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Knowledge base completion (KBC) aims to predict the missing links inknowledge graphs. Previous KBC tasks and approaches mainly focus on the settingwhere all test entities and relations have appeared in the training set.However, there has been limited research on the zero-shot KBC settings, wherewe need to deal with unseen entities and relations that emerge in a constantlygrowing knowledge base. In this work, we systematically examine differentpossible scenarios of zero-shot KBC and develop a comprehensive benchmark,ZeroKBC, that covers these scenarios with diverse types of knowledge sources.Our systematic analysis reveals several missing yet important zero-shot KBCsettings. Experimental results show that canonical and state-of-the-art KBCsystems cannot achieve satisfactory performance on this challenging benchmark.By analyzing the strength and weaknesses of these systems on solving ZeroKBC,we further present several important observations and promising futuredirections.

ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion