Multi-Granularity Temporal Question Answering over Knowledge Graphs

Ziyang Chen, Jinzhi Liao, Xiang Zhao

Abstract: Recently, question answering over temporal knowledge graphs (i.e., TKGQA) has been in- troduced and investigated, in quest of reasoning about dynamic factual knowledge. To foster re- search on TKGQA, a few datasets have been curated (e.g., CRONQUESTIONS and Complex- CRONQUESTIONS), and various models have been proposed based on these datasets. Never- theless, existing efforts overlook the fact that real-life applications of TKGQA also tend to be complex in temporal granularity, i.e., the ques- tions may concern mixed temporal granularities (e.g., both day and month). To overcome the limitation, in this paper, we motivate the notion of multi-granularity temporal question answer- ing over knowledge graphs and present a large- scale dataset for multi-granularity TKGQA, namely MULTITQ. To the best of our knowl- edge, MULTITQ is among the first of its kind, and compared with existing datasets on TKGQA, MULTITQ features at least two desir- able aspects—ample relevant facts and multiple temporal granularities. It is expected to better reflect real-world challenges, and serve as a test bed for TKGQA models. In addition, we propose a competing baseline MultiQA over MULTITQ, which is experimentally demon- strated to be effective in dealing with TKGQA.