Collaborative Research: Semantic Map of Biological Data Sources: Entity Identity and Path Characterization

协作研究：生物数据源语义图：实体身份和路径表征

• 批准号: 0223042
• 负责人: Zoe Lacroix
• 金额: --
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0223042&HistoricalAwards=false
• 关键词: Collaborative; Research; Semantic; Map; Biological

Collaborative Research: Semantic Map of Biological Data Sources: Entity Identity and Path CharacterizationA fundamental problem facing the biological researcher today is correctly identifying a specific instance of a biological entity, e.g., a specific gene or protein, and then obtaining a complete functional characterization of this entity instance by exploring a multiplicity of inter-related sources. While the diversity of available data presents an opportunity to attack this problem, it is accompanied by difficulties in harnessing and exploring data. This collaborative interdisciplinary, inter-institutional research project exploits the researchers' prior expertise on wrapper and mediator technology and apply domain specific semantic knowledge to this problem. The goal is the correct identification and complete characterization of scientific entities by exploring multiple data sources. This project will address three tasks: (1) Construction of a Semantic Map of biological data sources including unique identifiers; links between sources; attributes; and search and query; (2) Learning at the data source, schema, domain and instance level uses the concept of Identity Link to identify equivalent identifiers for the same instance, and (3) Learning the properties of physical links and paths that are implemented among multiple data sources. The topology of links (paths) based on their properties, and Link (Path) Equivalence is applied to the tasks of entity identity and characterization. The tools developed in this project will be accessible via the project Web site (http://www.umiacs.umd.edu/research/CLIP/BFEnt02/), and can be applied by biologists to support ongoing scientific investigations in cancer research and diabetes, resulting in a broad impact in medical research and treatment. The educational impact includes a course on data management solutions for bioinformatics researchers and a workshop on experiment planning using data sources for biologists.

协作研究：生物数据源的语义图：实体识别和路径表征当今生物学研究者面临的一个基本问题是正确识别生物实体的特定实例，例如特定的基因或蛋白质，然后通过探索相互关联的多重来源获得该实体实例的完整功能表征。虽然可用数据的多样性为解决这一问题提供了机会，但在利用和探索数据方面也存在困难。这个跨学科、跨机构的合作研究项目利用了研究人员在包装和中介技术方面的专业知识，并将特定领域的语义知识应用于这个问题。目标是通过探索多个数据源来正确识别和完整表征科学实体。本项目将解决三个任务：(1)构建包含唯一标识符的生物数据源语义图；来源之间的链接；属性;以及搜索和查询；(2)在数据源、模式、域和实例级别学习使用Identity Link的概念来标识相同实例的等效标识符；(3)学习在多个数据源之间实现的物理链接和路径的属性。基于链路（路径）属性的拓扑结构和链路（路径）等价性应用于实体标识和表征任务。在这个项目中开发的工具可以通过项目网站（http://www.umiacs.umd.edu/research/CLIP/BFEnt02/）访问，生物学家可以应用这些工具来支持正在进行的癌症研究和糖尿病的科学研究，从而对医学研究和治疗产生广泛的影响。教育方面的影响包括为生物信息学研究人员开设关于数据管理解决方案的课程，以及为生物学家举办关于利用数据源进行实验规划的讲习班。