Open Collections

Financial knowledge graph construction

University of British Columbia

The proliferation of financial news sources reporting on companies, markets, currencies and stocks presents an opportunity for strategic decision making by mining data with the goal of extracting structured representations about financial entities and their inter-relations. These representations can be conveniently stored as (subject, predicate, object) triples in a knowledge graph that can be used drive new in-sights through answering complex queries using high level declarative languages.Towards this goal, we develop a high precision knowledge extraction pipeline tailored for the financial domain. This pipeline combines multiple information ex-traction techniques with a financial dictionary that we built, all working together to produce over 342,000 compact extractions from over 288,000 financial news articles, with a precision of 78% at the top-100 extractions. These extractions are stored in a knowledge graph readily available for use in downstream applications. Our pipeline outperforms existing work in terms of precision, the total number of extractions and the coverage of financial predicates.

Text

2020-07-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/75344

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/