科技文献挖掘工具平台与关键技术综述

doi:10.11871/jfdc.10-1649.2021.06.005

数据与计算发展前沿 ›› 2021, Vol. 3 ›› Issue (6): 60-80.

doi: 10.11871/jfdc.10-1649.2021.06.005

• 专刊:科学大数据挖掘与知识发现 • 上一篇下一篇

科技文献挖掘工具平台与关键技术综述

白如江*(),赵梦梦(),张玉洁(),董坤()

山东理工大学,信息管理研究院,山东淄博 255000

收稿日期:2021-11-16 出版日期:2021-12-20 发布日期:2022-01-26
通讯作者: 白如江*
作者简介:白如江,山东理工大学信息管理研究院,教授,博士,情报学硕士研究生导师。山东省高等学校青创人才引育计划“科技大数据研究创新团队”负责人,入选山东理工大学高层次人才“双百工程”第三层次。主持国家社科基金项目3项、中国博士后科学基金特别资助、中国博士后科学基金一等资助等项目,合作出版专著2部、发表论文70余篇,申请计算机软件著作权1项。主要研究领域为科技大数据挖掘、科技情报分析、智慧情报感知等。
本文中负责总体研究思路确定、论文框架设计;负责撰写“1科技文献挖掘发展脉络”与“5总结与展望”;论文最终统稿修改。
BAI Rujiang, Ph.D, professor of Institute of Information Mana-gement, Shandong University of Technology, master super-visor. He is the PI of “Science and Technology Big Data Resear-ch and Innovation Team” of Shandong Youth Talent Program, and selected as the third level of the “ShuangBai Project” of Shandong University of Technology. He has been granted 3 projects of the National Social Science Fund, special funding of the China Postdoctoral Science Fund, first-class funding of the China Postdoctoral Science Fund.
He has published 2 monographs and more than 70 papers and has applied 1 computer software copyright cooperatively. His research interests include S&T big data mining, S&T intelligence analysis, and smart intelligence perception.In this paper, he is responsible for drawing up the paper framework, writing “1.the history roadmap of S&T literature mining” and “5 Conclusion and Prospect” and paper revision and approval.E-mail: brj@sdut.edu.cn;|赵梦梦,山东理工大学信息管理研究院,硕士研究生,研究方向为文本挖掘与科技情报分析。
本文中负责“2 科技文献挖掘工具现状”和“3科技文献挖掘系统平台现状”的撰写。
ZHAO Mengmeng, is a graduate student of Information Mana-gement Institute of Shandong University of technology. Her research interests include text mining and scientific and techno-logical information analysis.
In this paper, she is responsible for writing “2 current situation of scientific and technological literature mining tools” and “3 The status of sci-tech document mining system platform ”. E-mail: zhaomeng199701@163.com;|张玉洁,山东理工大学信息管理研究院,硕士研究生,研究方向为文本挖掘与科技情报分析。
本文中负责“4 科技文献挖掘关键技术及其发展趋势”的撰写。
ZHANG Yujie is a graduate student of the Information Management Institute of Shandong University of Technology. His research interests include text mining and scientific and technological information analysis.
In this paper, he is responsible for writing “4 key technologies and development trends of scientific and technological literature mining”. E-mail: zyj1725@163.com;|董坤,山东理工大学信息管理研究院,讲师,硕士生导师,研究方向为科技情报分析。
本文中负责部分图表绘制与论文修改。
DONG Kun is a lecturer and master tutor of the Information Management Institute of Shandong University of Technology. Her research interests include scientific and technological information analysis.
In this paper, she is responsible for diagrams drawing and manuscript revising. E-mail: dongkun@sdut.edu.cn
基金资助:
国家社科基金一般项目“多源数据融合驱动的智慧情报感知研究”(21BTQ071)

Review on Scientific Literature Mining: Tools and Technologies

BAI Rujiang(),ZHAO Mengmeng(),ZHANG Yujie(),DONG Kun()

Institute of Information Management, Shandong University of Technology, Zibo, Shandong 255000, China

Received:2021-11-16 Online:2021-12-20 Published:2022-01-26
Contact: BAI Rujiang

摘要/Abstract

摘要：

【目的】对科技文献挖掘的主要工具、系统平台和关键技术进行全面系统梳理,指出未来发展趋势,为相关研究提供参考。【方法】通过网络和文献调研等方法梳理科技文献挖掘的历史发展脉络,总结科技文献挖掘的主要工具、系统平台及其特点,从平台功能、数据类型、可视化功能等维度进行了对比分析,重点介绍科技文献挖掘的关键技术及其发展前沿。【结果】论文详细阐述了科技文献挖掘“从哪里挖、用什么工具挖、怎么挖”的问题,并指出科技文献挖掘的数据源逐步向多源数据融合和细粒度知识组织方向发展,科技文献语义知识图谱构建是目前研究的热点话题,图神经网络、预训练模型、对抗学习网络等深度学习模型是当前科技文献挖掘的前沿关键技术,因果推断方法正在逐步成为前沿方向。【结论】随着大数据、人工智能的持续深入发展,科技文献挖掘将借助数据和技术红利在科技情报决策等具体应用场景发挥更大价值。

关键词: 科技文献挖掘, 文献挖掘工具平台, 文献挖掘关键技术, 前沿进展

Abstract:

[Objective] By reviewing on the main tools, system platforms and key technologies of scientific and technological literature mining, this paper presents the future development trend and the reference to the relevant researches.[Methods] By literature review and network survey, this paper summarizes the historical roadmap of scientific and technological literature mining, introduces the main tools, system platforms, and characteristics of scientific and technological literature mining, makes a comparative analysis from the aspects of platform function, data type, and processing ability, and focuses on the key technologies and the state of art of the scientific and technological literature mining. [Results] This paper resolves the problem of "where to mine, what to mine, and how to mine" in scientific and technological literature mining. The data source of scientific and technological literature mining is evolving towards multi-source data fusion and fine-grained knowledge organization. The construction of semantic knowledge map of scientific and technological literature is a hot topic. The graph neural network, pre-trained model, generative adversarial networks are the frontier key technologies. The causal inference methods are becoming the frontier direction. [Conclusions] With the continuous in-depth development of big data and artificial intelligence, scientific and technological literature mining is of a great value in specific application scenarios such as scientific and technological information decision-making with the help of data and technological benefits.

