TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs

• URL: http://arxiv.org/abs/2210.06281v2
• Date: Fri, 6 Oct 2023 00:00:12 GMT
• Title: TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs
• Authors: Aditya Sharma, Apoorv Saxena, Chitrank Gupta, Seyed Mehran Kazemi, Partha Talukdar, Soumen Chakrabarti
• Abstract summary: We show how to generalize relational graph convolutional networks (RGCN) for temporal question answering (QA) We propose a novel, intuitive and interpretable scheme to modulate the messages passed through a KG edge during convolution. We evaluate the resulting system, which we call TwiRGCN, on TimeQuestions, a recently released, challenging dataset for complex temporal QA.
• Score: 35.50055476282997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have witnessed much interest in temporal reasoning over knowledge graphs (KG) for complex question answering (QA), but there remains a substantial gap in human capabilities. We explore how to generalize relational graph convolutional networks (RGCN) for temporal KGQA. Specifically, we propose a novel, intuitive and interpretable scheme to modulate the messages passed through a KG edge during convolution, based on the relevance of its associated time period to the question. We also introduce a gating device to predict if the answer to a complex temporal question is likely to be a KG entity or time and use this prediction to guide our scoring mechanism. We evaluate the resulting system, which we call TwiRGCN, on TimeQuestions, a recently released, challenging dataset for multi-hop complex temporal QA. We show that TwiRGCN significantly outperforms state-of-the-art systems on this dataset across diverse question types. Notably, TwiRGCN improves accuracy by 9--10 percentage points for the most difficult ordinal and implicit question types.

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