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

III：媒介：协作研究：挖掘和利用知识超立方体进行复杂应用

• 批准号: 1956151
• 负责人: Jiawei Han
• 金额: $ 40万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956151&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Mining

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.

知识库是指一种机器可读的结构，它存储关于各种实体(例如，组织、事件、基因)的知识，这有助于高效地寻找信息。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扁平结构不能捕获与不同上下文相关的复杂知识。为了使知识资源更容易找到、可访问、可互操作和可重复使用(公平)，该项目计划概念化一种新的结构--知识超立方体，用于组织和检索知识，这些知识可以支持不同领域的复杂应用。知识超立方体组织关于选定的重要维度(例如，时间、位置、条件)的知识，因此它允许人们在任何上下文中轻松地访问知识，封装不同的实体和事实，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，并通过在拥有大量文献和未解决的复杂任务之间架起一座桥梁，使广泛的领域受益。知识超立方体还可以支持教育创新，并为知识追踪等教育任务做出贡献。该建议的主要目标是形成一个从海量文本文档集合中挖掘知识超立方体并利用这些超立方体执行复杂的探索和预测任务的范例。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，设计了一种创新的弱监督方法，基于超立方体结构组织文本文档，提取开放的实体和关系信息，并以多维的方式组织特定于细胞和跨细胞的知识。其次，开发了新的求精方法，通过在知识超立方体内和与外部信息的交叉检查来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，知识超立方体推动着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。计划的范例在两个特定领域(即生物医学和新闻事件)实现，展示了知识超立方体的力量，使人们能够对这些领域进行新的洞察。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。

项目成果

Graph Clustering with Embedding Propagation

• DOI: 10.1109/bigdata50022.2020.9378031
• 发表时间: 2020-12
• 期刊: 2020 IEEE International Conference on Big Data (Big Data)
• 作者: Carl Yang;Liyuan Liu;Mengxiong Liu;Zongyi Wang;Chao Zhang;Jiawei Han

Corpus-Based Relation Extraction by Identifying and Refining Relation Patterns

通过识别和细化关系模式进行基于语料库的关系提取

• 发表时间: 2023
• 期刊: Springer
• 作者: Sizhe Zhou, Suyu Ge

Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks

• DOI: 10.1145/3442381.3450114
• 发表时间: 2021-02
• 期刊: Proceedings of the Web Conference 2021
• 作者: Xinyang Zhang;Chenwei Zhang;Xin Dong;Jingbo Shang;Jiawei Han

DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance

• DOI: 10.18653/v1/2023.findings-acl.14
• 发表时间: 2023
• 期刊: Blood
• 影响因子: 20.3
• 作者: Nishant Balepur;Shivam Agarwal;Karthik Venkat Ramanan;Susik Yoon;Diyi Yang;Jiawei Han

Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks

• DOI: 10.1145/3580305.3599376
• 发表时间: 2022-05
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Bowen Jin;Yu Zhang;Qi Zhu;Jiawei Han