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

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paper09.pdf (1.41 MB)

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http://ceur-ws.org/Vol-2576/paper09.pdf

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http://hdl.handle.net/11603/19220

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UMBC Computer Science and Electrical Engineering Department
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conference papers and proceedings
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Ankur Padia, Konstantinos Kalpakis, Francis Ferraro and Tim Finin, Reflections on: Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization, http://ceur-ws.org/Vol-2576/paper09.pdf

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UMBC Ebiquity Research Group

Abstract

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.