摘要：

现实世界中的知识图谱由大量事实三元组构成，其中通常包含许多出现次数很少的小样本关系，面向这些小样本关系补全知识图谱中缺失的三元组是一项具有挑战性的工作。针对现有小样本知识图谱补全模型中普遍存在的小样本关系表示无法有效提取问题，提出一种基于关系学习网络的小样本知识图谱补全模型。考虑关系的相关性，对参考和查询三元组进行邻域聚合编码，获得增强的实体嵌入表示。基于融合Transformer编码器与长短期记忆神经网络的结构，将三元组的关系表示进行编码输出。利用注意力机制得出查询关系与动态参考关系的语义相似性，并结合平移模型的假设对查询三元组成立的可能性进行综合打分。实验结果表明，该模型通过融合路径发现与上下文语义有效提取了小样本关系的细粒度语义，在小样本链接预测任务中，相较于基线模型中评价指标的最优值平均提升了9.5个百分点。

关键词: 小样本关系, 邻域聚合, 关系表示, 知识图谱补全, 链接预测

Abstract:

A Knowledge Graph(KG) is composed of a large number of fact triples, which often contain a large number of few-shot relations that rarely appear in the real world. For these few-shot relations, it is challenging to complete the missing triples in the KG, and existing few-shot Knowledge Graph Completion(KGC) models cannot effectively extract the representation of few-shot relations. To address this problem, a few-shot KGC model based on a relation learning network is proposed. Considering the relevance of the relations, neighbor aggregation encoding is performed on the reference and query triples to obtain an enhanced entity embedding representation. The structure that integrates a Transformer encoder and Long Short-Term Memory(LSTM) neural network, allows the relation representation of triples to be encoded and output. The semantic similarity between query and dynamic reference relations is obtained using the attention mechanism and combined with the hypothesis of the translation model, whereby the possibility of establishing query triples is comprehensively scored. The experimental results show that the model can effectively extract the fine-grained semantics of few-shot relations by integrating path-finding and context semantics. Compared with the optimal value of the evaluation metrics in baseline models, the average improvement of few-shot link prediction tasks reach 9.5 percentage points with the proposed model.

Key words: few-shot relation, neighbor aggregation, relation representation, Knowledge Graph Completion(KGC), link prediction