作者混合共引网络对知识图谱绘制的改进研究

doi:10.13266/j.issn.0252-3116.2017.03.015

图书情报工作 ›› 2017, Vol. 61 ›› Issue (3): 118-124.DOI: 10.13266/j.issn.0252-3116.2017.03.015

作者混合共引网络对知识图谱绘制的改进研究

黄文彬¹, 蒙汪阳¹, 步一²

1. 北京大学信息管理系北京 100871;
2. 美国印第安纳大学信息学与计算机学院印第安纳布鲁明顿 47408

收稿日期:2016-09-28 修回日期:2016-12-11 出版日期:2017-02-05 发布日期:2017-02-05
通讯作者: 步一(ORCID:0000-0003-2549-4580),博士研究生,通讯作者,E-mail:buyi@iu.edu
作者简介:黄文彬(ORCID:0000-0002-9174-5467),副教授;蒙汪阳(ORCID:0000-0001-5719-8431),本科生
基金资助:
本文系中国科技信息研究所系所合作项目研究成果之一。

Improvements on Mapping Knowledge Domains by Using Hybrid Author Co-citation Network

Huang Wenbin¹, Meng Wangyang¹, Bu Yi²

1. Department of Information Management, Peking University, Beijing 100871;
2. School of Informatics and Computing, Indiana University, Bloomington, Indiana 47408

Received:2016-09-28 Revised:2016-12-11 Online:2017-02-05 Published:2017-02-05

摘要/Abstract

摘要： [目的/意义]作者共引网络分析（ACNA）是文献计量学中的重要分析方法，旨在通过寻找学术文献集合中作者之间的共引关系绘制出特定领域的知识图谱，进而指导科学研究。然而，ACNA的一个缺陷是其原始矩阵输入信息量过小。本文通过提出作者混合共引网络（HACNA），绘制更为精确的科学知识图谱。[方法/过程]鉴于不同种类的学术网络能为绘制知识图谱提供不同维度的信息，提高知识图谱绘制的精确性，本文以合著网络和引用网络为例，结合其他种类的学术网络在ACNA基础上进行精确科学知识图谱的绘制。[结果/结论]实证研究结果显示，与ACNA相比，HACNA绘制出的知识图谱在聚类过程中能够使得同类作者更为聚拢、不同类作者更为分散，从而提高了聚类效果和可视化程度。同时，HACNA绘制出的知识图谱还能够挖掘出更多细节。

关键词: 作者共引网络, 共引网络, 合著网络, 引文网络, 文献计量学

Abstract: [Purpose/significance] Author co-citation network analysis (ACNA) is an important method in bibliometrics which aims to map knowledge domains and to guide scientific research by considering cocitation relationships between author pairs in dataset (see reference #1). However, it is criticized that the amount of information in raw co-citation matrix is limited in ACNA. This paper proposes hybrid author co-citation network (HACNA) in order to map knowledge domains more accurately.[Method/process] Because the accuracy of mapping knowledge domains could be improved due to different perspectives provided by multiple scholarly networks, this paper combines other types of scholarly networks (taking coauthorship networks and citation networks as examples) into ACNA to show more accurate knowledge domain maps, which is call hybrid author co-citation network analysis (HACNA).[Result/conclusion] Results show that compared with ACNA, HACNA makes authors in the same category closer and authors in different categories farther in knowledge domain maps clustering, and thus promotes the clustering performance and visualization. Moreover, it is able to mine more details.

Key words: author co-citation network, co-citation network, coauthorship network, citation network, bibliometrics

中图分类号:

G250

黄文彬, 蒙汪阳, 步一. 作者混合共引网络对知识图谱绘制的改进研究[J]. 图书情报工作, 2017, 61(3): 118-124.

Huang Wenbin, Meng Wangyang, Bu Yi. Improvements on Mapping Knowledge Domains by Using Hybrid Author Co-citation Network[J]. LIS, 2017, 61(3): 118-124.

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作者混合共引网络对知识图谱绘制的改进研究

Improvements on Mapping Knowledge Domains by Using Hybrid Author Co-citation Network

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