A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

Understand

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.