificial Intelligence

Representing Knowledge about Norms to Reason on Texts
type: Distribution
format: HTML
issued: 2026-05-07T21:19:31.261513
modified: 2026-05-07T21:19:31.247138
access URL: https://hal.science/hal-00091633

test_moissonnage_selune
type: Agent