KGEL: A novel end-to-end embedding learning framework for knowledge graph completion

Abstract

Knowledge graphs (KGs) have recently become increasingly popular due to the broad range of essential applications in various downstream tasks including intelligent search, personalized recommendations, intelligent financial data analytics, etc. During an automated construction of a KG, the knowledge facts from multiple knowledge sources are automatically extracted in the form of triples, and these observed triples are used to derive new unobserved triples for KG completion (also known as link prediction). State-of-the-art link prediction methods are known to be primarily KG embedding models, among which tensor factorization models have recently drawn much attention due to their scalability and expressive feature embeddings, and hence, perform well for link prediction. However, these embedding models consider each KG triple individually and fail to capture the useful information present in the neighborhood of a node. To this end, we propose a novel end-to-end KG embedding learning framework that consists of an encoder of a dual weighted graph convolutional network, and a decoder of a novel fully expressive tensor factorization model. The proposed encoder extends weighted graph convolutional network to generate two rich and high quality embedding vectors for each node by aggregating information from the neighboring nodes. The proposed decoder has a flexible and powerful tensor representation form of the Tensor Train decomposition that takes benefit of the two representations of each node in its embedding space to accurately model the KG triples. We also derive a bound on the size of the embeddings for full expressivity and show that our proposed tensor factorization model is fully expressive. Additionally, we show the relationship of our tensor factorization model to previous tensor factorization models. The experimental results show the effectiveness of the proposed framework that consistently marks performance gains over several previous models on recent standard link prediction datasets.