Unifying Structure and Language Semantic for Efficient Contrastive
  Knowledge Graph Completion with Structured Entity Anchors
Sang-Hyun Je Wontae Choi Kwangjin Oh
The goal of knowledge graph completion (KGC) is to predict missing links in a
KG using trained facts that are already known. In recent, pre-trained language
model (PLM) based methods that utilize both textual and structural information
are emerging, but their performances lag behind state-of-the-art (SOTA)
structure-based methods or some methods lose their inductive inference
capabilities in the process of fusing structure embedding to text encoder. In
this paper, we propose a novel method to effectively unify structure
information and language semantics without losing the power of inductive
reasoning. We adopt entity anchors and these anchors and textual description of
KG elements are fed together into the PLM-based encoder to learn unified
representations. In addition, the proposed method utilizes additional random
negative samples which can be reused in the each mini-batch during contrastive
learning to learn a generalized entity representations. We verify the
effectiveness of the our proposed method through various experiments and
analysis. The experimental results on standard benchmark widely used in link
prediction task show that the proposed model outperforms existing the SOTA KGC
models. Especially, our method show the largest performance improvement on
FB15K-237, which is competitive to the SOTA of structure-based KGC methods.