Exception-enriched rule learning from knowledge graphs

Mohamed H. Gad-Elrab*, Daria Stepanova, Jacopo Urbani, Gerhard Weikum

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are inevitably bound to be incomplete. To fill in the gaps, data correlations in the KG can be analyzed to infer Horn rules and to predict new facts. However, Horn rules do not take into account possible exceptions, so that predicting facts via such rules introduces errors. To overcome this problem, we present a method for effective revision of learned Horn rules by adding exceptions (i.e., negated atoms) into their bodies. This way errors are largely reduced. We apply our method to discover rules with exceptions from real-world KGs. Our experimental results demonstrate the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction.

Original languageEnglish
Title of host publicationThe Semantic Web - 15th International Semantic Web Conference, ISWC 2016, Proceedings
Number of pages18
Volume9981 LNCS
ISBN (Print)9783319465227
Publication statusPublished - 2016
Event15th International Semantic Web Conference, ISWC 2016 - Kobe, Japan
Duration: 17 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9981 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference15th International Semantic Web Conference, ISWC 2016


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