Abstract

Acquiring commonsense knowledge and reasoning is an important goal in modern natural language processing research. Despite much progress, there is still a lack of understanding (especially at scale) of the nature of commonsense knowledge itself. A potential source of structured commonsense knowledge that could be used to derive insights is ConceptNet. In particular, ConceptNet contains several coarse-grained relations, including ‘HasContext’, ‘FormOf’ and ‘SymbolOf’, which can prove invaluable in understanding broad, but critically important, commonsense notions such as ‘context’. In this article, we present a methodology based on unsupervised knowledge graph representation learning and clustering to reveal and study substructures in three coarse-grained and heavily used relations in ConceptNet. Our results show that, despite having an ‘official’ definition in ConceptNet, many of these coarse …

Date

2023

Authors

Ke Shen, Mayank Kejriwal

Book

Proceedings of SAI Intelligent Systems Conference

Pages

714-734

Publisher

Springer Nature Switzerland