International Semantic Web Conference (Journal Track)

Reflections on: Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization

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We present a family of four novel methods for embedding knowledge graphs into real-valued tensors that capture the ordered relations found in RDF. Unlike many previous models, these can easily use prior background knowledge from users or existing knowledge graphs.We demonstrate our models on the task of predicting new facts on eight different knowledge graphs, achieving a 5% to 50% improvement over existing systems. Through experiments, we derived recommendations for selecting the best model based on knowledge graph characteristics. We also give a provably-convergent, linear tensor factorization algorithm.


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InProceedings

CEUR

2576

(journal track short version of 2019 paper in Journal of Web Semantics )

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