Convergence Accelerator Phase I (RAISE): Leveraging Financial and Economic Data - Business OKN

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。这个融合加速器第一阶段项目的更广泛的影响和潜在的社会效益是为获取有关企业，创新和市场的基本知识奠定基础，并使用计算机科学的最新技术以易于使用的形式免费提供这些知识。该项目是商学院和计算机科学系教师之间的合作伙伴关系，并将吸引监管机构和金融技术公司的合作伙伴。 拟议中的商业开放知识网络（博肯）将为企业家提供必要的资源，使他们在创建小企业时充分了解竞争格局，使监管机构能够迅速发现问题，以帮助防止下一次金融危机，并使研究人员能够开发和测试理论，以改变我们国家的商业实践。利用博肯资源，新一代的学生和学者将能够将计算解决方案与金融、经济、数学和统计学的理论、模型和方法相结合，从而增加理解和更广泛的奖学金机会。开发博肯的项目工作将需要开发新的研究方法，这些方法可以联合收割机结合最先进的计算方法，利用有关业务领域的复杂而细致的知识来表示、链接和分析数据。该项目小组将开发专门针对商业和金融的计算工具，这些工具可以利用网上大量的非结构化数据，以及为监管或法律的目的提供的半结构化数据和时间序列数据集，以及具有标准标识符和元数据的参考数据集，以实现跨资源联合。业务专业知识将通过定义概念的具体本体、识别感兴趣的关键实体以及在一系列实际用例中验证提取的知识和下游预测来驱动这些计算工具。一个预期的技术成果是创建一个混合知识图，支持传统的符号知识表示和推理，通过捕获时间演化和语义关系的高维向量空间嵌入来增强，支持机器学习应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to lay the foundation for capturing the essential knowledge about businesses, innovation, and markets and to use the latest techniques in computer science to make this knowledge freely available in easily usable forms. The project is a partnership between faculty in business schools and computer science departments and will engage partners in regulatory agencies as well as financial technology companies. The proposed Business Open Knowledge Network (BOKN) will provide the resources necessary for entrepreneurs to fully understand the competitive landscape as they create small businesses, allow regulators to quickly identify issues to help prevent the next financial crisis, and enable researchers to develop and test theories to transform our nation's business practices. Using the BOKN resource, a new generation of students and scholars will be able to blend computational solutions with theories, models, and methodologies from finance, economics, mathematics, and statistics leading to increased understanding as well as broader opportunities for scholarship.The project efforts to develop the BOKN will require the development of new research approaches that can combine state-of-the-art computational approaches for extracting, representing, linking, and analyzing data with complex and nuanced knowledge about the business domain. The project team will develop business and finance-specific computational tools that can leverage a wealth of unstructured data on the Web, as well as semi-structured data and time series datasets provided for regulatory or legal purposes, and reference datasets with standard identifiers and metadata that enable cross-resource federation. Business expertise will drive these computational tools by defining a concrete ontology of concepts, identifying the key entities of interest, and validating the extracted knowledge and downstream predictions in a series of practical use cases. One expected technical result is the creation of a hybrid knowledge graph that supports traditional symbolic knowledge representation and reasoning enhanced by high-dimensional vector space embeddings capturing temporal evolution and semantic relationships that support machine learning applications.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.