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

科学学科产生了大量的研究出版物，这些出版物具有巨大的价值，但远远超出了研究人员的消化和分析能力。 迫切需要自动（在广泛可用的一般知识库的帮助下）将研究文本转换为结构化的信息网络，在该网络上可以开发高级搜索和分析工具，以方便研究人员和从业人员快速查找知识，进行推理，甚至产生新的科学假设。该项目旨在开发一种新的数据到网络到知识（D2 N2 K）范式，通过整合半结构化和非结构化数据，将非结构化但相互关联的研究文本数据转化为可操作的知识。首先，有组织的异构信息网络（因此被称为结构网）的构造，然后在这样的有组织的网络上开发强大的挖掘机制。 该项目以生物医学科学为重点，研究以下方面的原理、方法和算法：（i）通过属性提取、关系类型和声明挖掘挖掘生物医学研究语料库，构建相对结构化的异构信息网络（称为MediNet）;以及（ii）通过图OLAP和任务引导嵌入，探索和挖掘如此构建的网络。该项目开发了一个可扩展的框架，以促进基于文献的科学研究。关于MediNet构建和探索的研究不仅影响生物医学研究，而且还巩固了这种从数据到网络到知识的方法，易于转移到其他领域，用于将这些领域的大量非结构化文本数据自动转换为结构化和可操作的知识。

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.