Abstract
Knowledge graphs offer a powerful framework to structure and represent financial information in a flexible way by describing real world entities, such as financial securities, and their interrelations in the form of a graph. Semantic question answering systems allow to retrieve information from a knowledge graph using natural language questions and thus eliminate the need to be proficient in a formal query language. In this work, we present a proof-of-concept design for a financial knowledge graph and with it a semantic question answering framework specifically targeted for the finance domain. Our implemented approach uses a span-based joint entity and relation extraction model with BERT embeddings to translate a single-fact natural language question into its corresponding formal query representation. By employing a joint extraction model, we alleviate the concern of error propagation present in standard pipelined approaches for classification-based question answering. The presented framework is tested on a synthetic dataset derived from the instances of the implemented financial knowledge graph. Our empirical findings indicate very promising results with a F1-score of 84.60% for relation classification and 97.18% for entity detection.
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Bulla, M., Hillebrand, L., Lübbering, M., Sifa, R. (2021). Knowledge Graph Based Question Answering System for Financial Securities. In: Edelkamp, S., Möller, R., Rueckert, E. (eds) KI 2021: Advances in Artificial Intelligence. KI 2021. Lecture Notes in Computer Science(), vol 12873. Springer, Cham. https://doi.org/10.1007/978-3-030-87626-5_4
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