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

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2049–2060 May 2-6, 2023 ©2023 Association for Computational Linguistics TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs Aditya Sharma IISc, Bangalore adityasharma@iisc.ac.in Apoorv Saxena IISc, Bangalore apoorvsaxena@iisc.ac.in Chitrank Gupta IIT Bombay chigupta2011@gmail.com Mehran Kazemi Google Research, Montreal mehrankazemi@google.com Partha Talukdar Google Research, India partha@google.com Soumen Chakrabarti IIT Bombay soumen@cse.iitb.ac.in Abstract Recent years have witnessed interest in Tem- poral Question Answering over Knowledge Graphs (TKGQA), resulting in the develop- ment of multiple methods. However, these are highly engineered, thereby limiting their generalizability, and they do not automatically discover relevant parts of the KG during multi- hop reasoning. Relational graph convolutional networks (RGCN) provide an opportunity to address both of these challenges – we explore this direction in the paper. Specifically, we pro- pose a novel, intuitive and interpretable scheme to modulate the messages passed through a KG edge during convolution based on the relevance of its associated period to the question. We also introduce a gating device to predict if the an- swer 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 a recent challenging dataset for multi-hop com- plex temporal QA called TimeQuestions. We show that TwiRGCN significantly outperforms state-of-the-art models on this dataset across di- verse question types. Interestingly, TwiRGCN improves accuracy by 9–10 percentage points for the most difficult ordinal and implicit ques- tion types. 1 Introduction Question answering (QA) is a key problem in nat- ural language processing and a long-lasting mile- stone for artificial intelligence. A large class of approaches for QA makes use of knowledge graphs (KG), which are multi-relational graphs represent- ing facts (KGQA). Temporal KGs (TKG) represent facts that are only valid for specific periods of time as (subject, relation, object, time range), for ex- ample, (Franklin D Roosevelt, position held, Pres- ident of USA, [1933, 1945]). The problem of an- swering questions that require temporal reasoning over TKGs (TKGQA) is a special case of KGQA that specifically focuses on the following challenge: temporal questions constrain answers through tem- poral notions, e.g., “who was the first president of US during WW2?” Developing systems for tem- poral QA is of immense practical importance for many applications. It is considered a more chal- lenging problem than KGQA (Bhutani et al., 2019; Saxena et al., 2020), where questions are typically about persistent, non-temporal facts (e.g., place of birth), with only a small portion of the questions requiring any temporal reasoning (Jia et al., 2018a). Even though a variety of models have been pro- posed for the TKGQA recently, they suffer from the following problems: 1) they are either highly engi- neered toward the task (Jia et al., 2021; Chen et al., 2022) or 2) they do not incorporate graph structure information using Graph Neural Networks (GNN) (Mavromatis et al., 2021; Shang et …

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