Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations

Matthias Lalisse; Paul Smolensky

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arXiv · 1811.01062

Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations

Matthias Lalisse·Paul Smolensky

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Abstract

Neural models of Knowledge Base data have typically employed compositional representations of graph objects: entity and relation embeddings are systematically combined to evaluate the truth of a candidate Knowedge Base entry. Using a model inspired by Harmonic Grammar, we propose to tokenize triplet embeddings by subjecting them to a process of optimization with respect to learned well-formedness conditions on Knowledge Base triplets. The resulting model, known as Gradient Graphs, leads to sizable improvements when implemented as a companion to compositional models. Also, we show that the "supracompositional" triplet token embeddings it produces have interpretable properties that prove helpful in performing inference on the resulting triplet representations.

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Matthias Lalisse, Paul Smolensky. 2018-11-02. Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations. https://arxiv.org/abs/1811.01062

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