基于关系记忆的胶囊网络知识图谱嵌入模型

Capsule network knowledge graph embedding model based on relational memory

作为一种语义知识库,知识图谱(KG)使用结构化三元组的形式存储真实世界的实体及其内在关系。为了推理知识图谱中缺失的真实三元组,考虑关系记忆网络较强的三元组表征能力和胶囊网络强大的特征处理能力,提出一种基于关系记忆的胶囊网络知识图谱嵌入模型。首先,通过编码实体和关系之间的潜在依赖关系和部分重要信息形成编码嵌入向量;然后,把嵌入向量与过滤器卷积以生成不同的特征图,再重组为对应的胶囊;最后,通过压缩函数和动态路由指定从父胶囊到子胶囊的连接,并根据子胶囊与权重内积的得分判断当前三元组的可信度。链接预测实验的结果表明,与CapsE模型相比,在倒数平均排名(MRR)和Hit@10评价指标上,所提模型在WN18RR数据集上分别提高了7.95%和2.2个百分点,在FB15K-237数据集上分别提高了3.82%和2个百分点。实验结果表明,所提模型可以更准确地推断出头实体和尾实体之间的关系。

As a semantic knowledge base,Knowledge Graph(KG)uses structured triples to store real-world entities and their internal relationships.In order to infer the missing real triples in the knowledge graph,considering the strong triple representation ability of relational memory network and the powerful feature processing ability of capsule network,a knowledge graph embedding model of capsule network based on relational memory was proposed.First,the encoding embedding vectors were formed through the potential dependencies between encoding entities and relationships and some important information.Then,the embedding vectors were convolved with the filter to generate different feature maps,and the corresponding capsules were recombined.Finally,the connections from the parent capsule to the child capsule was specified through the compression function and dynamic routing,and the confidence coefficient of the current triple was estimated by the inner product score between the child capsule and the weight.Link prediction experimental results show that compared with CapsE model,on the Mean Reciprocal Rank(MRR)and Hit@10 evaluation indicators,the proposed model has the increase of 7.95%and 2.2 percentage points respectively on WN18RR dataset,and on FB15K-237 dataset,the proposed model has the increase of 3.82%and 2 percentage points respectively.Experiments results show that the proposed model can more accurately infer the relationship between the head entity and the tail entity.