  Large scale knowledge graph embedding has attracted much attention from both
academia and industry in the field of Artificial Intelligence. However, most
existing methods concentrate solely on fact triples contained in the given
knowledge graph. Inspired by the fact that logic rules can provide a flexible
and declarative language for expressing rich background knowledge, it is
natural to integrate logic rules into knowledge graph embedding, to transfer
human knowledge to entity and relation embedding, and strengthen the learning
process. In this paper, we propose a novel logic rule-enhanced method which can
be easily integrated with any translation based knowledge graph embedding
model, such as TransE . We first introduce a method to automatically mine the
logic rules and corresponding confidences from the triples. And then, to put
both triples and mined logic rules within the same semantic space, all triples
in the knowledge graph are represented as first-order logic. Finally, we define
several operations on the first-order logic and minimize a global loss over
both of the mined logic rules and the transformed first-order logics. We
conduct extensive experiments for link prediction and triple classification on
three datasets: WN18, FB166, and FB15K. Experiments show that the rule-enhanced
method can significantly improve the performance of several baselines. The
highlight of our model is that the filtered Hits@1, which is a pivotal
evaluation in the knowledge inference task, has a significant improvement (up
to 700% improvement).
