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Overview
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.
Topics/Open Questions
- Using KGs to represent relationships among companies and industries
- Automatic new industry and theme detection
- Identifying and representing supply chains
- Data sources and annotation for KGs in finance and economics
- Economic value of the data produced by KGs
- Knowledge graph entity hierarchy induction
- Combining numerical and semantic information in KGs
- Bootstrap KGs from public data sources other than Wikipedia
- Any other topics related to KGs for finance and economics
Call for Papers
We are looking for people to present their current research, work in progress, or published research during the conference through a presentation, lighting talk, or panel. If you are interested, please submit your abstract here.
Important Dates
September: Deadline to submit papers
November 3-5: AKBC Conference (Virtual and in London)
More specific dates to be added soon.
Committee Members
- Dr. Ye Tian (University College London, Theia Insights)
- Ben Jones (Nasdaq)
- Dr. Gaurav Singh (Binance)
- Dr. Migael Strydom (Theia Insights)
- Dr. Pranava Madhyastha (City University of London)
- Ioannis Dourats (Meta)
- Dr. Lemin Wu (Theia Insights)
- Pietro Lesci (University of Cambridge)
- Nathan Burton (Theia Insights)
- Caitlin Walsh (Theia Insights)
If you have any questions or concerns, please contact caitlin [at] theiainsights.com!