Improved knowledge graph embeddings by using inferred entity types
2018
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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