Padia, AnkurKalpakis, KonstantinosFerraro, FrancisFinin, Tim2020-07-222020-07-22Ankur 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.pdfhttp://hdl.handle.net/11603/19220International Semantic Web Conference 2019We 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.9 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/UMBC Ebiquity Research GroupText