III: Medium: Collaborative Research: Mining and Leveraging Knowledge Hypercubes for Complex Applications

知识库是指一种机器可读的结构，它存储了关于各种实体（如组织、事件、基因）的知识，便于高效地查找信息。在许多领域中，知识随上下文而变化，现有知识库通常采用的平面结构无法捕获与不同上下文相关的复杂知识。为了使知识资源更易于查找、访问、互操作和重用（FAIR），该项目计划概念化一种新的结构，即知识超立方体，用于组织和检索可以支持各个领域复杂应用程序的知识。知识超立方体根据选定的重要维度（如时间、地点、条件）对知识进行组织，从而使人们能够在任何情况下轻松获取知识，封装独特的实体和事实，并进行跨维度的比较和推理。该项目影响了人们发现和使用知识的方式，推进了基于知识的数据分析方法，并通过在它们之间建立桥梁，使具有大量文献和未解决的复杂任务的广泛领域受益。知识超多维数据集还可以支持教育创新，并有助于知识追踪等教育任务。本提案的主要目标是形成一个从大量文本文档中挖掘知识混合数据集的范例，并利用这些混合数据集进行复杂的探索和预测任务。为了实现这一目标，该项目解决了一系列技术挑战。首先，为了从海量文本中自动构建知识超立方体，设计了创新的弱监督方法，基于超立方体结构组织文本文档，提取开放实体和关系信息，多维地组织特定于细胞和跨细胞的知识。其次，开发了新的改进方法，通过在超立方体内部和外部信息进行交叉检查，自动验证知识超立方体中单元内和单元间的信息质量。第三，知识超立方体推动了新的发现和学习任务的发展。特别是，该项目引入了一个自动知识搜索管道，用于利用知识超立方体进行下游预测任务，以及一个假设生成方法，用于对概念之间的未知关联进行评分。规划的范式在两个特定领域（即生物医学和新闻事件）中实现，展示了知识超立方体的力量，可以对这些领域产生新的见解。该奖项反映了美国国家科学基金会的法定使命，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，被认为值得支持。

Knowledge repository refers to a machine-readable structure that stores knowledge about various entities (e.g., organizations, events, genes), which facilitates efficient information seeking. In many domains, knowledge varies with respect to contexts, and a flat structure that is commonly adopted by existing knowledge repositories cannot capture the complicated knowledge associated with different contexts. To make knowledge resources more findable, accessible, interoperable, and reusable (FAIR), this project plans to conceptualize a new structure, Knowledge Hypercube, for organizing and retrieving knowledge that could support complex applications in various domains. A knowledge hybercube organizes knowledge with respect to selected important dimensions (e.g., time, locations, conditions), and thus it allows people to easily access knowledge in any context, encapsulate distinctive entities and facts, and conduct cross-dimensional comparison and inference. This project impacts how people find and use knowledge, advances knowledge-based data analytics approaches, and benefits a wide range of domains which have gigantic literature and unsolved complex tasks by building a bridge between them. Knowledge hypercubes can also support educational innovation and contributes to educational tasks such as knowledge tracing. The major objective of this proposal is to form a paradigm of mining knowledge hybercubes from massive collection of text documents and leveraging such hybercubes for complex exploration and prediction tasks. To meet this goal, this project tackles a series of technical challenges. First, to automatically construct a knowledge hypercube from massive texts, innovative weakly supervised approaches are designed to organize text documents based on the hypercube structure, extract open entity and relationship information and organize cell-specific and cross-cell knowledge in a multi-dimensional manner. Second, novel refinement approaches are developed to automatically verify the information quality within and across cells in knowledge hypercubes by cross-checking within the hypercubes and with external information. Third, knowledge hypercubes motivate the development towards new discovery and learning tasks. In particular, the project introduces an automatic knowledge search pipeline for leveraging knowledge hypercubes for downstream prediction tasks, and a hypothesis generation approach for scoring unknown associations between concepts. The planned paradigm is realized in two specific domains (i.e., biomedical and news events), demonstrating the power of knowledge hypercubes to enable new insights into these domains.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.