Active knowledge graph completion

Enterprise and public Knowledge Graphs (KGs) are known to be incomplete. Methods for automatic completion, sometimes by rule learning, scale well. While previous rule-based methods learn closed (non-existential) rules, we introduce Open Path (OP) rules that are constrained existential rules. We present a novel algorithm, OPRL, for learning OP rules. Closed rules complete a KG by answering queries of unclear origin, usually derived from a holdback test set in experimental settings. However, OP rules can generate relevant queries for KG completion. OPRL generates queries even when there is no closed rule to answer the query, or when the correct answer is a missing entity that is not present in the KG. For OPRL to scale well, we propose a novel embedding-based fitness function to effi-ciently estimate rule quality. Additionally, we introduce a novel, efficient vector computa-tion to formally assess rule quality. We evaluate OPRL using adaptations of Freebase, YAGO2, Wikidata, anda synthetic Poker KG. We find that OPRL mines hundreds of accurate rules from massive KGs with up to 8 M facts. The OP rules generate queries with precision as high as 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion. Crown Copyright (c) 2022 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This article introduces a novel algorithm, OPRL, for learning Open Path (OP) rules that can generate relevant queries for Knowledge Graph completion, even when there is no closed rule to answer the query. This demonstrates the first solution for active knowledge graph completion.