Evaluating significance of historical entities based on tempo-spatial impacts analysis using Wikipedia link structure

Y. Takahashi, H. Ohshima, Mitsuo Yamamoto, H. Iwasaki, S. Oyama, Katsumi Tanaka
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引用次数: 22

Abstract

We propose a method to evaluate the significance of historical entities (people, events, and so on.). Here, the significance of a historical entity means how it affected other historical entities. Our proposed method first calculates the tempo-spacial impact of historical entities. The impact of a historical entity varies according to time and location. Historical entities are collected from Wikipedia. We assume that a Wikipedia link between historical entities represents an impact propagation. That is, when an entity has a link to another entity, we regard the former is influenced by the latter. Historical entities in Wikipedia usually have the date and location of their occurrence. Our proposed iteration algorithm propagates such initial tempo-spacial information through links in the similar manner as PageRank, so the tempo-spacial impact scores of all the historical entities can be calculated. We assume that a historical entity is significant if it influences many other entities that are far from it temporally or geographically. We demonstrate a prototype system and show the results of experiments that prove the effectiveness of our method.
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基于维基百科链接结构时空影响分析的历史实体意义评价
我们提出了一种评估历史实体(人物、事件等)意义的方法。在这里,一个历史实体的意义是指它如何影响其他历史实体。我们提出的方法首先计算历史实体的时空影响。历史实体的影响因时间和地点的不同而不同。历史实体是从维基百科收集的。我们假设历史实体之间的维基百科链接代表影响传播。也就是说,当一个实体与另一个实体有联系时,我们认为前者受到后者的影响。维基百科中的历史实体通常有其出现的日期和地点。我们提出的迭代算法以类似于PageRank的方式通过链接传播这些初始的时间空间信息,因此可以计算出所有历史实体的时间空间影响得分。如果一个历史实体影响了在时间上或地理上与它相距甚远的许多其他实体,我们就认为它是重要的。我们演示了一个原型系统,并给出了实验结果,证明了我们方法的有效性。
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HT '22: 33rd ACM Conference on Hypertext and Social Media, Barcelona, Spain, 28 June 2022- 1 July 2022 HT '21: 32nd ACM Conference on Hypertext and Social Media, Virtual Event, Ireland, 30 August 2021 - 2 September 2021 HT '20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, USA, July 13-15, 2020 Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides. QualityRank: assessing quality of wikipedia articles by mutually evaluating editors and texts
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