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

• 批准号: 2102803
• 负责人: Wenwen Dou
• 金额: $ 5万
• 依托单位: University of North Carolina at Charlotte
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102803&HistoricalAwards=false
• 关键词: Corps; Knowledge; Graph; Embeddings; based

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.

该I-Corps项目的更广泛的影响/商业潜力是为投资者，客户，供应商，员工和社区的企业绩效管理（EPM）平台开发。该技术旨在将知识的范围从以财务表现为中心的绩效扩大到有关所有利益相关者的经济，社会，心理和身体福祉的跨学科框架。此外，该技术可能会将人工智能（AI）民主化为可能没有复杂分析技能的普通组织经理。当前的AI模型缺乏互动和直观的讲故事。拟议的技术将数据的分层聚类与因果知识图匹配，将以模仿总经理的直觉思维的方式来准备用户数据。该技术解决了市场上的商业差距 - 缺乏规定能力的差距，也就是说，告诉最终用户他们应该做什么。数据可能是从组织中的不同来源收集的，因此它们是分散的，灾难性的联系丢失。灾难性知识图的外部来源通过在EPM数据中介绍和解释隐藏的灾难性链接来填补空白。该项目旨在帮助高管保留干预措施，以增强所有利益相关者的福祉。此I-Corps项目基于开发基于知识图的基于知识图嵌入的平台，用于企业绩效管理（EPM）数据的统计和机器学习模型。该技术旨在参与自然语言处理模型，以将组织科学中的大量科学研究转换为因果知识图，该研究将嵌入到视觉分析平台中，以结构和解释企业管理数据。目的是通过视觉和直观地解释隐藏的因果途径来帮助EPM用户，以改善组织管理。拟议的技术结合了组织和计算机科学之间的研究成果，并涉及两项创新：有关与组织绩效有关的原因和效应的科学知识图，以及一种新的知识图嵌入式可视化技术，以启用可解释的AI（XAI）。层次聚类用于阐明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中的已知cust链联系自动开发的，然后在统计和机器学习模型中进行了紧急测试。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估标准通过评估来获得的支持。