Knowledge graphs, which represent facts as interconnected entities, have emerged as a pivotal technique for enhancing AI systems with the capacity to assimilate and contextualize knowledge.
However, real-world knowledge continuously evolves, necessitating dynamic representations that can capture the fluid, time-sensitive intricacies of the world.
Temporal knowledge graphs (TKGs) fulfill this need by incorporating a temporal dimension, with each relationship tagged with a timestamp denoting its period of validity. TKGs allow modeling not only the connections between entities but also the dynamics of these relationships, unlocking new potentials for AI.
While TKGs have garnered substantial research attention, their application in specialized domains remains an open frontier. In particular, the financial sector possesses attributes like rapidly evolving markets and multifaceted textual data that could significantly benefit from dynamic knowledge graphs. However, underdeveloped access to high-quality financial knowledge graphs has constrained advances in this domain.
Addressing this gap, Xiaohui Victor Li(2023) introduces an innovative, open-source Financial Dynamic Knowledge Graph (FinDKG) powered by a novel temporal knowledge graph learning model named Knowledge Graph Transformer (KGTransformer).
The FinDKG, constructed from a corpus of global financial news spanning over two decades, encapsulates both quantitative indicators and qualitative drivers of financial systems into an interconnected, temporal framework. The authors demonstrate FinDKG’s utility in generating actionable insights for real-world applications like risk monitoring and thematic investing.
The KGTransformer model, designed to handle the intricacies of TKGs, is shown to outperform existing static knowledge graph models on benchmark TKG…