Resource-Bounded Crowd-Sourcing of Commonsense Knowledge
Yen-Ling Kuo, Jane Yung-jen Hsu
Knowledge acquisition is the essential process of extracting and encoding knowledge, both domainspecific and commonsense, to be used in intelligent systems. While many large knowledge bases have been constructed, none is close to complete. This paper presents an approach to improving a knowledge base efficiently under resource constraints. Using a guiding knowledge base, questions are generated from a weak form of similarity-based inference given the glossary mapping between two knowledge bases. The candidate questions are prioritized in terms of the concept coverage of the target knowledge. Experiments were conducted to find questions to grow the Chinese ConceptNet using the English ConceptNet as a guide. The results were evaluated by online users to verify that 94.17% of the questions and 85.77% of the answersare good. In addition, the answers collected in a six-week period showed consistent improvement to a 36.33% increase in concept coverage of the Chinese commonsense knowledge base against the English ConceptNet.