The entity type information in Knowledge Graphs (KGs) such as DBpedia,
Freebase, etc. is often incomplete due to automated generation or human
curation. Entity typing is the task of assigning or inferring the semantic type
of an entity in a KG. This paper presents \textit{GRAND}, a novel approach for
entity typing leveraging different graph walk strategies in RDF2vec together
with textual entity descriptions. RDF2vec first generates graph walks and then
uses a language model to obtain embeddings for each node in the graph. This
study shows that the walk generation strategy and the embedding model have a
significant effect on the performance of the entity typing task. The proposed
approach outperforms the baseline approaches on the benchmark datasets DBpedia
and FIGER for entity typing in KGs for both fine-grained and coarse-grained
classes. The results show that the combination of order-aware RDF2vec variants
together with the contextual embeddings of the textual entity descriptions
achieve the best results.

提出了一种利用 RDF2vec 不同图形遍历策略和实体的文本描述来进行实体打标的新方法（GRAND）。实验表明，引入上下文信息的方式和生成图行走序列的策略对于实体打标在 KG 中有重要影响。该方法在 DBpedia 和 FIGER 基准数据集上，对于精细和粗粒度类组合都优于基线方案。