TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph

Ryan Ong (), Jiahao Sun, Ovidiu Șerban and Yi-Ke Guo
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Ryan Ong: Department of Engineering, Imperial College London, London SW7 2BX, UK
Jiahao Sun: Department of Engineering, Imperial College London, London SW7 2BX, UK
Ovidiu Șerban: Department of Engineering, Imperial College London, London SW7 2BX, UK
Yi-Ke Guo: Department of Engineering, Imperial College London, London SW7 2BX, UK

Data, 2023, vol. 8, issue 3, 1-14

Abstract: Temporal knowledge graphs can be used to represent the current state of the world and, as daily events happen, the need to update the temporal knowledge graph, in order to stay consistent with the state of the world, becomes very important. However, there is currently no reliable method to accurately validate the update and evolution of knowledge graphs. There has been a recent development in text summarisation, whereby question answering is used to both guide and fact-check summarisation quality. The exact process can be applied to the temporal knowledge graph update process. To the best of our knowledge, there is currently no dataset that connects temporal knowledge graphs with documents with question–answer pairs. In this paper, we proposed the TKGQA dataset, consisting of over 5000 financial news documents related to M&A. Each document has extracted facts, question–answer pairs, and before and after temporal knowledge graphs, to highlight the state of temporal knowledge and any changes caused by the facts extracted from the document. As we parse through each document, we use question–answering to check and guide the update process of the temporal knowledge graph.

Keywords: temporal knowledge graph; question–answering; knowledge graph; entity dynamic; event knowledge graph; mergers and acquisitions; finance (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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