聚合邻域信息的联合知识表示模型

知识表示学习在关系抽取、自动问答等自然语言处理任务中获得了广泛关注,该技术旨在将知识库中的实体与关系表示为稠密低维实值向量。然而,已有的模型在建模知识库中的三元组时,或是忽略三元组的邻域信息,导致无法处理关联知识较少的罕见实体,或是在引入邻域信息时不能自适应地为每个实体抽取最相关的邻节点属性,导致引入了冗余信息。基于以上问题,该文在知识表示模型TransE的基础上提出了聚合邻域信息的联合知识表示模型TransE-NA(neighborhood aggregation on TransE)。该模型首先根据实体的稀疏度确定其邻节点数量,然后根据实体的邻边关系选取对应邻节点上最相关的属性作为实体的邻域信息。在链接预测和三元组分类任务上的实验结果表明,该文的模型效果超越了基线模型,验证了该模型能有效聚合邻域信息,缓解数据稀疏问题,改善知识表示性能。

Neighborhood Aggregation for Knowledge Graph Representation

Knowledge representation learning, which aims to encode entities and relations into a dense, real-valued and low-dimensional semantic space, has drawn massive attention in natural language processing tasks, such as relation extraction and question answering. To better capture the neighbor information, we propose a model named TransE-NA (Neighborhood Aggregation on TransE) based on TransE, which determines the number of neighbors according to sparse degrees of entities and then aggregates the most relevant attributes of neighbors according to the corresponding relations. Experimental results on link prediction and triplet classification show that our approach outperforms baselines, alleviating the data sparsity issue and improving the performance effectively.