Knowledge Graph Representation Learning With Multi-Scale Capsule-Based Embedding Model Incorporating Entity Descriptions
Knowledge Graph Representation Learning With Multi-Scale Capsule-Based Embedding Model Incorporating Entity Descriptions
复制标题
知识图表示学习与多尺度基于胶囊的嵌入模型结合实体描述
DOI：
10.1109/access.2020.3035636
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Yang, Zhi
中科院分区：
文献类型：
--
作者：
Cheng, Jingwei;Zhang, Fu;Yang, Zhi
知识图（kg）是一个定向图，带有节点作为实体和边缘作为关系。 KG表示学习（KGRL）旨在将kg中的实体和关系嵌入连续的低维矢量空间中，以简化操作，同时保留KG的固有结构。在本文中，我们提出了一个KG嵌入框架，即McApseed（基于多尺寸胶囊的嵌入模型，结合了实体描述）。 McApseed采用了变压器与关系注意机制结合使用，以识别实体描述的特定于关系部分，并获得实体的描述表示。实体的结构化和描述表示形式被整合到合成表示中。每个列的三列矩阵一个三重元素的合成表示形式被送入基于多尺寸胶囊的嵌入模型中，以产生头部实体，尾部实体和关系的最终表示。实验表明，McApseed在四个基准数据集上的链接预测任务中，McApseed的性能要比最新的嵌入模型更好。我们的代码可以在https://github.com/1780041410/mcapseed上找到。
A Knowledge Graph (KG) is a directed graph with nodes as entities and edges as relations. KG representation learning (KGRL) aims to embed entities and relations in a KG into continuous low-dimensional vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. In this paper, we propose a KG embedding framework, namely MCapsEED (Multi-Scale Capsule-based Embedding Model Incorporating Entity Descriptions). MCapsEED employs a Transformer in combination with a relation attention mechanism to identify the relation-specific part of an entity description and obtain the description representation of an entity. The structured and description representations of an entity are integrated into a synthetic representation. A 3-column matrix with each column a synthetic representation of an element of a triple is fed into a Multi-Scale Capsule-based Embedding model to produce final representations of the head entity, the tail entity and the relation. Experiments show that MCapsEED achieves better performance than state-of-the-art embedding models for the task of link prediction on four benchmark datasets. Our code can be found at https://github.com/1780041410/McapsEED.