With the introduction of deep learning models, semantic parsingbased knowledge base question answering (KBQA) systems have achieved high performance in handling complex questions. However, most existing approaches primarily focus on enhancing the model's effectiveness on individual benchmark datasets, disregarding the high costs of adapting the system to disparate datasets in real-world scenarios (e.g., multi-tenant platform). Therefore, we present ADMUS, a progressive knowledge base question answering framework designed to accommodate a wide variety of datasets, including multiple languages, diverse backbone knowledge bases, and disparate question answering datasets. To accomplish the purpose, we decouple the architecture of conventional KBQA systems and propose this dataset-independent framework. 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, ranges 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

翻译：随着深度学习模型的引入，基于语义解析的知识库问答（KBQA）系统在处理复杂问题方面取得了高性能。然而，现有方法主要侧重于提升模型在单个基准数据集上的有效性，忽视了在实际场景（如多租户平台）中适配不同数据集的高昂成本。为此，我们提出ADMUS，一种渐进式知识库问答框架，旨在适应多种数据集，包括多语言、多样化的骨干知识库以及不同的问答数据集。为实现这一目标，我们解构了传统KBQA系统的架构，并提出了这一与数据集无关的框架。我们的框架支持以最小工作量无缝集成新数据集，仅需以可忽略的成本创建与数据集相关的微服务。为提升ADMUS的可用性，我们设计了一个包含三阶段的渐进式框架，涵盖执行精确查询、生成近似查询以及借助大语言模型检索开放领域知识。ADMUS的在线演示地址为：https://answer.gstore.cn/pc/index.html