Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer

KEnS is a novel framework for embedding learning and ensemble knowledge transfer across a number of language-specific KGs . It embeds all KGs in a shared embedding space, where the association of entities is captured based on self-learning . Then, it performs ensemble inference to combine prediction results from embeddings of multiple KGs, for which multiple ensemble techniques are investigated . Experiments on five real-world KGs consistently improve state-of-the-art methods on KG completion, via effectively identifying and leveraging complementary knowledge . The new framework improves state of the art methods on KGs completion, by effectively identifying and leveraging competitiveness of complementary knowledge of KG and informability of the KG-commissionary knowledge . It is published in The Open Science journal, October 26, 2013, by John Defterios, The Nature of the Open Science .

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Keywords : knowledge - kgs - ensemble - kg - completion -

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