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Hamming Distance Encoding Multihop Relation Knowledge Graph Completion
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DOI：10.1109/ACCESS.2020.3004448.png)
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Abstract
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Knowledge graphs (KGs) play an important role in many real-world applications like information retrieval, question answering, relation extraction, etc. To reveal implicit knowledge from a knowledge graph (KG), viz. knowledge graph completion (KGC), is a crucial task for the downstream applications based on KG. For this purpose various embedding-based approaches have been proposed recently. This paper proposes a new approach named HRESCAL to KGC. It extends the well-known embedding-based approach RESCAL by introducing Hamming distance-based encoder to capture implicit multihop and partial inverse relation features in a KG. Experimental results on widely used KGC benchmarks show that the new approach achieves state-of-the-art or is competitive AUC performance.
Keywords:
Knowledge graph
knowledge graph completion
tensor factorization
matrix factorization
Hamming distance
multihop relations
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