Related papers: ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion

ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion

URL: http://arxiv.org/abs/2206.04192v2
Date: Wed, 22 Mar 2023 11:20:01 GMT
Title: ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion
Authors: Aleksandar Pavlovi\'c and Emanuel Sallinger
Abstract summary: ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space. We show that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on W18RR.
Score: 78.8942067357231
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). KG embedding models (KGEs) have yielded promising results for KGC, yet any current KGE is incapable of: (1) fully capturing vital inference patterns (e.g., composition), (2) capturing prominent patterns jointly (e.g., hierarchy and composition), and (3) providing an intuitive interpretation of captured patterns. In this work, we propose ExpressivE, a fully expressive spatio-functional KGE that solves all these challenges simultaneously. ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space $\mathbb{R}^{2d}$. This model design allows ExpressivE not only to capture a rich set of inference patterns jointly but additionally to display any supported inference pattern through the spatial relation of hyper-parallelograms, offering an intuitive and consistent geometric interpretation of ExpressivE embeddings and their captured patterns. Experimental results on standard KGC benchmarks reveal that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on WN18RR.

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