Active knowledge graph completion

Date

2020

Authors

Ghiasnezhad Omran, Pouya
Taylor, Kerry
Rodríguez Méndez, Sergio
Haller, Armin

Journal Title

Journal ISSN

Volume Title

Publisher

CEUR Workshop Proceedings

Abstract

Knowledge 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.

Description

Keywords

Knowledge Graph Completion, Open Path Rule, Rule Learning, Knowledge Graph

Citation

Source

Proceedings of the 19th International Semantic Web Conference on Demos and Industry Tracks (ISWC)

Type

Conference paper

Book Title

Entity type

Access Statement

Open Access

License Rights

Creative Commons Attribution 4.0 International License

DOI

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