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- W2584683887 abstract "Traditional approaches to knowledge base completion have been based on symbolic representations. Lowdimensional vector embedding models proposed recently for this task are attractive since they generalize to possibly unlimited sets of relations. A significant drawback of previous embedding models for KB completion is that they merely support reasoning on individual relations (e.g., bornIn(X, Y ) ⇒ nationality(X, Y )). In this work, we develop models for KB completion that support chains of reasoning on paths of any length using compositional vector space models. We construct compositional vector representations for the paths in the KB graph from the semantic vector representations of the binary relations in that path and perform inference directly in the vector space. Unlike previous methods, our approach can generalize to paths that are unseen in training and, in a zero-shot setting, predict target relations without supervised training data for that relation." @default.
- W2584683887 created "2017-02-10" @default.
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- W2584683887 date "2015-01-01" @default.
- W2584683887 modified "2023-09-24" @default.
- W2584683887 title "Compositional Vector Space Models for Knowledge Base Inference." @default.
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