文献类型：期刊文献

英文题名：Sweet Apple, company? or food? Adjective-centric commonsense knowledge acquisition with taxonomy-guided induction

作者：Wang, Chao[1,2];Liu, Juntao[3];Liu, Jingping[4];Jiang, Sihang[3];Li, Zhixu[3];Xiao, Yanghua[3]

机构：[1]Shanghai Univ, Sch Future Technol, Shanghai, Peoples R China;[2]Shanghai Univ, Sch Artificial Intelligence, Shanghai, Peoples R China;[3]Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai, Peoples R China;[4]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

年份：2023

卷号：280

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20233914802518);WOS:【SCI-EXPANDED(收录号:WOS:001095585700001)】；

基金：We thank the anonymous reviewers for their valuable comments. This work was supported by the Program of Natural Science Foundation of Shanghai (No. 23ZR1422800) .

语种：英文

外文关键词：Adjective-centric; Adjective-concept pair; Commonsense knowledge; Knowledge graph; Induction

摘要：How to enable intelligent machines to possess human commonsense knowledge is one of the central concerns of artificial intelligence. Consequently, a series of commonsense knowledge bases have been designed and constructed by scholars. However, as an important kind of commonsense knowledge, adjective-centric commonsense knowledge (a crucial type of adjective-concept pair such as (blue, sky), (eatable, food)) is far from satisfactory due to the lack of existing knowledge bases and limited acquisition methods. In this paper, we concentrate on automatically constructing large-scale adjective-centric commonsense knowledge bases and propose an effective framework to achieve the goal. The framework mainly contains a filtering module to remove unreasonable inputs, a clustering module, and a conceptualization module to obtain adjective-concept pairs and an evaluation module to assess their plausibility. Extensive automatic and human evaluation results demonstrate the effectiveness of our method, and we finally harvest over 200 k adjective-centric commonsense knowledge, where 81.56% of the implicit commonsense knowledge is not covered by WebChild.1 Moreover, we also show that our mined adjective-centric commonsense knowledge can benefit the downstream query conceptualization task.