I-Corps: Knowledge Graph Embeddings-based Explainable Artificial Intelligence for Enterprise Performance Management

I-Corps:用于企业绩效管理的基于知识图嵌入的可解释人工智能

基本信息

  • 批准号:
    2102803
  • 负责人:
  • 金额:
    $ 5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-02-01 至 2022-09-30
  • 项目状态:
    已结题

项目摘要

The broader impact/commercial potential of this I-Corps project is the development of an enterprise performance management (EPM) platform for investors, customers, suppliers, employees, and the community. The technology aims to broaden the scope of knowledge from financial-centric performance to an interdisciplinary framework of economic, social, psychological, and physical well-being concerning all stakeholders. In addition, the technology may democratize artificial intelligence (AI) to ordinary organizational managers who may not possess sophisticated analytics skills. The current AI models lack interactive and intuitive storytelling. Matching the hierarchical clustering of data with a causal knowledge graph, the proposed technology will prepare user data in a way that mimics a general manager’s intuitive thinking. The technology addresses a commercial gap in the market - that of a lack of prescriptive capability, that is, telling end-users what they should do. Data may be collected from different sources in an organization, so they are fragmented and the causal links are lost. The external source of a causal knowledge graph fills the gap by presenting and interpreting the hidden causal links in EPM data. The project seeks to help executives to prescribe interventions to enhance the well-being of all stakeholders.This I-Corps project is based on the development of a knowledge graph embeddings-based platform for statistical and machine learning models of enterprise performance management (EPM) data. The technology is designed to engage natural language processing models to convert a massive volume of scientific research in organizational science into a causal knowledge graph, which will be embedded into a visual analytics platform to structure and interpret enterprise management data. The goal is to help EPM users by explaining the hidden causal pathways visually and intuitively to enable improvements in organizational management. The proposed technology combines research outcomes across organizational and computer sciences and involves two innovations: a scientific knowledge graph on causes-and-effects related to organizational performance and a new knowledge graph embeddings-based visualization technique to enable explainable AI (XAI). Hierarchical clustering is used to explicate the descriptions of variables in data and organize these descriptions. Causal hypotheses are automatically developed based on the known causal links in the knowledge graph and then empirically tested in statistical and machine learning models.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.
这个I-Corps项目更广泛的影响/商业潜力是为投资者、客户、供应商、员工和社区开发一个企业绩效管理平台。该技术旨在将知识范围从以财务为中心的绩效扩展到涉及所有利益相关者的经济,社会,心理和身体健康的跨学科框架。 此外,该技术可能会将人工智能(AI)民主化到可能不具备复杂分析技能的普通组织管理人员。目前的人工智能模型缺乏交互式和直观的故事讲述。 将数据的层次聚类与因果知识图相匹配,所提出的技术将以模仿总经理直觉思维的方式准备用户数据。 这项技术解决了市场上的一个商业空白-缺乏规范能力,即告诉最终用户他们应该做什么。数据可能是从一个组织的不同来源收集的,因此它们是支离破碎的,因果联系也就丢失了。因果知识图的外部来源通过呈现和解释数据中隐藏的因果链接来填补差距。 该项目旨在帮助管理人员制定干预措施,以提高所有利益相关者的福祉。该I-Corps项目的基础是开发一个基于知识图嵌入的平台,用于企业绩效管理数据的统计和机器学习模型。 该技术旨在利用自然语言处理模型将大量组织科学的科学研究转化为因果知识图,并将其嵌入到可视化分析平台中,以构建和解释企业管理数据。 我们的目标是通过视觉和直观地解释隐藏的因果路径来帮助企业用户改进组织管理。 这项技术结合了组织科学和计算机科学的研究成果,涉及两项创新:一项是关于与组织绩效相关的因果关系的科学知识图,另一项是基于知识图嵌入的可视化技术,以实现可解释的人工智能(XAI)。层次聚类用于解释数据中变量的描述并组织这些描述。因果假设是根据知识图谱中已知的因果关系自动开发的,然后在统计和机器学习模型中进行经验测试。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而被认为值得支持。

项目成果

期刊论文数量(0)
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Wenwen Dou其他文献

Analysts aren't machines: Inferring frustration through visualization interaction
分析师不是机器:通过可视化交互推断挫败感
Recovering Reasoning Process From User Interactions
从用户交互中恢复推理过程
  • DOI:
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wenwen Dou;Charlotte;Viscenter;D. Jeong;Felesia Stukes;W. Ribarsky;H. Lipford;Remco Chang
  • 通讯作者:
    Remco Chang
Novel peptides based on sea squirt as biocide enhancers to mitigate biocorrosion of EH36 steel
基于海鞘的新型肽作为杀菌剂增强剂以减轻EH36钢的生物腐蚀
  • DOI:
    10.1016/j.bioelechem.2024.108890
  • 发表时间:
    2025-06-01
  • 期刊:
  • 影响因子:
    4.500
  • 作者:
    Jiahao Sun;Shihang Lu;Ming Cheng;Nianting Xue;Shiqiang Chen;Guangzhou Liu;Yuanyuan Gao;Li Lai;Wenwen Dou
  • 通讯作者:
    Wenwen Dou
Riboflavin-mediated Fesup0/sup-to-microbe electron transfer corrosion of EH40 steel by emHalomonas titanicae/em
由嗜泰坦盐单胞菌介导的核黄素介导的 Fes0 到微生物的电子转移对 EH40 钢的腐蚀
  • DOI:
    10.1016/j.corsci.2024.111981
  • 发表时间:
    2024-05-01
  • 期刊:
  • 影响因子:
    8.500
  • 作者:
    Shihang Lu;Lingqun Zhang;Nianting Xue;Shiqiang Chen;Muqiu Xia;Mengyu Fu;Yuanyuan Gao;Wenwen Dou
  • 通讯作者:
    Wenwen Dou
Helping users recall their reasoning process
帮助用户回忆他们的推理过程

Wenwen Dou的其他文献

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{{ truncateString('Wenwen Dou', 18)}}的其他基金

Collaborative Research: SaTC: CORE: Medium: Information Integrity: A User-centric Intervention
协作研究:SaTC:核心:媒介:信息完整性:以用户为中心的干预
  • 批准号:
    2323795
  • 财政年份:
    2023
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
PFI-TT: Artificial Intelligence System for Enterprise Performance Management that Integrates Causal Analytics and Human Expertise
PFI-TT:集成因果分析和人类专业知识的企业绩效管理人工智能系统
  • 批准号:
    2141124
  • 财政年份:
    2022
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
Phase II IUCRC UNC Charlotte Site: Center for Visual and Decision Informatics (CVDI)
第二阶段 IUCRC UNC 夏洛特站点:视觉与决策信息学中心 (CVDI)
  • 批准号:
    1747785
  • 财政年份:
    2018
  • 资助金额:
    $ 5万
  • 项目类别:
    Continuing Grant

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