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The majority of knowledge graph embedding techniques treat entities andpredicates as separate embedding matrices, using aggregation functions to builda representation of the input triple. However, these aggregations are lossy,i.e. they do not capture the semantics of the original triples, such asinformation contained in the predicates. To combat these shortcomings, currentmethods learn triple embeddings from scratch without utilizing entity andpredicate embeddings from pre-trained models. In this paper, we design a novelfine-tuning approach for learning triple embeddings by creating weaksupervision signals from pre-trained knowledge graph embeddings. We develop amethod for automatically sampling triples from a knowledge graph and estimatingtheir pairwise similarities from pre-trained embedding models. These pairwisesimilarity scores are then fed to a Siamese-like neural architecture tofine-tune triple representations. We evaluate the proposed method on two widelystudied knowledge graphs and show consistent improvement over otherstate-of-the-art triple embedding methods on triple classification and tripleclustering tasks.

Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision