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Modeling of relation pattern is the core focus of previous Knowledge GraphEmbedding works, which represents how one entity is related to anothersemantically by some explicit relation. However, there is a more natural andintuitive relevancy among entities being always ignored, which is that how oneentity is close to another semantically, without the consideration of anyexplicit relation. We name such semantic phenomenon in knowledge graph asproximity pattern. In this work, we explore the problem of how to define andrepresent proximity pattern, and how it can be utilized to help knowledge graphembedding. Firstly, we define the proximity of any two entities according totheir statistically shared queries, then we construct a derived graph structureand represent the proximity pattern from global view. Moreover, with theoriginal knowledge graph, we design a Chained couPle-GNN (CP-GNN) architectureto deeply merge the two patterns (graphs) together, which can encode a morecomprehensive knowledge embedding. Being evaluated on FB15k-237 and WN18RRdatasets, CP-GNN achieves state-of-the-art results for Knowledge GraphCompletion task, and can especially boost the modeling capacity for complexqueries that contain multiple answer entities, proving the effectiveness ofintroduced proximity pattern.

Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding