مقاله انگلیسی استنباط در جناحهای تاریخی براساس شبکه چند لایه شخصیتهای تاریخی
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مقاله انگلیسی استنباط در جناحهای تاریخی براساس شبکه چند لایه شخصیتهای تاریخی

عنوان فارسی مقاله: استنباط در جناحهای تاریخی براساس شبکه چند لایه شخصیتهای تاریخی
عنوان انگلیسی مقاله: Inference on historical factions based on multi-layered network of historical figures
مجله/کنفرانس: سیستم های خبره با برنامه های کاربردی - Expert Systems with Applications
رشته های تحصیلی مرتبط: تاریخ
کلمات کلیدی فارسی: تجزیه و تحلیل داده های بزرگ تاریخی ، شناسایی جنبه های تاریخی ، شبکه چند لایه شخصیت های تاریخی ، یادگیری نیمه نظارت شده
کلمات کلیدی انگلیسی: Historical big data analysis, Historical faction identification, Multi-layered network of historical figures, Semi-supervised learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.eswa.2020.113703
دانشگاه: Ajou University, Suwon, South Korea
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2021
ایمپکت فاکتور: 5.452 در سال 2020
شاخص H_index: 184 در سال 2021
شاخص SJR: 1.494 در سال 2020
شناسه ISSN: 0957-4174
شاخص Quartile (چارک): Q1 در سال 2020
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 9
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E15357
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
نوع رفرنس دهی: vancouver
فهرست انگلیسی مطالب

Highlights


Abstract


Keywords


1. Introduction


2. Background


3. Proposed method


4. Results and discussion


5. Conclusion


CRediT authorship contribution statement


Declaration of Competing Interest


Acknowledgements


References

نمونه متن انگلیسی مقاله

Abstract


With immense influx of historical data, quantitative inferences on history based on machine learning is becoming more prevalent, attracting many researchers. In particular, understanding the dynamics of historical factions is important as they shared academic beliefs, political views and interests, in which the interactions between the factions portray general political, social, and economic structure of a certain era. In recent years, studying such dynamics through network-based methods on human networks, constructed from genealogy data, have shown promising results. In this paper, we enhance the identification of historical factions by exploiting multi-layered network of historical figures. To understand the mechanisms of historical factions, it is pivotal to comprehend the change in relation between important historical events. The proposed method consists of constructing a multi-layered network of historical figures and applying semi-supervised learning framework to identify historical factions. The proposed method was applied to the classification of factions in the political turmoil occurred during the 15th to 16th century Korea.


 


1. Introduction

Many years of huge efforts from historians to create database for historical figures and events have led to easier access to a tremendous pile of records of the old days (Manabe, 1999, 2010; Lee, 2010, 2016; Lee, Lee, Kim, & Shin, 2018). The databases for historical data now provide convenient access to genealogy records, texts from literature, images of artifacts and so on. Following from the rapid growth in influx of historical data, many studies attempt to stretch from human-labored restricted inference to machineaided comprehensive approaches. Machine learning plays a key role in the latter approach, providing historians with data-driven inference. To give few examples, Malmi, Gionis, and Solin (2018) develops automated methods, using Naïve Bayes approaches, for inferring large-scale genealogical networks. In the domain of art history, deep convolution neural networks are often employed to classify style of fine-art collections (Bar, Levy, & Wolf, 2014; Cetinic, Lipic, & Grgic, 2018; Saleh & Elgammal, 2015). For textual data, Lansdall-Welfare et al. (2017) employs text mining techniques on 150 years of British newspapers from 1800–1950 to extract macroscopic trends in history and culture. In Rochat (2015), network analysis on character network from historical writing is employed. Furthermore, many visualization efforts (Liu, Dai, Wang, Zhou, & Qu, 2017; Lee, Campbell, & Chen, 2010) for historical data are carried out to aid in understanding the structure and contents of history.

From the vast domain of using machine learning for historical big data, studies on past faction politics may convey important inferences on political, social, and economic structure of a certain era. Throughout history, people who shared academic beliefs, political views and interests joined to form factions to pursue a particular aim or purpose (Belloni & Beller, 1976). Analysis on the prevalence of factions and their rivalries is crucial for understanding political decision patterns and power mechanisms of times long past. Furthermore, the political competition of factions may be viewed as competition for goods and services (Persico, Pueblita, & Silverman, 2011), which may yield important inferences in economic perspectives

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