DASH: An Agile Knowledge Graph System Disentangling Demands, Algorithms, Data Resources, and Humans
Shaowei Chen, Haoran Wang, Jie Liu, Jiahui Wu · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Knowledge graph (KG) is an important branch of artificial intelligence, which has attracted increasing research interest. However, in most enterprises, it is challenging to quickly construct KGs with multi-source and heterogeneous data and apply KGs to meet diverse business demands. To deal with these challenges, we propose an agile knowledge graph system following the novel principle of disentangling Demands, Algorithms, data reSources, and Humans (DASH). Specifically, our system is equipped with prior information-based knowledge extraction, self-supervised knowledge integration, and hierarchical knowledge base question answering algorithms that have outstanding generalizability and portability. Meanwhile, we propose a semi-automatic data accumulation framework to reduce labor costs of data annotations. Based on DASH, we develop a Web application with easy-to-use functionalities such as canvases and drag-and-drop, and illustrate its usage in a financial scenario.