Key words: scientific literature mining, literature mining tool platform, key technologies of literature mining, research frontiers

白如江*,赵梦梦,张玉洁,董坤. 科技文献挖掘工具平台与关键技术综述[J]. 数据与计算发展前沿, 2021, 3(6): 60-80.

BAI Rujiang,ZHAO Mengmeng,ZHANG Yujie,DONG Kun. Review on Scientific Literature Mining: Tools and Technologies[J]. Frontiers of Data and Computing, 2021, 3(6): 60-80.

图/表 6

图1

表1

图2

表2

科技文献数据与挖掘分析平台"

数据服务平台名称	数据类型	文献服务功能	文献挖掘服务
Dimensions	文献、数据集、基金、专利、临床试验、政策文档等	1.提供各种研究数据之间的链接 2.支持多种API 3.提供不限制用户的综合数据库,并提供多种筛选选项 4.提供项目资助数据	1.提供语义检索和本体论检索 2.基于元数据将出版物和引文与基金、专利、临床实验、数据集和政策论文链接 3.提供文献引文网络分析
LENS	专利记录、学术数据、生物序列和文档链接	1.提供强大的专利、学术数据检索与分析功能 2.提供用户收藏的动态跟踪功能 3.提供学术机构与企业的影响力映射	1.聚合元数据,提供链接和映射元数据之间的关系 2. 提供多样化专利检索与分析服务 3.实体歧义消岐
Europe PMC	生命科学领域文献摘要和全文	1.提供文献与外部研究资源的链接 2.提供生命科学研究相关丰富的文献数据,包括预印 3.提供用户作品展示 4提供注释检索功能 5.提供文献背后研究数据 6.支持通过文章API访问出版物和相关信息	1.支持注释检索 2.支持出版物与研究数据和外部相关数据的链接,智能引文、同行评议材料等外部相关数据相链接 3.提供对开放内容和元数据的API访问,构建文献的多样化分析应用
ArrowSmith知识发现系统	医学领域科技文献	根据文献数据集提供有价值的科学假设	支持对不相关或相关性较弱的文献集进行文献知识挖掘
AMiner	文献、数据集、基金、专利、临床试验、政策文档等	1.提供科技文献、作者等学术信息检索功能 2.提供专利、科技文献等语义检索、语义分析服务 3.提供研究学者档案管理和挖掘服务	1.支持科研人员、科技文献、学术活动数据关联检索 2.支持学科领域发展趋势分析
脑科学知识引擎（Linked Brain Data）	神经元数据	1.提供神经元数据和知识提取、表示、可视化服务等 2.为相关大脑数据推理提供服务	支持神经元数据和知识的语义检索
干细胞知识发现平台	干细胞核心专利、基金项目、科学实验、热点论文等数据	1.提供科技大数据集成服务 2.提供知识计算服务 3.提供基于大数据知识计算的知识发现服务	1.支持科技文献知识内涵挖掘、知识语义关联等 2.支持领域热点前沿探测 3.支持科研画像服务

表2

表3

图3

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工具名称	开发者	支持的科技文献数据源	支持的挖掘分析维度	可视化分析功能
CiteSpace	陈超美	WoS数据库、PubMed数据库、Derwent 数据库、CNKI数据库、CSSCI数据库、Scopus数据库、Google Scholar 数据库、NSF Awards数据库等	合作：作者、机构、国家;共现：特征词、关键词、学科;共引：文献、作者、期刊;耦合：文献、基金	聚类视图、时间线视图（鱼眼视图）、时区视图
VOSviewer	Van Eck、Waltman	WoS数据库、Scopus数据库、Dimen-sions数据库、PubMed数据库等	文献、作者、国家、学科重要术语	标签视图、密度视图、聚类密度视图、散点视图
SCI²	Katy Börner及其团队	WoS数据库等	文献、作者、	主题地图分析、有向网络分析

挖掘对象	挖掘方法
文献计量分析	影响因子分析、被引频次分析、h指数、布拉德福定律、洛特卡定律、引文分析、非相关文献知识发现、社会网络分析方法
科技文献文本内容挖掘	简单规则	词频分析、One-Hot向量编码、词袋模型（BOW）、N-Grams、共词网络分析、作者合作共现分析、机构合作共现分析
	统计机器学习	关键词抽取：TF-IDF、TextRank、CRF、HMM、SVM
		语言模型：Word2Vec、Item2Vec、DeepWalk、Node2Vec
		主题模型：LDA、LSA、STM、LDA2Vec
	深度学习	经典模型：CNN、RNN、LSTM、Attention、Transformer
		预训练模型：BERT、SciBERT、RoBERTa、SpanBERT、ERNIE、 GPT-3
		图神经网络：GCN、GAT、VGAE、GraphSAGE
智能推理挖掘	知识图谱	Google Knowledge Graph、Microsoft Academic Graph、OpenAca-demic Grap、AceKG、SciKGraph
	因果智能	因果涌现
	因果智能	因果推断：随机对照试验、准实验设计、倾向得分匹配法、断点回归、结构因果模型、因果机器学习、反事实推理

科技文献挖掘工具平台与关键技术综述

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