›› 2013, Vol. 28 ›› Issue (4): 689-719.doi: 10.1007/s11390-013-1369-6

Special Issue: Surveys; Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

A Survey of Commonsense Knowledge Acquisition

Liang-Jun Zang1,2 (臧良俊), Cong Cao1,2 (曹聪), Ya-Nan Cao3 (曹亚男), Yu-Ming Wu1 (吴昱明) and Cun-Gen CAO1 (曹存根), Member, CCF   

  1. 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China;
    2. University of the Chinese Academy of Sciences, Beijing 100049, China;
    3. The Second Lab, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
  • Received:2012-06-15 Revised:2013-04-28 Online:2013-07-05 Published:2013-07-05
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant Nos. 91224006, 61173063, 61035004, 61203284, and 309737163, and the National Social Science Foundation of China under Grant No. 10AYY003.

Collecting massive commonsense knowledge (CSK) for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects: CSK acquisition task (what CSK is acquired from where), technique used (how can CSK be acquired), and CSK evaluation methods (how to evaluate the acquired CSK). In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.

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