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。

知识图谱实体类型的语义和结构知识整合

The Integration of Semantic and Structural Knowledge in Knowledge Graph  Entity Typing

Knowledge graph entity typing (KGET) aims at inferring plausible types of
entities in knowledge graphs. Existing approaches to KGET focus on how to
better encode the knowledge provided by the neighbors and types of an entity
into its representation. However, they ignore the semantic knowledge provided
by the way in which types can be clustered together. In this paper, we propose
a novel method called Multi-view Contrastive Learning for knowledge graph
Entity Typing (MCLET), which effectively encodes the coarse-grained knowledge
provided by clusters into entity and type embeddings. MCLET is composed of
three modules: i) Multi-view Generation and Encoder module, which encodes
structured information from entity-type, entity-cluster and cluster-type views;
ii) Cross-view Contrastive Learning module, which encourages different views to
collaboratively improve view-specific representations of entities and types;
iii) Entity Typing Prediction module, which integrates multi-head attention and
a Mixture-of-Experts strategy to infer missing entity types. Extensive
experiments show the strong performance of MCLET compared to the
state-of-the-art

本文介绍了一种名为多视图对比学习的知识图谱实体类型推断方法，它将聚类提供的粗粒度知识编码到实体和类型嵌入中，并通过多个模块实现结构化信息的编码、不同视角的对比学习以及缺失实体类型的推断。实验证明了该方法相对于现有的最优方法具有较强的性能。

知识图谱上的实体类型的多视角对比学习

Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs

Knowledge graph entity typing (KGET) is a task to predict the missing entity
types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to
solve the KGET task by introducing an auxiliary relation, 'hasType', to model
the relationship between entities and their types. However, a single auxiliary
relation has limited expressiveness for diverse entity-type patterns. We
improve the expressiveness of KGE methods by introducing multiple auxiliary
relations in this work. Similar entity types are grouped to reduce the number
of auxiliary relations and improve their capability to model entity-type
patterns with different granularities. With the presence of multiple auxiliary
relations, we propose a method adopting an Asynchronous learning scheme for
Entity Typing, named AsyncET, which updates the entity and type embeddings
alternatively to keep the learned entity embedding up-to-date and informative
for entity type prediction. Experiments are conducted on two commonly used KGET
datasets to show that the performance of KGE methods on the KGET task can be
substantially improved by the proposed multiple auxiliary relations and
asynchronous embedding learning. Furthermore, our method has a significant
advantage over state-of-the-art methods in model sizes and time complexity.

知识图谱实体类型（KGET）是一项用于预测知识图谱（KG）中缺失实体类型的任务。我们通过引入多个辅助关系来提高 KG 嵌入方法的表达能力，将相似的实体类型分组以减少辅助关系的数量，并改进不同粒度实体类型模式的建模能力。我们提出了 AsyncET 方法，采用异步学习方案进行实体类型预测，通过交替更新实体和类型嵌入来保持学习到的实体嵌入的最新性和信息性。实验结果表明，所提出的多个辅助关系和异步嵌入学习方法显著改善了 KGE 方法在 KGET 任务上的性能，而且我们的方法在模型大小和时间复杂度方面具有显著优势。