Work

Commonsense AI in the History of the Web

Year: 2025

Type: conference-paper

Abstract: Machine common sense (MCS)-the challenge of enabling computers to grasp everyday human knowledge-has been a grand challenge in Artificial Intelligence (AI) since the 1950s. While recent advances in large language models have led to impressive progress, there is still no consensus on how much common sense today's AI actually possesses. In this brief review, we revisit the historical development of MCS in the context of the Web, examining how the Web's evolution-from early knowledge representation efforts to knowledge graphs, the Semantic Web, and crowdsourcing-has shaped MCS research. We argue that key breakthroughs in Web technologies were instrumental in addressing longstanding challenges of scale and coverage in commonsense reasoning. At the same time, MCS research has influenced the development of core Web applications, including intelligent agents, plausibility-based reasoning, and robust evaluation of black-box AI systems.

DOI: 10.1145/3701716.3716841

Authors: Mayank Kejriwal, Deborah L. McGuinness, Henry Lieberman

Institutions: University of Southern California, Rensselaer Polytechnic Institute, Massachusetts Institute of Technology

Language: English

Topic: Semantic Web and Ontologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: University of Southern California, Universitas Brawijaya, Defense Advanced Research Projects Agency, Air Force Office of Scientific Research

Awards: G2118216107, G5865625667

OpenAlex ID: W4410636901

FWCI: 11.8

Cites: 14

Cited by: 12

Related to: 10