Ghiasnezhad Omran, PouyaTaylor, KerryRodríguez Méndez, SergioHaller, ArminPan, J.Z.Tamma, V.d’Amato, C.Janowicz, K.2022-10-172022-10-17November 1978-3-030-62465-1http://hdl.handle.net/1885/275571Knowledge graphs (KGs) proliferating on theWeb are known to be incomplete. Much research has been proposed for automatic com- pletion, sometimes by rule learning, that scales well. All existing methods learn closed rules. Here we introduce open path (OP) rules and present a novel algorithm, oprl, for learning them. While closed rules are used to complete a KG by answering given queries, OP rules identify the incom- pleteness of a KG by inducing such queries to ask. We use adaptations of Freebase, YAGO2, and a synthetic but complete Poker KG to evaluate oprl. We find that oprl mines hundreds of accurate rules from massive KGs with up to 1M facts. The learnt OP rules induce queries with preci- sion up to 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion.application/pdfen-AU© Copyright 2020 for this paper by its authors.https://creativecommons.org/licenses/by/4.0/Knowledge Graph CompletionOpen Path RuleRule LearningKnowledge Graph20202021-11-28Creative Commons Attribution 4.0 International License