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Knowledge graph embedding (KGE), which maps entities and relations in aknowledge graph into continuous vector spaces, has achieved great success inpredicting missing links in knowledge graphs. However, knowledge graphs oftencontain incomplete triples that are difficult to inductively infer by KGEs. Toaddress this challenge, we resort to analogical inference and propose a noveland general self-supervised framework AnKGE to enhance KGE models withanalogical inference capability. We propose an analogical object retriever thatretrieves appropriate analogical objects from entity-level, relation-level, andtriple-level. And in AnKGE, we train an analogy function for each level ofanalogical inference with the original element embedding from a well-trainedKGE model as input, which outputs the analogical object embedding. In order tocombine inductive inference capability from the original KGE model andanalogical inference capability enhanced by AnKGE, we interpolate the analogyscore with the base model score and introduce the adaptive weights in the scorefunction for prediction. Through extensive experiments on FB15k-237 and WN18RRdatasets, we show that AnKGE achieves competitive results on link predictiontask and well performs analogical inference.

Analogical Inference Enhanced Knowledge Graph Embedding