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Question answering over temporal knowledge graphs (TKGQA) has recently foundincreasing interest. TKGQA requires temporal reasoning techniques to extractthe relevant information from temporal knowledge bases. The only existing TKGQAdataset, i.e., CronQuestions, consists of temporal questions based on the factsfrom a fixed time period, where a temporal knowledge graph (TKG) spanning thesame period can be fully used for answer inference, allowing the TKGQA modelsto use even the future knowledge to answer the questions based on the pastfacts. In real-world scenarios, however, it is also common that given theknowledge until now, we wish the TKGQA systems to answer the questions askingabout the future. As humans constantly seek plans for the future, buildingTKGQA systems for answering such forecasting questions is important.Nevertheless, this has still been unexplored in previous research. In thispaper, we propose a novel task: forecasting question answering over temporalknowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e.,ForecastTKGQuestions, for this task. It includes three types of questions,i.e., entity prediction, yes-no, and fact reasoning questions. For everyforecasting question in our dataset, QA models can only have access to the TKGinformation before the timestamp annotated in the given question for answerinference. We find that the state-of-the-art TKGQA methods perform poorly onforecasting questions, and they are unable to answer yes-no questions and factreasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model thatemploys a TKG forecasting module for future inference, to answer all threetypes of questions. Experimental results show that ForecastTKGQA outperformsrecent TKGQA methods on the entity prediction questions, and it also showsgreat effectiveness in answering the other two types of questions.

ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs