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

这个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、员工和社区开发一个企业绩效管理(EPM)平台。这项技术旨在将知识范围从以财务为中心的绩效扩大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，这项技术可能会将人工智能(AI)大众化，提供给可能不具备复杂分析技能的普通组织经理。目前的人工智能模式缺乏互动性和直观性的故事讲述。将数据的层次聚类与因果知识图相匹配，建议的技术将以一种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缺口--缺乏规范能力，即告诉最终用户他们应该做什么。数据可能是从组织中的不同来源收集的，因此它们是零散的，并且失去了因果联系。因果知识图的外部来源通过显示和解释EPM数据中隐藏的因果链接来填补这一空白。该项目旨在帮助高管制定干预措施，以增进所有利益相关者的福祉。该i-Corps项目基于开发一个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。该技术旨在利用自然语言处理模型，将组织科学中的大量科学研究转换为因果知识图，并将其嵌入可视化分析平台，以构建和解释企业管理数据。其目标是通过直观和直观地解释隐藏的因果路径来帮助EPM用户，以改进组织管理。该技术结合了组织和计算机科学的研究成果，涉及两个创新：一个是与组织绩效相关的因果关系的科学知识图，另一个是基于知识图嵌入的可视化技术，以实现可解释人工智能(XAI)。层次聚类用于阐明数据中变量的描述并组织这些描述。因果假设是基于知识图谱中的已知因果链接自动开发的，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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.