# Fusing Context Into Knowledge Graph for Commonsense Question Answering

arXiv: https://arxiv.org/abs/2012.04808 · HTML: https://mlsanity.com/paper/2012.04808/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 30 of 33)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Retrieving Multimodal Information for Augmented Generation: A Survey](https://mlsanity.com/paper/2303.10868/) (2023)
- [KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering](https://mlsanity.com/paper/2110.04330/) (2021)
- [JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering](https://mlsanity.com/paper/2112.02732/) (2021)
- [Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention](https://mlsanity.com/paper/2112.03254/) (2021)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference](https://mlsanity.com/paper/2306.10790/) (2023)
- [DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning](https://mlsanity.com/paper/2208.00635/) (2022)
- [Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer](https://mlsanity.com/paper/2203.07519/) (2022)
- [Leveraging Knowledge in Multilingual Commonsense Reasoning](https://mlsanity.com/paper/2110.08462/) (2021)
- [Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks](https://mlsanity.com/paper/2301.12847/) (2023)
- [AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension](https://mlsanity.com/paper/2203.08992/) (2022)
- [SEMMA: A Semantic Aware Knowledge Graph Foundation Model](https://mlsanity.com/paper/2505.20422/) (2025)
- [Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles](https://mlsanity.com/paper/2211.16504/) (2022)
- [MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation](https://mlsanity.com/paper/2210.07570/) (2022)
- [Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering](https://mlsanity.com/paper/2305.08135/) (2023)
- [ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering](https://mlsanity.com/paper/2211.07065/) (2022)
- [Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification](https://mlsanity.com/paper/2305.05921/) (2023)
- [KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations](https://mlsanity.com/paper/2306.02980/) (2023)
- [Detecting Euphemisms with Literal Descriptions and Visual Imagery](https://mlsanity.com/paper/2211.04576/) (2022)
- [Decouple knowledge from parameters for plug-and-play language modeling](https://mlsanity.com/paper/2305.11564/) (2023)
- [Commonsense Knowledge in Word Associations and ConceptNet](https://mlsanity.com/paper/2109.09309/) (2021)
- [KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering](https://mlsanity.com/paper/2305.08347/) (2023)
- [Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts](https://mlsanity.com/paper/2201.02740/) (2021)
- [Ered: Enhanced Text Representations with Entities and Descriptions](https://mlsanity.com/paper/2208.08954/) (2022)
- [Probing Commonsense Knowledge in Pre-trained Language Models with Sense-level Precision and Expanded Vocabulary](https://mlsanity.com/paper/2210.06376/) (2022)
- [EEE-QA: Exploring Effective and Efficient Question-Answer Representations](https://mlsanity.com/paper/2403.02176/) (2024)
- [Solution of DeBERTaV3 on CommonsenseQA](https://mlsanity.com/paper/2206.05033/) (2022)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)

## Abstract (arXiv)

Commonsense question answering (QA) requires a model to grasp commonsense and factual knowledge to answer questions about world events. Many prior methods couple language modeling with knowledge graphs (KG). However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts. This creates a gap when fusing knowledge graphs into language modeling, especially when there is insufficient labeled data. Thus, we propose to employ external entity descriptions to provide contextual information for knowledge understanding. We retrieve descriptions of related concepts from Wiktionary and feed them as additional input to pre-trained language models. The resulting model achieves state-of-the-art result in the CommonsenseQA dataset and the best result among non-generative models in OpenBookQA.