Details

Autor(en) / Beteiligte

• Lajus, Jonathan
• Galárraga, Luis
• Suchanek, Fabian
• Kirrane, Sabrina
• Ngonga Ngomo, Axel-Cyrille
• Paulheim, Heiko
• Gentile, Anna Lisa
• Cochez, Michael
• Harth, Andreas
• Rula, Anisa
• Haase, Peter

Titel

Fast and Exact Rule Mining with AMIE 3

Ist Teil von

• The Semantic Web, 2020, p.36-52

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Given a knowledge base (KB), rule mining finds rules such as “If two people are married, then they live (most likely) in the same place”. Due to the exponential search space, rule mining approaches still have difficulties to scale to today’s large KBs. In this paper, we present AMIE 3, a system that employs a number of sophisticated pruning strategies and optimizations. This allows the system to mine rules on large KBs in a matter of minutes. Most importantly, we do not have to resort to approximations or sampling, but are able to compute the exact confidence and support of each rule. Our experiments on DBpedia, YAGO, and Wikidata show that AMIE 3 beats the state of the art by a factor of more than 15 in terms of runtime.