• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

SPHN 模式生成器 - 通过利用语义网技术将医疗保健语义从人类可读转换为机器可读。

The SPHN Schema Forge - transform healthcare semantics from human-readable to machine-readable by leveraging semantic web technologies.

作者信息

Touré Vasundra, Unni Deepak, Krauss Philip, Abdelwahed Abdelhamid, Buchhorn Jascha, Hinderling Leon, Geiger Thomas R, Österle Sabine

机构信息

Personalized Health Informatics, SIB Swiss Institute of Bioinformatics, Basel, 4051, Switzerland.

Accenture, Basel, 4051, Switzerland.

出版信息

J Biomed Semantics. 2025 May 8;16(1):9. doi: 10.1186/s13326-025-00330-9.

DOI:10.1186/s13326-025-00330-9
PMID:40341005
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12063216/
Abstract

BACKGROUND

The Swiss Personalized Health Network (SPHN) adopted the Resource Description Framework (RDF), a core component of the Semantic Web technology stack, for the formal encoding and exchange of healthcare data in a medical knowledge graph. The SPHN RDF Schema defines the semantics on how data elements should be represented. While RDF is proven to be machine readable and interpretable, it can be challenging for individuals without specialized background to read and understand the knowledge represented in RDF. For this reason, the semantics described in the SPHN RDF Schema are primarily defined in a user-accessible tabular format, the SPHN Dataset, before being translated into its RDF representation. However, this translation process was previously manual, time-consuming and labor-intensive.

RESULT

To automate and streamline the translation from tabular to RDF representation, the SPHN Schema Forge web service was developed. With a few clicks, this tool automatically converts an SPHN-compliant Dataset spreadsheet into an RDF schema. Additionally, it generates SHACL rules for data validation, an HTML visualization of the schema and SPARQL queries for basic data analysis.

CONCLUSION

The SPHN Schema Forge significantly reduces the manual effort and time required for schema generation, enabling researchers to focus on more meaningful tasks such as data interpretation and analysis within the SPHN framework.

摘要

背景

瑞士个性化健康网络(SPHN)采用了资源描述框架(RDF),这是语义网技术栈的一个核心组件,用于在医学知识图谱中对医疗数据进行形式化编码和交换。SPHN RDF模式定义了数据元素应如何表示的语义。虽然RDF已被证明是机器可读和可解释的,但对于没有专业背景的个人来说,阅读和理解RDF中表示的知识可能具有挑战性。因此,SPHN RDF模式中描述的语义主要以用户可访问的表格形式(即SPHN数据集)定义,然后再转换为其RDF表示形式。然而,此翻译过程以前是手动的,既耗时又费力。

结果

为了实现从表格到RDF表示的自动化和简化,开发了SPHN Schema Forge网络服务。通过点击几下,该工具就能自动将符合SPHN的数据集电子表格转换为RDF模式。此外,它还会生成用于数据验证的SHACL规则、模式的HTML可视化以及用于基本数据分析的SPARQL查询。

结论

SPHN Schema Forge显著减少了模式生成所需的人工工作量和时间,使研究人员能够专注于更有意义的任务,如在SPHN框架内进行数据解释和分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/40a41affcb3b/13326_2025_330_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/7273dc524d69/13326_2025_330_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/bdc73d37af26/13326_2025_330_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/8c35db690e94/13326_2025_330_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/40a41affcb3b/13326_2025_330_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/7273dc524d69/13326_2025_330_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/bdc73d37af26/13326_2025_330_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/8c35db690e94/13326_2025_330_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddd/12063216/40a41affcb3b/13326_2025_330_Fig4_HTML.jpg

