We present a novel system that automatically extracts and generates
informative and descriptive sentences from the biomedical corpus and
facilitates the efficient search for relational knowledge. Unlike previous
search engines or exploration systems that retrieve unconnected passages, our
system organizes descriptive sentences as a relational graph, enabling
researchers to explore closely related biomedical entities (e.g., diseases
treated by a chemical) or indirectly connected entities (e.g., potential drugs
for treating a disease). Our system also uses ChatGPT and a fine-tuned relation
synthesis model to generate concise and reliable descriptive sentences from
retrieved information, reducing the need for extensive human reading effort.
With our system, researchers can easily obtain both high-level knowledge and
detailed references and interactively steer to the information of interest. We
spotlight the application of our system in COVID-19 research, illustrating its
utility in areas such as drug repurposing and literature curation.

我们提出了一个新颖的系统，从生物医学语料库中自动提取和生成信息丰富、描述性的句子，促进相关知识的高效检索。与以往的搜索引擎或探索系统不同，我们的系统将描述性句子组织为关系图，使研究人员能够探索密切相关的生物医学实体（例如，化学品治疗的疾病）或间接相连的实体（例如，治疗疾病的潜在药物）。我们的系统还使用 ChatGPT 和经过微调的关系综合模型从检索到的信息中生成简明可靠的描述性句子，减少了对大量人工阅读的需求。通过我们的系统，研究人员可以轻松获得高层次知识和详细参考资料，并交互式地定位所需的信息。我们重点介绍了我们的系统在 COVID-19 研究中的应用，说明了它在药物再利用和文献整理等领域的实用性。

生物医学领域的描述性知识图谱

Descriptive Knowledge Graph in Biomedical Domain

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous
data and making inferences in biology and many other domains, but a coherent
solution for constructing, exchanging, and facilitating the downstream use of
knowledge graphs is lacking. Here we present KG-Hub, a platform that enables
standardized construction, exchange, and reuse of knowledge graphs. Features
include a simple, modular extract-transform-load (ETL) pattern for producing
graphs compliant with Biolink Model (a high-level data model for standardizing
biological data), easy integration of any OBO (Open Biological and Biomedical
Ontologies) ontology, cached downloads of upstream data sources, versioned and
automatically updated builds with stable URLs, web-browsable storage of KG
artifacts on cloud infrastructure, and easy reuse of transformed subgraphs
across projects. Current KG-Hub projects span use cases including COVID-19
research, drug repurposing, microbial-environmental interactions, and rare
disease research. KG-Hub is equipped with tooling to easily analyze and
manipulate knowledge graphs. KG-Hub is also tightly integrated with graph
machine learning (ML) tools which allow automated graph machine learning,
including node embeddings and training of models for link prediction and node
classification.

KG-Hub 是一个平台，它通过一种简单、模块化的提取 - 转换 - 加载（ETL）模式来生成符合 Biolink Model 的图形数据，易于集成任何 OBO 本体论，支持对上游数据源的缓存下载、版本化和自动更新构建，提供易于重复使用的转换子图，并支持基于知识图的机器学习。