Commonsense Knowledge and Its Representation in Artificial Intelligence: A Conceptual Review of Symbolic, Graph-Based, Neural, and Neuro-Symbolic Approaches

Commonsense knowledge comprises the everyday facts, expectations, causal assumptions, social conventions, Physical regularities, and practical inference patterns that people ordinarily use without explicitly stating them. Although artificial intelligence (AI) has achieved substantial progress in natural-language processing, vision, planning, decision support, and generative language, reliable commonsense remains difficult to acquire, represent, retrieve, update, and reason over. This article presents a conceptual review of commonsense knowledge and its representation in AI, synthesizing the major representational traditions from symbolic logic, frames, scripts, and semantic networks to ontologies, knowledge graphs, probabilistic models, neural language models, and neuro-symbolic architectures. Particular attention is given to Cyc, ConceptNet, ATOMIC, and ATOMIC 2020 as influential resources that illustrate complementary approaches to explicit and inferential commonsense. The review shows that commonsense is characterized by implicitness, context dependence, defeasibility, uncertainty, temporal and spatial structure, social situatedness, and cultural variation. These characteristics make a single universal representation difficult to sustain. The article therefore proposes a layered hybrid architecture comprising input interpretation, an explicit commonsense knowledge graph, neural retrieval, rule-based and probabilistic reasoning, and an explanation/provenance layer. The framework is positioned as a research design rather than an experimentally validated system. The article also identifies knowledge acquisition, coverage, inconsistency, temporal change, cultural variation, grounding, scalability, evaluation, provenance, and hallucination control as continuing challenges. Finally, it highlights opportunities for educational AI and context-sensitive systems in Nigerian and African settings, where local linguistic and institutional knowledge can complement globally developed commonsense resources.

Keywords: commonsense knowledge; knowledge representation; artificial intelligence; knowledge graphs; symbolic reasoning; neural reasoning; neuro-symbolic AI; ConceptNet; Cyc; ATOMIC.