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Abstract
The incompleteness of the knowledge base (KB) is one of the key issues when answering natural language questions over an incomplete knowledge base (KB-QA). To alleviate this problem, a framework, RuKBC-QA, is proposed to integrate methods of rule-based knowledge base completion (KBC) into general QA systems. Three main components are included in our framework, namely, a rule miner that mines logic rules from the KB, a rule selector that selects meaningful rules for QA, and a QA model that aggregates information from the original knowledge base and the selected rules. Experiments on WEBQUESTIONS dataset indicate that the proposed framework can effectively alleviate issues caused by incompleteness and obtains a significant improvement in terms of micro average Fl score by 2.4% to 4.5% under different incompleteness settings.
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Acknowledgement
This work is supported by National Key Research and Development Program of China under grant 2016YFB1000902; And NSFC Project No. 61472412 and No. 61621003.
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Academy of Mathematics and Systems Science and Key Lab-MADIS, Chinese Academy of Sciences, Beijing, 100190, China
Qilin Sun
School of the Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Qilin Sun
School of Modern Posts and Institute of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
Weizhuo Li
- Qilin Sun
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Correspondence toQilin Sun.
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ECE & Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, Canada
Xiaodan Zhu
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Min Zhang
School of Computer Science and Technology, Soochow University, Suzhou, China
Yu Hong
College of Intelligence and Computing, Tianjin University, Tianjin, China
Ruifang He
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Sun, Q., Li, W. (2020). RuKBC-QA: A Framework for Question Answering over Incomplete KBs Enhanced with Rules Injection. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_7
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