Abstract
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the \textit{\textbf{structural knowledge}} in the local neighborhood of entities, disregarding \textit{\textbf{semantic knowledge}} in the textual representations of entities, relations, and types that are also crucial for type inference. Additionally, we observe that the interaction between semantic and structural knowledge can be utilized to address the false-negative problem. In this paper, we propose a novel \textbf{\underline{S}}emantic and \textbf{\underline{S}}tructure-aware KG \textbf{\underline{E}}ntity \textbf{\underline{T}}yping~{(SSET)} framework, which is composed of three modules. First, the \textit{Semantic Knowledge Encoding} module encodes factual knowledge in the KG with a Masked Entity Typing task. Then, the \textit{Structural Knowledge Aggregation} module aggregates knowledge from the multi-hop neighborhood of entities to infer missing types. Finally, the \textit{Unsupervised Type Re-ranking} module utilizes the inference results from the two models above to generate type predictions that are robust to false-negative samples. Extensive experiments show that SSET significantly outperforms existing state-of-the-art methods.
Abstract (translated)
知识图实体类型标注（KGET）任务的目的是预测知识图中实体的缺失类型标注。最近的工作仅利用实体局部邻域中的结构化知识，而忽略了文本表示中实体、关系和类型也至关重要用于类型推理的语义知识。此外，我们还观察到语义和结构化知识的相互作用可以用于解决假阴性问题。在本文中，我们提出了一个新颖的语义和结构感知的知识图实体类型标注（SSET）框架，它由三个模块组成。首先，\textit{语义知识编码}模块通过遮罩实体类型标注任务对知识图进行语义化知识编码。然后，\textit{结构化知识聚合}模块将来自实体多级邻域的知识进行聚合，以推断缺失类型。最后，\textit{无监督类型重新排序}模块利用上述两个模型的推理结果生成对假阴性样本鲁棒的类型预测。大量实验证明，SSET显著优于现有最先进的 methods。
URL
https://arxiv.org/abs/2404.08313
