In recent studies, the focus has been on enhancing knowledge graph embedding
learning, which encodes entities and relations in knowledge graphs into
low-dimensional vector spaces. While current models mainly consider the
structural aspects of these graphs, there's a wealth of contextual and literal
information in knowledge graphs that can be utilized for more effective
embeddings. This paper introduces a novel model that incorporates both
contextual and literal information into entity and relation embeddings,
utilizing graph convolutional networks. Specifically, for contextual
information, we assess its significance through confidence and relatedness
metrics. A unique rule-based method is developed to calculate the confidence
metric, and the relatedness metric is derived from the literal information's
representations. We validated our model's performance with thorough experiments
on two established benchmark datasets.

本研究论文介绍了一种新颖的模型，将知识图谱中的实体和关系编码为低维向量空间，并将上下文信息和字面信息融入到实体和关系嵌入中，使用图卷积网络进行建模，通过置信度和相关性度量评估上下文信息的重要性，并通过字面信息的表示来计算相关性度量。我们在两个常用基准数据集上进行了全面的实验证实以验证模型的性能。