Question answering over temporal knowledge graphs

Saxena, A and Chakrabarti, S and Talukdar, P (2021) Question answering over temporal knowledge graphs. In: Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021, 1 August - 6 August 2021, Virtual, Online, pp. 6663-6676.

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Abstract

Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (e.g., start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broad-coverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340×. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformer-based solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well. © 2021 Association for Computational Linguistics

Item Type:	Conference Paper
Publication:	ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference
Publisher:	Association for Computational Linguistics (ACL)
Additional Information:	The copyright for this article belongs to Association for Computational Linguistics (ACL).
Keywords:	Embeddings; Knowledge graphs; Performance; Question Answering; Research communities; State of the art; Structural complexity; Temporal knowledge, Knowledge graph
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	22 Feb 2023 03:28
Last Modified:	22 Feb 2023 03:28
URI:	https://eprints.iisc.ac.in/id/eprint/80416

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