Knowledge Graphs for Finance and Economics

Caitlin Walsh, Nathan Burton, Garrett Conway, Migael Strydom, Ye Tian

Abstract: The structure of the relationships between entities in finance and economics is inherently a dynamically-changing network, and knowledge graphs are a natural choice for representing and storing information of such networks. As knowledge graphs of the financial economy encompasses not only information extracted and abstracted from large amounts of textual data, but also from numerical and time series data, this field emerges as cross-pollination between NLP, quantitative finance and economics. Currently, most finance/economic literature focuses on quantitative data as modern NLP techniques are still recent developments. Relation extraction from non-financial data allows to uncover relationships amongst companies that are not immediately apparent from the price signal or official financial documents. Previous research at the intersection of KGs and finance has primarily focused on stock price prediction and financial fraud detection. In the field of NLP, KG construction has been an active field for many years, but the application and focus on finance and economics is only emerging. Most existing large-scale knowledge graphs are bootstrapped from Wikipedia, which only has entries for a fraction of existing companies, so novel knowledge graph bootstrapping techniques need to be developed. The aim of this workshop is to bring researchers from both industry and academia to bridge the gap between semantic and symbolic information and numerical information in knowledge graphs, and to discuss the application of KGs in finance and economics. Using automated methods to build knowledge graphs that are dynamic, nuanced and large scale allows researchers to learn from advances from several fields and enables the financial and economics industry to draw new insights and make more informed and timely decisions.