Recently, several large-scale RDF knowledge bases have been built and applied
in many knowledge-based applications. To further increase the number of facts
in RDF knowledge bases, logic rules can be used to predict new facts based on
the existing ones. Therefore, how to automatically learn reliable rules from
large-scale knowledge bases becomes increasingly important. In this paper, we
propose a novel rule learning approach named RDF2Rules for RDF knowledge bases.
RDF2Rules first mines frequent predicate cycles (FPCs), a kind of interesting
frequent patterns in knowledge bases, and then generates rules from the mined
FPCs. Because each FPC can produce multiple rules, and effective pruning
strategy is used in the process of mining FPCs, RDF2Rules works very
efficiently. Another advantage of RDF2Rules is that it uses the entity type
information when generates and evaluates rules, which makes the learned rules
more accurate. Experiments show that our approach outperforms the compared
approach in terms of both efficiency and accuracy.

本论文提出了一种名为 RDF2Rules 的新型规则学习方法，通过从 RDF 知识库中挖掘频繁谓词周期并从中生成规则来实现自动可靠规则的学习，这种方法采用了有效的修剪策略和实体类型信息生成和评估规则，从而在效率和准确性方面优于其他比较方法。