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。

利用图遍历和实体描述进行实体类型预测

Entity Type Prediction Leveraging Graph Walks and Entity Descriptions

Medical diagnosis is the process of making a prediction of the disease a
patient is likely to have, given a set of symptoms and observations. This
requires extensive expert knowledge, in particular when covering a large
variety of diseases. Such knowledge can be coded in a knowledge graph --
encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge
itself and its encoding can be incomplete, refining the knowledge graph with
additional information helps physicians making better predictions. At the same
time, for deployment in a hospital, the diagnosis must be explainable and
transparent. In this paper, we present an approach using diagnosis paths in a
medical knowledge graph. We show that those graphs can be refined using latent
representations with RDF2vec, while the final diagnosis is still made in an
explainable way. Using both an intrinsic as well as an expert-based evaluation,
we show that the embedding-based prediction approach is beneficial for refining
the graph with additional valid conditions.

本研究提出了一种使用诊断路径的医学知识图谱的方法，并使用 RDF2vec 进行潜在表示以优化图谱的精度，同时保持预测可解释性和透明度。研究结果表明此预测方法对于图谱优化有益。

基于增强知识图谱的医学诊断诊断路径优化

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

RDF2vec is a knowledge graph embedding mechanism which first extracts
sequences from knowledge graphs by performing random walks, then feeds those
into the word embedding algorithm word2vec for computing vector representations
for entities. In this poster, we introduce two new flavors of walk extraction
coined e-walks and p-walks, which put an emphasis on the structure or the
neighborhood of an entity respectively, and thereby allow for creating
embeddings which focus on similarity or relatedness. By combining the walk
strategies with order-aware and classic RDF2vec, as well as CBOW and skip-gram
word2vec embeddings, we conduct a preliminary evaluation with a total of 12
RDF2vec variants.

RDF2vec 是一种知识图谱嵌入机制，从知识图中提取实体的序列，然后使用 word2vec 算法计算实体的向量表示，新引入的 e-walks 和 p-walks 强调实体的结构或邻域，可用于创建重点相似性或相关性的嵌入。