TY - JOUR
T1 - Some thoughts on knowledge-enhanced machine learning
AU - Cozman, Fabio Gagliardi
AU - Munhoz, Hugo Neri
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - Knowledge representation
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85109146391&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/b48c4ef2-c3e3-3915-8ea5-867c0923737d/
U2 - 10.1016/j.ijar.2021.06.003
DO - 10.1016/j.ijar.2021.06.003
M3 - Artículo
AN - SCOPUS:85109146391
VL - 136
SP - 308
EP - 324
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
SN - 0888-613X
ER -