TY - GEN
T1 - Exception-enriched rule learning from knowledge graphs
AU - Gad-Elrab, Mohamed H.
AU - Stepanova, Daria
AU - Urbani, Jacopo
AU - Weikum, Gerhard
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84992645513&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992645513&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46523-4_15
DO - 10.1007/978-3-319-46523-4_15
M3 - Conference contribution
AN - SCOPUS:84992645513
SN - 9783319465227
VL - 9981 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 234
EP - 251
BT - The Semantic Web - 15th International Semantic Web Conference, ISWC 2016, Proceedings
PB - Springer/Verlag
T2 - 15th International Semantic Web Conference, ISWC 2016
Y2 - 17 October 2016 through 21 October 2016
ER -