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Generalization performance optimization of KBQA system for Chinese open domain

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Abstract

Knowledge-Based Question Answering (KBQA) is a technique that utilizes the rich semantic information present in knowledge bases to comprehensively understand questions and obtain answers. The mainstream approaches consist of two methods: Semantic Parsing-Based (SP-based) and Information Retrieval-Based (IR-based). The former converts the question into a logical form that can be understood and executed by machines through semantic analysis, and then queries the knowledge base for answers. The latter first identifies the topic entity in the question and retrieves candidate answers, and then extracts features from both the question and candidate answers. Finally, a ranking model is used to model and predict the question and candidate answers. Compared to the impressive results achieved by English KBQA systems, Chinese KBQA systems face challenges due to the sparse semantic expression and limited features of the Chinese knowledge base, as well as the large number of similar entities that are difficult to differentiate. This makes it difficult for general models to properly understand the text’s characteristics, resulting in a challenge to improve the accuracy of Entity Linking and to maximize the performance of the KBQA system. To address this, this paper proposes two steps to improve Entity Linking in the KBQA system: Candidate Generation (CG) and Entity Disambiguation (ED), with a focus on realizing Entity Disambiguation. In this paper, Entity Disambiguation is treated as a classification task, and a Dual-Channel Network Model based on Bi-LSTM and CNN is constructed. By combining different featuresextracted from Bi-LSTM and CNN, this paper also introduces an attention mechanism to fully explore the weak semantic relationship between the question answering system and candidate entity, effectively reducing the reliance of the question answering system on additional feature rules. Experimental results show that the Entity Linking model proposed in this paper can effectively improve the performance of the question and answer system, has strong generalization, weakens dependence on additional information, and ensures the quality of Q &A while reducing manual intervention. Our method has achieved the current best average F1 value in the Chinese open domain datasets NLPCC-2016KBQA and CCKS2019KBQA.

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The data sets supporting the results of this article are included within the article and its additional files.

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Acknowledgements

The authors gratefully acknowledge the financial supports by the National Key R &D Program of China (Grant No. 2020AAA0109300).

Funding

The research leading to these results received funding from the National Key R &D Program of China under Grant Agreement Grant No. 2020AAA0109300.

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Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People’s Republic of China

    Yang Chen, Weibing Wan & Bo Huang

  2. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Yuming Zhao

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  1. Yang Chen
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  2. Weibing Wan
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  3. Yuming Zhao
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Correspondence to Weibing Wan.

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Chen, Y., Wan, W., Zhao, Y. et al. Generalization performance optimization of KBQA system for Chinese open domain. Multimed Tools Appl 83, 12445–12466 (2024). https://doi.org/10.1007/s11042-023-16011-7

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