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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Some thoughts on knowledge-enhanced machine learning

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Author(s):
Cozman, Fabio Gagliardi [1] ; Munhoz, Hugo Neri [1]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Escola Politecn, Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 136, p. 308-324, SEP 2021.
Web of Science Citations: 0
Abstract

How can we employ theoretical insights and practical tools from knowledge representation and reasoning to enhance machine learning, and when is it worthwhile to do so? This paper is based on an invited talk delivered at ECSQARU2019 around this question. It emphasizes the knowledge representation and reasoning side of knowledge-enhanced machine learning, looking at a few case studies: the finite model theory of probabilistic languages, the generation of explanations for embeddings, and an ``explainable{''} version of the Winograd Challenge. (C) 2021 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 16/18841-0 - Inference and learning algorithms for probabilistic logic programming
Grantee:Fabio Gagliardi Cozman
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
Grantee:Fabio Gagliardi Cozman
Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
FAPESP's process: 18/09681-4 - Assessment of Winograd schemes and the use of commonsense knowledge for the resolution of ambiguities
Grantee:Hugo Neri Munhoz
Support Opportunities: Scholarships in Brazil - Post-Doctoral