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：生物学数据来源的语义图：当今生物学研究人员面临的实体身份和路径表征的基本问题正确地识别了生物实体的特定实例，例如，特定的基因或蛋白质，然后通过探索乘坐相间源的多重性来获得该实例的完整功能表征。尽管可用数据的多样性为攻击此问题提供了机会，但它伴随着利用和探索数据的困难。这个合作的跨学科的机构间研究项目利用了研究人员在包装纸和调解器技术方面的先前专业知识，并将特定领域的语义知识应用于此问题。目标是通过探索多个数据源对科学实体的正确识别和完整表征。该项目将解决三个任务：（1）构建包括唯一标识符在内的生物数据源的语义图；来源之间的链接；属性;以及搜索和查询； （2）在数据源，架构，域和实例级别上学习身份链接的概念来识别同一实例的等效标识符，以及（3）学习在多个数据源之间实现的物理链接和路径的属性。链接（路径）基于其属性的拓扑，链接（路径）等效性应用于实体身份和表征的任务。该项目中开发的工具将可以通过项目网站（http://www.umiacs.umd.edu/research/clip/bfent02/）访问，并且可以由生物学家应用于支持癌症研究和糖尿病的持续科学研究，从而在医疗研究和治疗中产生广泛的影响。教育影响包括有关生物信息学研究人员数据管理解决方案的课程，以及使用生物学家数据源进行实验计划的研讨会。