上海交通大学学报

上海交通大学学报 >

2018 , Vol. 52 >Issue 10: 1357 - 1362

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2018.10.026

学报（中文）

AceMap学术地图与AceKG学术知识图谱——学术数据可视化

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  • 上海交通大学 智能物联网中心，上海 200240
张晔(1994-)，男，上海市人，硕士生，从事学术大数据、数据可视化和知识图谱研究.

基金资助

国家自然科学基金资助项目(61532012, 61325012, 61521062, 61602303, 61428205)

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AceMap Academic Map and AceKG Academic Knowledge Graph for Academic Data Visualization

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  • Research Center of Intelligent Internet of Things, Shanghai Jiao Tong University, Shanghai 200240, China
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摘要

学术大数据能为科研人员提供信息上的帮助.针对传统学术搜索引擎只提供搜索结果而不进行分析的情况，基于AceMap提出的学术地图的概念，设计了多种类型的学术地图，将学术信息可视化地展现给用户.针对SQL数据库多表联合查询的形式不利于生成用户定制的学术地图的问题，提出将其以三元组的形式储存为知识图谱AceKG.该方法有助于用户定制地图、异构地图绘制功能的实现，并且能通过嵌入算法为各种类型的实体生成相关性地图.通过对学术数据的可视化展示，能使用户更直观地了解到感兴趣的学术内容，提高了学术平台的实用性.

关键词： 学术地图; 数据可视化; 知识图谱

本文引用格式

张晔,贾雨葶,傅洛伊,王新兵 . AceMap学术地图与AceKG学术知识图谱——学术数据可视化[J]. 上海交通大学学报, 2018 , 52(10) : 1357 -1362 . DOI: 10.16183/j.cnki.jsjtu.2018.10.026

Abstract

Academic big data can provide help on information to researchers. While traditional academic search engines only offer search results without analysis, academic map is proposed based on AceMap, visualizing academic information to users in form of graph with various types of designed maps. When SQL-based database is not capable for generating customized academic map, AceKG is proposed to store data in form of triplets. AceKG can help realize the heterogeneous and customized map generator and can generate correlation map for any type of entity after embedding. Through the visualization of academic data, users are able to find points of interest more intuitively, thus improves the practicality of academic platform.

Key words： academic map; data visualization; knowledge graph

参考文献

［1］TAN Z, LIU C, MAO Y, et al. AceMap: A novel approach towards displaying relationship among academic literatures［C］//International Conference Companion on World Wide Web. Montreal: 25th International World Wide Web Conferences Steering Committee, 2016: 437-442. ［2］HU Y. Efficient, high-quality force-directed graph drawing［J］. Mathematica Journal, 1984, 10(1): 37-71. ［3］JACOMY M, BASTIAN M, HEYMANN S. Gephi: An open source software for exploring and manipulating networks［C］//International AAAI Conference on Weblogs and Social Media. San Jose: 3rd International AAAI Conference on Weblogs and Social Media, 2009. ［4］JACOMY M, VENTURINI T, HEYMANN S, et al. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software［J］. Plos One, 2014, 9(6): e98679. ［5］BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks［J］. Journal of Statistical Mechanics, 2008, 2008(10): 155-168. ［6］YANG B, YIH W T, HE X, et al. Embedding entities and relations for learning and inference in knowledge bases［C］//International Conference on Learning Representations. San Diego: 3rd International Conference on Learning Representations, 2015. ［7］JOLLIFFE I T. Principal component analysis ［M］. Berlin: Springer, 1986: 115-128.
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