ADMUS: A Progressive Question Answering Framework Adaptable to Multiple
  Knowledge Sources
Yirui Zhan Yanzeng Li Minhao Zhang Lei Zou
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