# Commonsense Knowledge Graph Reasoning by Selection or Generation? Why?

arXiv: https://arxiv.org/abs/2008.05925 · HTML: https://mlsanity.com/paper/2008.05925/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [On the Explainability of Natural Language Processing Deep Models](https://mlsanity.com/paper/2210.06929/) (2022)
- [Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning](https://mlsanity.com/paper/2108.06743/) (2021)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Do Language Models Perform Generalizable Commonsense Inference?](https://mlsanity.com/paper/2106.11533/) (2021)

## Abstract (arXiv)

Commonsense knowledge graph reasoning(CKGR) is the task of predicting a missing entity given one existing and the relation in a commonsense knowledge graph (CKG). Existing methods can be classified into two categories generation method and selection method. Each method has its own advantage. We theoretically and empirically compare the two methods, finding the selection method is more suitable than the generation method in CKGR. Given the observation, we further combine the structure of neural Text Encoder and Knowledge Graph Embedding models to solve the selection method's two problems, achieving competitive results. We provide a basic framework and baseline model for subsequent CKGR tasks by selection methods.