Link Prediction using Embedded Knowledge Graphs

Abstract

Since large knowledge bases are typically incomplete, missing facts need to be inferred from observed facts in a task called knowledge base completion. The most successful approaches to this task have typically explored explicit paths through sequences of triples. These approaches have usually resorted to human-designed sampling procedures, since large knowledge graphs produce prohibitively large numbers of possible paths, most of which are uninformative. As an alternative approach, we propose performing a single, short sequence of interactive lookup operations on an embedded knowledge graph which has been trained through end-to-end backpropagation to be an optimized and compressed version of the initial knowledge base. Our proposed model, called Embedded Knowledge Graph Network (EKGN), achieves new state-of-the-art results on popular knowledge base completion benchmarks.

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Cited by in corpus (5)

• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
• Differentiable Learning of Logical Rules for Knowledge Base Reasoning
• Fast Linear Model for Knowledge Graph Embeddings
• An Interpretable Knowledge Transfer Model for Knowledge Base Completion
• Scaffolding Networks: Incremental Learning and Teaching Through Questioning