LowFER: Low-rank Bilinear Pooling for Link Prediction

• URL: http://arxiv.org/abs/2008.10858v1
• Date: Tue, 25 Aug 2020 07:33:52 GMT
• Title: LowFER: Low-rank Bilinear Pooling for Link Prediction
• Authors: Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield, G\"unter Neumann
• Abstract summary: We propose a factorized bilinear pooling model, commonly used in multi-modal learning, for better fusion of entities and relations. Our model naturally generalizes decomposition Tucker based TuckER model, which has been shown to generalize other models. We evaluate on real-world datasets, reaching on par or state-of-the-art performance.
• Score: 4.110108749051657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs are incomplete by nature, with only a limited number of observed facts from the world knowledge being represented as structured relations between entities. To partly address this issue, an important task in statistical relational learning is that of link prediction or knowledge graph completion. Both linear and non-linear models have been proposed to solve the problem. Bilinear models, while expressive, are prone to overfitting and lead to quadratic growth of parameters in number of relations. Simpler models have become more standard, with certain constraints on bilinear map as relation parameters. In this work, we propose a factorized bilinear pooling model, commonly used in multi-modal learning, for better fusion of entities and relations, leading to an efficient and constraint-free model. We prove that our model is fully expressive, providing bounds on the embedding dimensionality and factorization rank. Our model naturally generalizes Tucker decomposition based TuckER model, which has been shown to generalize other models, as efficient low-rank approximation without substantially compromising the performance. Due to low-rank approximation, the model complexity can be controlled by the factorization rank, avoiding the possible cubic growth of TuckER. Empirically, we evaluate on real-world datasets, reaching on par or state-of-the-art performance. At extreme low-ranks, model preserves the performance while staying parameter efficient.

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