Abstract
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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Notes
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Facilities available in the Apache Jena framework were used: https://jena.apache.org.
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Acknowledgment
We would like to thank Giovanni Sansaro who formalized and developed the code for the preliminary version of TransOWL for his bachelor thesis.
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d’Amato, C., Quatraro, N.F., Fanizzi, N. (2021). Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs. In: Verborgh, R., et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_26
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