Resource-bounded crowd-sourcing of commonsense knowledge

Yen Ling Kuo, Jane Yung Jen Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages2470-2475
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: 16 07 201122 07 2011

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Country/TerritorySpain
CityBarcelona, Catalonia
Period16/07/1122/07/11

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