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The rapid growth of large scale event datasets with timestamps has given rise to the dynamically evolving multi-relational knowledge graphs. Temporal reasoning over such data brings on many challenges and is still not well understood. Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This observation has given rise to the recent interest in low-shot learning methods that aredblp:conf/aaai/Mirtaheri21 fatcat:2z3m7tsmvbarbhfoxb7n5u23bu