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Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs by Claudia d’Amato; Nicola Flavio Quatraro; Nicola Fanizzi is a book available to read on EtoBox.

What is Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs about?

Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a lowdimensional 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.

Author

Claudia d’Amato; Nicola Flavio Quatraro; Nicola Fanizzi

Publisher

Springer International Publishing : Imprint: Springer

Published

2021

Language

EN

ISBN

9783030773854

Subjects

Management, Business, Computer Science