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.

本文提出了一种新颖的 SSET（Semantic and Structure-aware KG Entity Typing）框架，通过结合结构知识和语义知识，以及利用两种模块的推理结果生成鲁棒性较强的类型预测，显著优于现有的最先进方法。