相似文献

1
The SPHN Schema Forge - transform healthcare semantics from human-readable to machine-readable by leveraging semantic web technologies.SPHN 模式生成器 - 通过利用语义网技术将医疗保健语义从人类可读转换为机器可读。
J Biomed Semantics. 2025 May 8;16(1):9. doi: 10.1186/s13326-025-00330-9.
2
Semantic web-based ontology: a comprehensive framework for cardiovascular knowledge representation.基于语义网的本体:心血管知识表示的综合框架。
BMC Cardiovasc Disord. 2025 Jul 18;25(1):519. doi: 10.1186/s12872-025-04956-6.
3
Prescription of Controlled Substances: Benefits and Risks管制药品的处方:益处与风险
4
Short-Term Memory Impairment短期记忆障碍
5
Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational workflows.通过分布式数据驱动的语义服务和跨组织工作流实现优化的连续家庭护理供应。
J Biomed Semantics. 2024 Jun 6;15(1):9. doi: 10.1186/s13326-024-00303-4.
6
The measurement of collaboration within healthcare settings: a systematic review of measurement properties of instruments.医疗机构内协作的测量:对测量工具属性的系统评价
JBI Database System Rev Implement Rep. 2016 Apr;14(4):138-97. doi: 10.11124/JBISRIR-2016-2159.
7
Home treatment for mental health problems: a systematic review.心理健康问题的居家治疗:一项系统综述
Health Technol Assess. 2001;5(15):1-139. doi: 10.3310/hta5150.
8
Sexual Harassment and Prevention Training性骚扰与预防培训
9
Semantic units: organizing knowledge graphs into semantically meaningful units of representation.语义单元:将知识图组织成具有语义意义的表示单元。
J Biomed Semantics. 2024 May 27;15(1):7. doi: 10.1186/s13326-024-00310-5.
10
Cost-effectiveness of using prognostic information to select women with breast cancer for adjuvant systemic therapy.利用预后信息为乳腺癌患者选择辅助性全身治疗的成本效益
Health Technol Assess. 2006 Sep;10(34):iii-iv, ix-xi, 1-204. doi: 10.3310/hta10340.

本文引用的文献

1
Mapping the SPHN Dataset to FHIR.将 SPHN 数据集映射到 FHIR。
Stud Health Technol Inform. 2023 May 18;302:133-134. doi: 10.3233/SHTI230082.
2
Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets.构建开放政府数据的知识图谱:以新斯科舍省疾病数据集为例。
J Biomed Semantics. 2023 Apr 18;14(1):4. doi: 10.1186/s13326-023-00284-w.
3
FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network.利用语义网技术在瑞士个性化健康网络中实现健康相关数据的 FAIR 化。
Sci Data. 2023 Mar 10;10(1):127. doi: 10.1038/s41597-023-02028-y.
4
ECO: the Evidence and Conclusion Ontology, an update for 2022.ECO:证据和结论本体论,2022 年更新。
Nucleic Acids Res. 2022 Jan 7;50(D1):D1515-D1521. doi: 10.1093/nar/gkab1025.
5
A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study.一项旨在实现瑞士个性化健康网络内研究的健康数据二次使用互操作性的全国性、语义驱动的三支柱战略:方法学研究。
JMIR Med Inform. 2021 Jun 24;9(6):e27591. doi: 10.2196/27591.
6
SPHN - The Swiss Personalized Health Network Initiative.瑞士个性化健康网络倡议
Stud Health Technol Inform. 2020 Jun 16;270:1156-1160. doi: 10.3233/SHTI200344.
7
Knowledge Graph-Enabled Cancer Data Analytics.知识图谱赋能的癌症数据分析。
IEEE J Biomed Health Inform. 2020 Jul;24(7):1952-1967. doi: 10.1109/JBHI.2020.2990797. Epub 2020 May 4.
8
ROBOT: A Tool for Automating Ontology Workflows.机器人:自动化本体工作流程的工具。
BMC Bioinformatics. 2019 Jul 29;20(1):407. doi: 10.1186/s12859-019-3002-3.
9
CausalTAB: the PSI-MITAB 2.8 updated format for signalling data representation and dissemination.因果 TAB:PSI-MITAB 2.8 更新的信号数据表示和传播格式。
Bioinformatics. 2019 Oct 1;35(19):3779-3785. doi: 10.1093/bioinformatics/btz132.
10
UniProt: the universal protein knowledgebase.通用蛋白质知识库:UniProt
Nucleic Acids Res. 2017 Jan 4;45(D1):D158-D169. doi: 10.1093/nar/gkw1099. Epub 2016 Nov 29.