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Implicit everyday knowledge enabling humans and AI systems to understand and infer ordinary situations beyond explicit data and rules.
DESCRIPTION
Commonsense knowledge encompasses basic facts and assumptions about the everyday world that humans typically acquire without formal instruction. In AI, it underpins tasks like natural language understanding, prediction of consequences, and resolving ambiguities. Knowledge bases and datasets, such as ConceptNet and ATOMIC, aim to encode commonsense relations for machine reasoning. Challenges include coverage, cultural context, and robust generalization. Evaluations often use benchmarks in language, vision, and embodied interactions to measure commonsense reasoning. It is central to building trustworthy, helpful AI assistants and agents.
ALTERNATIVE DEFINITIONS
ConceptNet
ConceptNet is a freely-available semantic network of commonsense knowledge, designed to help computers understand the meanings of words that people use.
Highlights a practical, open semantic network explicitly focused on commonsense for computational use.
Allen Institute for AI
ATOMIC is an atlas of everyday commonsense knowledge about causes and effects of events.
Emphasizes causal commonsense relations captured in a structured dataset.
Stanford Encyclopedia of Philosophy
Common sense is a basic ability to perceive, understand, and judge things, which is shared by nearly all people and can reasonably be expected of nearly all people without need for debate.
Philosophical perspective framing the human foundation of commonsense used in AI.
RELATED TERMS
Knowledge Graph Knowledge Representation Common Sense Reasoning ConceptNet World Model Inference Natural Language Understanding Embodied AI Multimodal Learning Reasoning
COMPETING TERMS
RELATIONS
- Broader: Knowledge
KNOWLEDGE AREAS
AI Data Science Semantic & Knowledge Modeling AI Governance
TARGET AUDIENCE
AI Engineer Data Scientist Data Modeler Data Architect
EXTERNAL SOURCES
- Wikipedia - Common sense
- ConceptNet - Open commonsense knowledge graph
- Allen Institute for AI - Causal commonsense dataset
- Papers with Code - Community overview of ATOMIC
TOP QUESTIONS
Why is Commonsense Knowledge important for AI?
It enables AI to fill gaps, infer plausible outcomes, and interpret ambiguous input like humans do. Without it, systems struggle with everyday reasoning and robust generalization.
Category: Relevancy
What is a common misunderstanding about Commonsense Knowledge?
Many assume facts are trivial, but encoding and generalizing everyday assumptions across contexts, cultures, and modalities is complex and data-intensive.
Category: Misunderstanding
How is Commonsense Knowledge represented in practice?
Graph structures, ontologies, and causal templates encode relations like cause, effect, intent, and affordances to support reasoning and retrieval.
Category: Example
What datasets relate to Commonsense Knowledge?
ConceptNet, ATOMIC-2020, Social IQa, and Winograd Schema Challenge benchmarks evaluate and supply commonsense relations and reasoning tasks.
Category: Overview
What are risks of relying on crowdsourced Commonsense Knowledge?
Bias and cultural skew can emerge from contributor demographics. Poor quality control may introduce contradictions or noise into the knowledge base.
Category: Risks
How do LLMs learn Commonsense Knowledge?
They absorb patterns from large corpora, then fine-tune with targeted datasets and evaluations; retrieval augmentation and tool-use can improve grounding.
Category: Requirements
What are effective ways to evaluate Commonsense Knowledge?
Use causal reasoning tasks, multi-step inference, counterfactual queries, and multimodal benchmarks that require everyday knowledge.
Category: Testing
What is an alternative to curated Commonsense Knowledge bases?
Learned representations via self-supervised pretraining and retrieval over web-scale corpora can provide emergent commonsense abilities.
Category: Alternatives
Where can I start learning about Commonsense Knowledge in AI?
Explore ConceptNet documentation, ATOMIC-2020 dataset, and survey papers on commonsense reasoning and knowledge graphs.
Category: Learning