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Improved knowledge graph embeddings by using inferred entity types

By Esma Balkir, Masha Naslidnyk, Dave Palfrey, Arpit Mittal, Sophie Durrant
2018
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In this paper we study techniques to improve the performance of bilinear embedding methods for knowledge graph completion on large datasets, where at each epoch the model sees a very small percentage of the training data, and the number of generated negative examples for each positive example is limited to a small portion of the entire set of entities. We first present a heuristic method to infer the types and type constraints of entities and relations. We then use this method to construct both a joint learning model, and a straightforward method for increasing the quality of sampled negatives during training. We show that when these two techniques are combined, they give an improvement in performance of up to 5.6% for Hits@1. We find the improvement is especially significant when the batch size and the number of generated negative examples are low relative to the size of the dataset.
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