A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which ...
Jun 6, 2020 · This paper proposes a novel framework for joint repre- sentation learning of knowledge graphs and textual Infor- mation via reference sentences.
A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, ...
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Jun 1, 2020 · A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is ...
Sep 22, 2016 · Knowledge Representation via Joint Learning of Sequential Text and ... sent knowledge graphs, and textual information has shown significant ...
Joint Representations of Knowledge Graphs and Textual Information via Reference Sentences ... representation learning of knowledge graphs and text information ...
Abstract. The objective of knowledge graph embedding is to encode both entities and relations of knowl- edge graphs into continuous low-dimensional vec-.
A key feature of neural models is that they can produce semantic vector representations of objects (texts, images, speech, etc.) en-.
This work proposes a novel framework to embed words, entities and relations into the same continuous vector space and shows that the model can significantly ...
We learn joint representations for knowledge base elements and corresponding text, which allows to perform retrieval and referenceless adequacy evaluation.
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