RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles

TL;DR

RDF2Rules is a novel method for automatically learning logical rules from large RDF knowledge bases by mining frequent predicate cycles, improving rule accuracy and efficiency for knowledge base completion.

Contribution

The paper introduces RDF2Rules, a new approach that mines frequent predicate cycles and uses entity type info to generate more accurate rules efficiently.

Findings

Outperforms existing methods in efficiency and accuracy.

Effectively mines frequent predicate cycles for rule learning.

Utilizes entity type info to enhance rule quality.

Abstract

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.