Question Answering Over Temporal Knowledge Graphs Apoorv Saxena Soumen Chakrabarti Partha Talukdar Indian Institute of Science Indian Institute of Technology Google Research Bangalore Bombay India
[email protected] [email protected] [email protected] Abstract time intervals as well. These temporal scopes on facts can be either automatically estimated (Taluk- Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by provid- dar et al., 2012) or user contributed. Several such ing temporal scopes (e.g., start and end times) Temporal KGs have been proposed in the literature, on each edge in the KG. While Question An- where the focus is on KG completion (Dasgupta swering over KG (KGQA) has received some et al. 2018; Garc´ıa-Duran´ et al. 2018; Leetaru and attention from the research community, QA Schrodt 2013; Lacroix et al. 2020; Jain et al. 2020). over Temporal KGs (Temporal KGQA) is a The task of Knowledge Graph Question Answer- relatively unexplored area. Lack of broad- coverage datasets has been another factor lim- ing (KGQA) is to answer natural language ques- iting progress in this area. We address this tions using a KG as the knowledge base. This is challenge by presenting CRONQUESTIONS, in contrast to reading comprehension-based ques- the largest known Temporal KGQA dataset, tion answering, where typically the question is ac- clearly stratified into buckets of structural com- companied by a context (e.g., text passage) and plexity. CRONQUESTIONS expands the only the answer is either one of multiple choices (Ra- known previous dataset by a factor of 340×. jpurkar et al., 2016) or a piece of text from the We find that various state-of-the-art KGQA methods fall far short of the desired perfor- context (Yang et al., 2018).