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The goal of knowledge graph completion (KGC) is to predict missing links in aKG using trained facts that are already known. In recent, pre-trained languagemodel (PLM) based methods that utilize both textual and structural informationare emerging, but their performances lag behind state-of-the-art (SOTA)structure-based methods or some methods lose their inductive inferencecapabilities in the process of fusing structure embedding to text encoder. Inthis paper, we propose a novel method to effectively unify structureinformation and language semantics without losing the power of inductivereasoning. We adopt entity anchors and these anchors and textual description ofKG elements are fed together into the PLM-based encoder to learn unifiedrepresentations. In addition, the proposed method utilizes additional randomnegative samples which can be reused in the each mini-batch during contrastivelearning to learn a generalized entity representations. We verify theeffectiveness of the our proposed method through various experiments andanalysis. The experimental results on standard benchmark widely used in linkprediction task show that the proposed model outperforms existing the SOTA KGCmodels. Especially, our method show the largest performance improvement onFB15K-237, which is competitive to the SOTA of structure-based KGC methods.

Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors