HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

In recent years, domain-specific knowledge bases (KBs) have been increasingly popular due to their expertise and in-depth representation in a specific domain. Among these domain-specific KBs, financial KBs have attracted considerable attention from academics and industries due to their broad spectrum of downstream applications, such as stock movement prediction, financial fraud detection, and supply chain management. Until now, no datasets including open-domain and domain-specific KBs provide dynamic, up-to-date, and comprehensive financial knowledge, which leads to a fair comparison and broader research among popular financial task models impossible or at least very difficult. We, therefore, construct a dynamic financial KB, HiDy, to address the current limitations discussed above. HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 501,500 relations, 17 entity types, and more than 51,000 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>. Moreover, the robustness of HiDy is a result of the state-of-the-art knowledge extraction and fusion techniques as well as the manual cleaning. For temporality, HiDy's dynamic knowledge is extracted from various resources under different time-granularity to ensure each branch's knowledge is the most up-to-date.