| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Sao Paulo, Escola Politecn, Sao Paulo - Brazil
Total Affiliations: 1
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| 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 |