Proto-OKN Theme 1: DREAM-KG: Develop Dynamic, REsponsive, Adaptive, and Multifaceted Knowledge Graphs to address homelessness with Explainable AI

Proto-OKN 主题 1：DREAM-KG：开发动态、响应式、自适应和多方面的知识图，通过可解释的人工智能解决无家可归问题

• 批准号: 2333703
• 负责人: Yuzhou Chen
• 金额: $ 149.69万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333703&HistoricalAwards=false
• 关键词: Proto; OKN; Theme; DREAM; KG

This project aims to create a knowledge graph that will provide a comprehensive understanding of the social, economic, and political factors that contribute to homelessness while triaging existing services and resources to support people experiencing homelessness. The primary users of the system will be people experiencing homelessness, front-line case workers, law enforcement agents, non-profit organizations, and federal agencies. The knowledge graph will equip users with essential insights to address the homelessness problem more effectively. This includes understanding core structural factors, effective intervention methods, community resources, cultural nuances across diverse backgrounds, and policy knowledge from local to federal levels. The project will utilize emerging topological data analysis tools, artificial intelligence (AI) models, and innovative approaches for data acquisition and analysis related to homelessness. The resulting knowledge graph will feature user-friendly user interfaces, offering easy access to data and insights, enhancing service delivery to homeless populations. By harnessing geometric theory, explainable AI, and ontology techniques, the project aims to advance understanding in AI applications, bridge the gap between AI and homelessness research, and offer novel solutions for homelessness prevention. The educational component of the project includes mentoring underrepresented groups and incorporating project outcomes into various courses, such as Topological Data Analysis, Data Semantics, and Social Work education. The project outcomes will be shared broadly, engaging multiple stakeholders including the affected communities, clinicians, policymakers, and researchers, aiming to address the national homelessness crisis and improve resource allocation and housing policies.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.

该项目旨在创建一个知识图，该图表将对社会，经济和政治因素提供全面的理解，这些因素有助于无家可归，同时为现有的服务和资源进行分类，以支持经历无家可归者的人们。该系统的主要用户将是经历无家可归者，一线案件工人，执法人员，非营利组织和联邦机构的人。知识图将使用户能够为无家可归的问题提供基本见解。这包括了解核心结构因素，有效的干预方法，社区资源，各种背景的文化细微差别以及从当地到联邦一级的政策知识。该项目将利用新兴的拓扑数据分析工具，人工智能（AI）模型以及与无家可归者有关的数据获取和分析的创新方法。所得的知识图将具有用户友好的用户界面，可轻松访问数据和见解，从而增强了服务到无家可归者的服务。通过利用几何理论，可解释的AI和本体论技术，该项目旨在提高对AI应用的理解，弥合AI和无家可归研究之间的差距，并为无家可归的预防提供新颖的解决方案。该项目的教育部分包括指导代表性不足的群体，并将项目成果纳入各种课程，例如拓扑数据分析，数据语义和社会工作教育。该项目的成果将广泛共享，吸引多个利益相关者，包括受影响的社区，临床医生，政策制定者和研究人员，旨在解决国家无家可归危机并改善资源分配和住房政策。这奖反映了NSF的统计任务，并被认为是值得的。通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。