III: Medium: Collaborative Research: StructNet: Constructing and Mining Structure-Rich Information Networks for Scientific Research

III：媒介：协作研究：StructNet：为科学研究构建和挖掘结构丰富的信息网络

• 批准号: 1705169
• 负责人: Yizhou Sun
• 金额: $ 40万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1705169&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; StructNet

Science disciplines have been generating huge volumes of research publications, which are of tremendous value but far beyond researchers' capacity to digest and analyze. There is a critical need to automatically (with the help of widely available, general knowledge-bases) transform research text into structured information networks on which advanced search and analytics tools can be developed to facilitate researchers and practitioners to quickly locate knowledge, make inferences, and even generate new scientific hypotheses.This project aims at developing a new data-to-network-to-knowledge (D2N2K) paradigm to transform massive, unstructured but interconnected research text data into actionable knowledge, by integrating semi-structured and unstructured data. First, organized heterogeneous information networks (hence called StructNet) are constructed, and then powerful mining mechanisms on such organized networks are developed. With a focus on biomedical sciences, the project investigates the principles, methodologies and algorithms for (i) construction of relatively structured heterogeneous information networks (called MediNet) by mining biomedical research corpora via attribute extraction, relation typing, and claim mining, and (ii) exploration and mining of the networks so constructed via graph OLAP and task-guided embedding. The project develops an extensible framework to facilitate literature-based scientific research. The study on construction and exploration of MediNet not only impacts biomedical research but also consolidates this data-to-network-to knowledge methodology, readily to be transferred to other domains, for automatic transformation of massive unstructured text data in those domains into structured and actionable knowledge.

科学学科一直在产生大量的研究出版物，它们具有巨大的价值，但远远超出了研究人员的消化和分析能力。 There is a critical need to automatically (with the help of widely available, general knowledge-bases) transform research text into structured information networks on which advanced search and analytics tools can be developed to facilitate researchers and practitioners to quickly locate knowledge, make inferences, and even generate new scientific hypotheses.This project aims at developing a new data-to-network-to-knowledge (D2N2K) paradigm to transform massive, unstructured but interconnected通过整合半结构化和非结构化数据，将文本数据研究成可行的知识。首先，构建了有组织的异质信息网络（因此称为structnet），然后开发了这种有组织的网络上的强大采矿机制。 该项目着重于生物医学科学，调查了（i）通过属性提取，关系键入以及索赔挖掘以及（II）勘探和矿场构建的Olap和Tasking和Task-Gu Olap和Task-Gu Olap和Task-Gu Olap和Tasked和Tasks的构建的原理，方法和算法，以（i）构建相对结构化的异质信息网络（称为MEDINET），以及通过构建和矿场构建的Olap和任务。该项目开发了一个可扩展的框架，以促进基于文学的科学研究。关于MEDINET的构建和探索的研究不仅会影响生物医学研究，而且还巩固了这种数据到网络对知识方法，很容易将其转移到其他领域，以自动转换这些领域中的大规模非结构化文本数据，以使结构化和可行的知识转换。

项目成果

GLSearch: Maximum Common Subgraph Detection via Learning to Search

• 发表时间: 2020-02
• 作者: Yunsheng Bai;Derek Xu;Yizhou Sun;Wei Wang

Heterogeneous Graph Transformer

• DOI: 10.1145/3366423.3380027
• 发表时间: 2020-01-01
• 期刊: WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
• 作者: Hu, Ziniu;Dong, Yuxiao;Sun, Yizhou
• 通讯作者: Sun, Yizhou

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

• DOI: 10.1145/3394486.3403275
• 发表时间: 2020-06
• 期刊: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Zhiping Xiao;Weiping Song;Haoyan Xu;Zhicheng Ren;Yizhou Sun

Heterogeneous Information Networks: the Past, the Present, and the Future

• DOI: 10.14778/3554821.3554901
• 发表时间: 2022-08
• 期刊: Proc. VLDB Endow.
• 作者: Yizhou Sun;Jiawei Han;Xifeng Yan;Philip S. Yu;Tianyi Wu

Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs

• DOI: 10.1145/3534678.3539350
• 发表时间: 2022-01-01
• 期刊: PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022
• 作者: Iyer, Roshni G.;Bai, Yunsheng;Sun, Yizhou
• 通讯作者: Sun, Yizhou