# Dimensions of Commonsense Knowledge

arXiv: https://arxiv.org/abs/2101.04640 · HTML: https://mlsanity.com/paper/2101.04640/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 100 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 30 of 36)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models](https://mlsanity.com/paper/1907.13528/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://mlsanity.com/paper/2010.15980/) (2020)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [Commonsense Knowledge Mining from Pretrained Models](https://mlsanity.com/paper/1909.00505/) (2019)
- [Unsupervised Commonsense Question Answering with Self-Talk](https://mlsanity.com/paper/2004.05483/) (2020)
- [Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2004.03829/) (2020)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models](https://mlsanity.com/paper/2005.00683/) (2020)
- [GLUCOSE: GeneraLized and COntextualized Story Explanations](https://mlsanity.com/paper/2009.07758/) (2020)
- [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://mlsanity.com/paper/2004.03066/) (2020)
- [GenericsKB: A Knowledge Base of Generic Statements](https://mlsanity.com/paper/2005.00660/) (2020)
- [ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning](https://mlsanity.com/paper/2005.00771/) (2020)
- [CSKG: The CommonSense Knowledge Graph](https://mlsanity.com/paper/2012.11490/) (2020)
- [WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge](https://mlsanity.com/paper/2005.05763/) (2020)
- [Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering](https://mlsanity.com/paper/1910.14087/) (2019)

## Built on by (most cited first, 29 of 29)

- [Relational World Knowledge Representation in Contextual Language Models: A Review](https://mlsanity.com/paper/2104.05837/) (2021)
- [BRAINTEASER: Lateral Thinking Puzzles for Large Language Models](https://mlsanity.com/paper/2310.05057/) (2023)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [A Study of Situational Reasoning for Traffic Understanding](https://mlsanity.com/paper/2306.02520/) (2023)
- [Inferring Capabilities from Task Performance with Bayesian Triangulation](https://mlsanity.com/paper/2309.11975/) (2023)
- [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](https://mlsanity.com/paper/2212.07425/) (2022)
- [A survey on knowledge-enhanced multimodal learning](https://mlsanity.com/paper/2211.12328/) (2022)
- [Knowledge-enhanced Agents for Interactive Text Games](https://mlsanity.com/paper/2305.05091/) (2023)
- [PaCo: Preconditions Attributed to Commonsense Knowledge](https://mlsanity.com/paper/2104.08712/) (2021)
- [NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge](https://mlsanity.com/paper/2305.04978/) (2023)
- [CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks](https://mlsanity.com/paper/2310.15239/) (2023)
- [Task2Dial: A Novel Task and Dataset for Commonsense enhanced Task-based Dialogue Grounded in Documents](https://mlsanity.com/paper/2204.01061/) (2022)
- [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://mlsanity.com/paper/2302.07926/) (2023)
- [Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning](https://mlsanity.com/paper/2206.03715/) (2022)
- [An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs](https://mlsanity.com/paper/2205.10661/) (2022)
- [Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph Generation](https://mlsanity.com/paper/2305.18668/) (2023)
- [Probing Physical Reasoning with Counter-Commonsense Context](https://mlsanity.com/paper/2306.02258/) (2023)
- [A large-scale evaluation of commonsense knowledge in humans and large language models](https://mlsanity.com/paper/2505.10309/) (2025)
- [LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference](https://mlsanity.com/paper/2505.22848/) (2025)
- [Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering](https://mlsanity.com/paper/2105.13662/) (2021)
- [Generalizable Neuro-symbolic Systems for Commonsense Question Answering](https://mlsanity.com/paper/2201.06230/) (2022)
- [Commonsense Knowledge in Word Associations and ConceptNet](https://mlsanity.com/paper/2109.09309/) (2021)
- [Adversarial Transformer Language Models for Contextual Commonsense Inference](https://mlsanity.com/paper/2302.05406/) (2023)
- [The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges](https://mlsanity.com/paper/2303.02411/) (2023)
- [JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions](https://mlsanity.com/paper/2210.15456/) (2022)
- [Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability](https://mlsanity.com/paper/2303.08291/) (2023)
- [Towards One-Shot Learning for Text Classification using Inductive Logic Programming](https://mlsanity.com/paper/2308.15885/) (2023)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)
- [Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models](https://mlsanity.com/paper/2609.26610/) (2026)

## Abstract (arXiv)

Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that include commonsense knowledge have been designed and constructed over the past decades. Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization. Efforts to consolidate commonsense knowledge have yielded partial success, with no clear path towards a comprehensive solution. We aim to organize these sources around a common set of dimensions of commonsense knowledge. We survey a wide range of popular commonsense sources with a special focus on their relations. We consolidate these relations into 13 knowledge dimensions. This consolidation allows us to unify the separate sources and to compute indications of their coverage, overlap, and gaps with respect to the knowledge dimensions. Moreover, we analyze the impact of each dimension on downstream reasoning tasks that require commonsense knowledge, observing that the temporal and desire/goal dimensions are very beneficial for reasoning on current downstream tasks, while distinctness and lexical knowledge have little impact. These results reveal preferences for some dimensions in current evaluation, and potential neglect of others.