# Commonsense mining as knowledge base completion? A study on the impact of novelty

arXiv: https://arxiv.org/abs/1804.09259 · HTML: https://mlsanity.com/paper/1804.09259/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 8 of 8)

- [Distilling Relation Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2110.15705/) (2021)
- [Do Language Models Perform Generalizable Commonsense Inference?](https://mlsanity.com/paper/2106.11533/) (2021)
- [Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths](https://mlsanity.com/paper/2210.07621/) (2022)
- [PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population](https://mlsanity.com/paper/2210.07988/) (2022)
- [Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge](https://mlsanity.com/paper/2105.13607/) (2021)
- [Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering](https://mlsanity.com/paper/2305.17019/) (2023)
- [Probing Commonsense Knowledge in Pre-trained Language Models with Sense-level Precision and Expanded Vocabulary](https://mlsanity.com/paper/2210.06376/) (2022)
- [Commonsense Knowledge Mining from Term Definitions](https://mlsanity.com/paper/2102.00651/) (2021)

## Abstract (arXiv)

Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by the recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method outperforms the previous state of the art on predicting more novel.