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Temporal event searches based on event maps and relationships()
To satisfy a user’s need to find and understand the whole picture of an event effectively and efficiently, in this paper we formalize the problem of temporal event searches and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105191/ https://www.ncbi.nlm.nih.gov/pubmed/32288693 http://dx.doi.org/10.1016/j.asoc.2019.105750 |
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author | Cai, Yi Xie, Haoran Lau, Raymond Y.K. Li, Qing Wong, Tak-Lam Wang, Fu Lee |
author_facet | Cai, Yi Xie, Haoran Lau, Raymond Y.K. Li, Qing Wong, Tak-Lam Wang, Fu Lee |
author_sort | Cai, Yi |
collection | PubMed |
description | To satisfy a user’s need to find and understand the whole picture of an event effectively and efficiently, in this paper we formalize the problem of temporal event searches and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event relationships: temporal, content dependence, and event reference, that can be used to identify to what extent a component event is dependent on another in the evolution of a target event (i.e., the query event). The search results are organized as a temporal event map (TEM) that serves as the whole picture about an event’s evolution or development by showing the dependence relationships among events. Based on the event relationships in the TEM, we further propose a method to measure the degrees of importance of events, so as to discover the important component events for a query, as well as the several algebraic operators involved in the TEM, that allow users to view the target event. Experiments conducted on a real data set show that our method outperforms the baseline method Event Evolution Graph (EEG), and it can help discover certain new relationships missed by previous methods and even by human annotators. |
format | Online Article Text |
id | pubmed-7105191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71051912020-03-31 Temporal event searches based on event maps and relationships() Cai, Yi Xie, Haoran Lau, Raymond Y.K. Li, Qing Wong, Tak-Lam Wang, Fu Lee Appl Soft Comput Article To satisfy a user’s need to find and understand the whole picture of an event effectively and efficiently, in this paper we formalize the problem of temporal event searches and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event relationships: temporal, content dependence, and event reference, that can be used to identify to what extent a component event is dependent on another in the evolution of a target event (i.e., the query event). The search results are organized as a temporal event map (TEM) that serves as the whole picture about an event’s evolution or development by showing the dependence relationships among events. Based on the event relationships in the TEM, we further propose a method to measure the degrees of importance of events, so as to discover the important component events for a query, as well as the several algebraic operators involved in the TEM, that allow users to view the target event. Experiments conducted on a real data set show that our method outperforms the baseline method Event Evolution Graph (EEG), and it can help discover certain new relationships missed by previous methods and even by human annotators. Elsevier B.V. 2019-12 2019-09-25 /pmc/articles/PMC7105191/ /pubmed/32288693 http://dx.doi.org/10.1016/j.asoc.2019.105750 Text en © 2019 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Cai, Yi Xie, Haoran Lau, Raymond Y.K. Li, Qing Wong, Tak-Lam Wang, Fu Lee Temporal event searches based on event maps and relationships() |
title | Temporal event searches based on event maps and relationships() |
title_full | Temporal event searches based on event maps and relationships() |
title_fullStr | Temporal event searches based on event maps and relationships() |
title_full_unstemmed | Temporal event searches based on event maps and relationships() |
title_short | Temporal event searches based on event maps and relationships() |
title_sort | temporal event searches based on event maps and relationships() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105191/ https://www.ncbi.nlm.nih.gov/pubmed/32288693 http://dx.doi.org/10.1016/j.asoc.2019.105750 |
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