Abstract
Existing deep learning-based models for knowledge base question answering (KBQA) suffer from the high costs of adapting the system to disparate datasets in real-world scenarios (e.g., multi-tenant platform). In this paper, we present ADMUS, a progressive knowledge base question answering framework designed to accommodate a wide variety of datasets with multiple languages by decoupling the architecture of conventional KBQA systems. Our framework supports the seamless integration of new datasets with minimal effort, only requiring creating a dataset-related micro-service at a negligible cost. To enhance the usability of ADUMS, we design a progressive framework consisting of three stages, ranging from executing exact queries, generating approximate queries and retrieving open-domain knowledge referring from large language models. An online demonstration of ADUMS is available at: https://answer.gstore.cn/pc/index.html.
This work was done during the internship of Yirui Zhan at Peking University.
Y. Zhan and Y. Li—Contributed equally to this work.
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Zhan, Y., Li, Y., Zhang, M., Zou, L. (2024). A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14965. Springer, Singapore. https://doi.org/10.1007/978-981-97-7244-5_26
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DOI: https://doi.org/10.1007/978-981-97-7244-5_26
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