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

CRII：III：向SPARQL添加探索性统计分析和预测支持

• 批准号: 1850097
• 负责人: Charalampos Chelmis
• 金额: $ 17.46万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850097&HistoricalAwards=false
• 关键词: CRII; III; Adding; Exploratory; Statistical

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.

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。除了网络搜索和诸如虚拟助理之类的最新技术之外，语义丰富的数据出现在广泛的应用领域中，包括但不限于生物信息学、神经科学、医疗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。该项目旨在大幅减少访问和分析语义丰富的数据所需的工作量和专业知识，从而增加可以从这些数据中受益的应用程序的范围。此外，该奖项将支持博士生和本科生的发展，以及奥尔巴尼的纽约州立大学关于语义丰富数据统计分析的研究生课程。第一个推力将开发一个通用的方法来支持简单和直观的，但功能，视觉语义查询。特别是，算法将被设计为自动构建语义查询从关键字通过类似搜索的接口提供。第二个重点将开放语义数据的统计探索、分析和预测建模。具体来说，基于统计学和信息论思想的新原语将直接纳入SPARQL，这是一种用于检索和发现语义数据关系的查询语言。将设计一个本体来支持替代统计操作。在这种情况下，查询回答的计算方法和算法将被开发。该奖项反映了NSF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。

项目成果

Recipe Networks and the Principles of Healthy Food on the Web

食谱网络和网络健康食品的原则

• DOI: 10.1609/icwsm.v17i1.22129
• 发表时间: 2023
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Chelmis, Charalampos;Gergin, Bedirhan
• 通讯作者: Gergin, Bedirhan

Hierarchical MultiClass AdaBoost

分层多类 AdaBoost

• DOI: 10.1109/bigdata52589.2021.9671291
• 发表时间: 2021
• 期刊: 2021 IEEE International Conference on Big Data (Big Data
• 作者: Chelmis, Charalampos;Qi, Wenting
• 通讯作者: Qi, Wenting

Noisy Label Detection and Counterfactual Correction

• DOI: 10.1109/tai.2023.3271963
• 发表时间: 2024-02
• 期刊: IEEE Transactions on Artificial Intelligence
• 作者: Wenting Qi;C. Chelmis

Label Denoising and Counterfactual Explanation with A Plug and Play Framework

• DOI: 10.1109/bigdata55660.2022.10020488
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Wenting Qi;C. Chelmis

Robust Learning with Noisy Label Detection and Counterfactual Correction

• DOI: 10.1109/bigdata55660.2022.10020228
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Wenting Qi;C. Chelmis