III:Small: A Logic-Based, Provenance-Aware System for Merging Scientific Data under Context and Classification Constraints

III:Small：基于逻辑、来源感知的系统，用于在上下文和分类约束下合并科学数据

• 批准号: 1118088
• 负责人: Bertram Ludaescher
• 金额: $ 47.92万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118088&HistoricalAwards=false
• 关键词: III; Small; Logic; Based; Provenance

There is a rich research literature on information integration (e.g., on data fusion, data integration, and data exchange, including schema matching, mapping, and composition), knowledge-representation, ontologies, and semantic web technologies. However, there has been little prior work on the related problem of merging annotated datasets that already have largely compatible schemas, but where data values of some fields can come from (or link to) different concept hierarchies (taxonomies). Combining datasets into a single, consistent representation is a prerequisite for addressing many important scientific questions (e.g. those that rely on data to be expressed at broad spatial, temporal, and taxonomic scales). In practice, scientists combine multiple datasets manually, a time-intensive and error-prone process. In many application domains (e.g., biodiversity, ecology, systematics) data are often annotated with concepts from different but interrelated taxonomies. For instance, scientists who wish to combine datasets that record the presence or absence of species at given locations are often faced with datasets that draw species names from different taxonomies. In such cases, merging datasets requires aligning the different taxonomies. However, even for aligned taxonomies (i.e., where formal articulation constraints are given), many different dataset merges are possible, including inconsistent or incomplete ones. These in turn can yield different or even contradictory outcomes in subsequent interpretations and downstream data analysis. The primary goals of this project are to develop new techniques at the interface of data integration, knowledge-representation, and reasoning, to empower scientists by giving them new tools for merging and 'logically debugging' taxonomies and annotated datasets. The proposed Euler toolkit will include a formal framework with a broad range of constraints and data types; novel provenance-based techniques to detect, explain, and repair inconsistencies in taxonomy alignments; and new techniques to reduce uncertainty in alignments. For further information see the project web site at the URL: http://www.daks.ucdavis.edu/projects/euler

关于信息集成有丰富的研究文献（例如，数据融合、数据集成和数据交换，包括模式匹配、映射和组合）、知识表示、本体和语义网技术。然而，合并已经具有很大程度上兼容的模式的注释数据集的相关问题，但其中一些字段的数据值可以来自（或链接到）不同的概念层次结构（分类），有很少的先前工作。将数据集组合成一个单一的，一致的表示是解决许多重要科学问题的先决条件（例如，那些依赖于在广泛的空间，时间和分类尺度上表达的数据）。在实践中，科学家们手动联合收割机组合多个数据集，这是一个耗时且容易出错的过程。在许多应用领域（例如，生物多样性、生态学、系统分类学）数据往往用不同但相互关联的分类学概念加以注释。例如，希望将记录特定地点物种存在或不存在的联合收割机数据集结合起来的科学家通常会面临从不同分类中提取物种名称的数据集。在这种情况下，合并数据集需要对齐不同的分类法。然而，即使对于对齐的分类法（即，在给出正式的表达约束的情况下），许多不同的数据集合并是可能的，包括不一致的或不完整的数据集合并。这些反过来又会在随后的解释和下游数据分析中产生不同甚至相互矛盾的结果。该项目的主要目标是在数据集成，知识表示和推理的界面上开发新技术，通过为科学家提供合并和“逻辑调试”分类法和注释数据集的新工具来增强科学家的能力。拟议中的欧拉工具包将包括一个正式的框架，具有广泛的约束条件和数据类型;新颖的基于出处的技术，以检测，解释和修复分类比对中的不一致性;以及减少比对不确定性的新技术。欲了解更多信息，请访问该项目网站的URL：http://www.daks.ucdavis.edu/projects/euler