Knowledge graphs (KGs) are a popular way to organise information based on
ontologies or schemas and have been used across a variety of scenarios from
search to recommendation. Despite advances in KGs, representing knowledge
remains a non-trivial task across industries and it is especially challenging
in the biomedical and healthcare domains due to complex interdependent
relations between entities, heterogeneity, lack of standardization, and
sparseness of data. KGs are used to discover diagnoses or prioritize genes
relevant to disease, but they often rely on schemas that are not centred around
a node or entity of interest, such as a person. Entity-centric KGs are
relatively unexplored but hold promise in representing important facets
connected to a central node and unlocking downstream tasks beyond graph
traversal and reasoning, such as generating graph embeddings and training graph
neural networks for a wide range of predictive tasks. This paper presents an
end-to-end representation learning framework to extract entity-centric KGs from
structured and unstructured data. We introduce a star-shaped ontology to
represent the multiple facets of a person and use it to guide KG creation.
Compact representations of the graphs are created leveraging graph neural
networks and experiments are conducted using different levels of heterogeneity
or explicitness. A readmission prediction task is used to evaluate the results
of the proposed framework, showing a stable system, robust to missing data,
that outperforms a range of baseline machine learning classifiers. We highlight
that this approach has several potential applications across domains and is
open-sourced. Lastly, we discuss lessons learned, challenges, and next steps
for the adoption of the framework in practice.

本文提出一种基于星形本体的端到端表示学习框架，从结构化和非结构化数据中提取面向实体的知识图谱，采用图神经网络创建紧凑的图形表示，并利用研究结果进行再入院预测。

面向个人或实体中心知识图谱的表示学习：在医疗健康领域的应用

Representation Learning for Person or Entity-centric Knowledge Graphs:  an application in Healthcare

We introduce the task of entity-centric query refinement. Given an input
query whose answer is a (potentially large) collection of entities, the task
output is a small set of query refinements meant to assist the user in
efficient domain exploration and entity discovery. We propose a method to
create a training dataset for this task. For a given input query, we use an
existing knowledge base taxonomy as a source of candidate query refinements,
and choose a final set of refinements from among these candidates using a
search procedure designed to partition the set of entities answering the input
query. We demonstrate that our approach identifies refinement sets which human
annotators judge to be interesting, comprehensive, and non-redundant. In
addition, we find that a text generation model trained on our newly-constructed
dataset is able to offer refinements for novel queries not covered by an
existing taxonomy. Our code and data are available at
this https URL.

该论文提出了实体为中心的查询细化任务，并提出了一种使用现有知识库分类法来产生查询细化数据集的方法，该方法可通过生成模型对新查询进行精细化，并证明了该方法的有效性。

实体中心的查询优化

Entity-Centric Query Refinement

Recent information extraction approaches have relied on training deep neural
models. However, such models can easily overfit noisy labels and suffer from
performance degradation. While it is very costly to filter noisy labels in
large learning resources, recent studies show that such labels take more
training steps to be memorized and are more frequently forgotten than clean
labels, therefore are identifiable in training. Motivated by such properties,
we propose a simple co-regularization framework for entity-centric information
extraction, which consists of several neural models with identical structures
but different parameter initialization. These models are jointly optimized with
the task-specific losses and are regularized to generate similar predictions
based on an agreement loss, which prevents overfitting on noisy labels.
Extensive experiments on two widely used but noisy benchmarks for information
extraction, TACRED and CoNLL03, demonstrate the effectiveness of our framework.
We release our code to the community for future research.

本文研究信息提取中遇到的嘈杂标签问题，提出了一个共同正则化框架来解决这个问题，该框架基于识别嘈杂标签需要更多训练步骤且更容易被忘记的性质，在 TACRED 和 CoNLL03 这两个嘈杂基准数据集上进行了广泛的实验，并发布了代码供未来研究使用。

面向实体中心信息抽取的噪声标签学习

Learning from Noisy Labels for Entity-Centric Information Extraction

The recent introduction of entity-centric implicit network representations of
unstructured text offers novel ways for exploring entity relations in document
collections and streams efficiently and interactively. Here, we present
TopExNet as a tool for exploring entity-centric network topics in streams of
news articles. The application is available as a web service at
this https URL .

介绍了一种名为 TopExNet 的工具，用于探索实体中心网络主题在新闻文章流中的应用。该应用程序作为 Web 服务提供。