Knowledge graph completion using topological correlation and multi-perspective independence

Knowledge graphs (KGs) are large-scale semantic networks designed to describe real-world facts. Existing KGs contain only a fraction of what is really happening in the real life. Knowledge graph completion (KGC) has attracted attention because it can automatically infer and predict missing links in the KGs. The classical geometric, tensor decomposition and convolutional neural network (CNN) based models generate expressive feature embeddings, but these models treat triples independently and thus fail to cover the hidden information that is inherently implicit in the local neighborhood surrounding a triple. Recent works have introduced Graph Convolutional Network (GCN) into KGC task for leveraging the rich structural information in complex graphs. However, existing GCN-based models have limitations in relational topology distinction and multi-perspective feature aggregation. As multi-relational graphs, KGs display intrinsic heterogeneous structures and rich entity types. Therefore, ignoring the rich structural information and perspective features will greatly limit the expressive power of these models. In this paper, we propose multi-relational GCNs for modeling topological correlation and multi-perspective independence (CorIn). Specifically, we propose a multi-integration ring relational topology to fine-grained select neighbors according to relational correlation patterns, and capture the independence of multi-perspective feature groups by mutual information minimization. Our model adaptively utilizes an embedding learning design that can leverage a variety of entity-relation composition operations from classical KGC models. We evaluate our model on the public benchmark datasets and achieve marked performance gains in comparison to state-of-the-art methods.