Journal article

A convolutional neural network-based model for knowledge base completion and its application to search personalization

Dai Quoc Nguyen, Dat Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

Semantic Web: interoperability, usability, applicability | IOS Press | Published : 2019

Abstract

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the in..

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Funding Acknowledgements

This research was partially supported by the Australian Research Council (ARC) DP150100031 and DP160103934.