CRII: III: Adding Exploratory Statistical Analysis and Prediction Support to SPARQL

搜索引擎使用语义注释来提供搜索结果，这些结果隐藏在熟悉的搜索框界面后面。除了网络搜索和虚拟助手等最新技术之外，语义丰富的数据出现在广泛的应用领域，包括但不限于生物信息学、神经科学、卫生保健以及社会和心理科学。然而，对语义数据的访问仅限于那些非常熟悉语义Web技术、标准和协议、数据格式和查询语言的人员。该项目旨在大大减少访问和分析语义丰富的数据所需的工作和专业知识，从而增加可以从这些数据中受益的应用程序的范围。此外，该奖项将支持博士和本科生的发展，以及纽约州立大学奥尔巴尼分校关于语义丰富数据统计分析的研究生课程。该项目的技术目标分为两个重点。第一个重点将开发一种通用方法来支持简单、直观、但功能强大的可视化语义查询。特别是，算法将被设计为通过类似搜索的界面提供的关键字自动构建语义查询。第二个重点是对语义数据的统计探索、分析和预测建模。具体来说，基于统计和信息论思想的新原语将被直接合并到SPARQL中，SPARQL是用于从语义数据中检索和发现关系的查询语言。将设计一个本体来支持可选的统计操作。在这种情况下，将开发用于查询回答的计算方法和算法。这些研究目标将由一项综合评价计划加以补充。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Search engines use semantic annotations to provide search results, which remain well hidden behind the familiar search box interface. Beyond web search and recent technologies such as virtual assistants, semantically enriched data appear in a wide spectrum of application domains, including but not limited to, bioinformatics, neuroscience, health care, and social and psychological sciences. Access to semantic data, however, has been restricted to those intimately familiar with Semantic Web Technologies, standards, and protocols, data formats, and query languages. This project aims to substantially reduce the effort and expertise required to access and analyze semantically enriched data, and therefore increase the range of applications that can benefit from such data. Furthermore, this award will support the development of PhD and undergraduate students, and a graduate-level course on the statistical analysis of semantically enriched data at the State University of New York at Albany.The technical aims of the project are divided into two thrusts. The first thrust will develop a general approach to support simple and intuitive, yet functional, visual semantic querying. In particular, algorithms will be devised to automatically construct semantic queries from keywords provided through a search-like interface. The second thrust will open up the statistical exploration, analysis, and predictive modeling of semantic data. Specifically, new primitives based on ideas from statistics and information theory will be incorporated directly into SPARQL, the query language for retrieving, and discovering relationships from semantic data. An ontology will be designed to support alternative statistical operations. Computational methods and algorithms for query answering in this setting will be developed. These research aims will be complemented by a comprehensive evaluation plan.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.