Journal
SEMANTIC WEB, ESWC 2021
Volume 12731, Issue -, Pages 441-457Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-77385-4_26
Keywords
Knowledge graphs; Embeddings; Link prediction; Triple classification; Representation learning
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The study introduces a method to incorporate background knowledge into embedding models to enhance the quality of knowledge graph embeddings. Experimental evaluation demonstrates a significant improvement of the proposed method over the original ones.
Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a low-dimensional space, in which graph structural information are preserved as much as possible, enabling an efficient computation of solutions. We propose a solution for injecting available background knowledge (schema axioms) to further improve the quality of the embeddings. The method has been applied to enhance existing models to produce embeddings that can encode knowledge that is not merely observed but rather derived by reasoning on the available axioms. An experimental evaluation on link prediction and triple classification tasks proves the improvement yielded implementing the proposed method over the original ones.
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