CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
WordNet: a lexical database for English
Communications of the ACM
Open Mind Common Sense: Knowledge Acquisition from the General Public
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Learner: a system for acquiring commonsense knowledge by analogy
Proceedings of the 2nd international conference on Knowledge capture
Verbosity: a game for collecting common-sense facts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Can we derive general world knowledge from texts?
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Methods for domain-independent information extraction from the web: an experimental comparison
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Community-based game design: experiments on social games for commonsense data collection
Proceedings of the ACM SIGKDD Workshop on Human Computation
An analysis of knowledge collected from volunteer contributors
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
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Knowledge acquisition is the essential process of extracting and encoding knowledge, both domain specific 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 answers are 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.