Stay Positive: Knowledge Graph Embedding Without Negative Sampling

• URL: http://arxiv.org/abs/2201.02661v1
• Date: Fri, 7 Jan 2022 20:09:27 GMT
• Title: Stay Positive: Knowledge Graph Embedding Without Negative Sampling
• Authors: Ainaz Hajimoradlou and Mehran Kazemi
• Abstract summary: We propose a training procedure that obviates the need for negative sampling by adding a novel regularization term to the loss function. Our results for two relational embedding models (DistMult and SimplE) show the merit of our proposal both in terms of performance and speed.
• Score: 1.8275108630751844
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Knowledge graphs (KGs) are typically incomplete and we often wish to infer new facts given the existing ones. This can be thought of as a binary classification problem; we aim to predict if new facts are true or false. Unfortunately, we generally only have positive examples (the known facts) but we also need negative ones to train a classifier. To resolve this, it is usual to generate negative examples using a negative sampling strategy. However, this can produce false negatives which may reduce performance, is computationally expensive, and does not produce calibrated classification probabilities. In this paper, we propose a training procedure that obviates the need for negative sampling by adding a novel regularization term to the loss function. Our results for two relational embedding models (DistMult and SimplE) show the merit of our proposal both in terms of performance and speed.

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