Discovering Latent Threads in Entity Histories

Author:

Duan YijunORCID,Jatowt Adam,Tanaka Katsumi

Abstract

AbstractKnowledge of entity histories is often necessary for comprehensive understanding and characterization of entities. Yet, the analysis of an entity’s history is often most meaningful when carried out in comparison with the histories of other entities. In this paper, we describe a novel task of history-based entity categorization and comparison. Based on a set of entity-related documents which are assumed as an input, we determine latent entity categories whose members share similar histories; hence, we are effectively grouping entities based on the correspondences in their historical developments. Next, we generate comparative timelines for each determined group allowing users to elucidate similarities and differences in the histories of entities. We evaluate our approach on several datasets of different entity types demonstrating its effectiveness against competitive baselines.

Funder

Japan Society for the Promotion of Science

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computational Mechanics

Reference43 articles.

1. Arora S, Liang Y, Ma T (2016) A simple but tough-to-beat baseline for sentence embeddings

2. Bairi RB, Carman M, Ramakrishnan G (2015) On the evolution of Wikipedia: dynamics of categories and articles. In: AAAI

3. Bamman D, Smith NA (2014) Unsupervised discovery of biographical structure from text. TACL 2:363–376

4. Blanco R, Cambazoglu BB, Mika P, Torzec N (2013) Entity recommendations in web search. In: ISWC. Springer, pp 33–48

5. Brin S, Page L (2012) Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput Netw 56(18):3825–3833

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Contrastive text summarization: a survey;International Journal of Data Science and Analytics;2023-08-09

2. EXPERIENCE: Algorithms and Case Study for Explaining Repairs with Uniform Profiles over IoT Data;Journal of Data and Information Quality;2021-04-27

3. IoT Data Quality;Proceedings of the 29th ACM International Conference on Information & Knowledge Management;2020-10-19

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3