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（在广泛可用的通用知识库的帮助下）将研究文本转换为结构化的信息网络，在该信息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者快速定位知识，做出推断，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（D2N2K）范式，通过整合半结构化和非结构化数据，将大量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，构建有组织的异构信息网络（StructNet），然后在这种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，研究了以下方面的原则、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mediinet）； （ii）通过图OLAP和任务导向嵌入对构建的网络进行探索和挖掘。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。MediNet的构建和探索不仅影响了生物医学研究，而且巩固了这种数据-网络-知识的方法，易于转移到其他领域，以便将这些领域中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 volume of research publications, which is 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 hypothesis.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.