Temporal event searches based on event maps and relationships

Yi CAI; Haoran XIE; Raymond Y.K. LAU; Qing LI; Tak-lam WONG; Fu Lee WANG

doi:10.1016/j.asoc.2019.105750

Temporal event searches based on event maps and relationships

Yi CAI
, Haoran XIE^*
, Raymond Y.K. LAU
, Qing LI
, Tak-lam WONG
, Fu Lee WANG

^*Corresponding author for this work

Department of Computing and Decision Sciences

Research output: Journal Publications › Journal Article (refereed) › peer-review

8 Citations (Scopus)

Abstract

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.

Original language	English
Article number	105750
Journal	Applied Soft Computing
Volume	85
Early online date	25 Sept 2019
DOIs	https://doi.org/10.1016/j.asoc.2019.105750
Publication status	Published - Dec 2019

Bibliographical note

This article is an extension version of a conference proceeding published in Cai et al. (2013) [1], and some notations, definitions, formulations, descriptions, figures, tables and so on are reused to make new contributions smoothly in this article. The new contributions of this article are (1) new methods of event ranking and temporal event map construction; (2) algebraic operations of the event map; (3) more examples, case studies, and metrics in experiments; and (4) more comprehensive literature review and descriptions.

© 2019 Elsevier B.V. All rights reserved.

Funding

The research presented in this paper has been supported by the General Research Fund from the Research Grants Council (Project No. 11525716), National Natural Science Foundation of China (Grant no. 61300137), Guangdong Natural Science Foundation, China (no. S2013010013836), the Fundamental Research Funds for the Central Universities , SCUT (no. 2014ZZ0035), the Innovation and Technology Fund (Project No. GHP/022/17GD) from the Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region, Internal Research Grant (RG 90/2018-2019R), Interdisciplinary Research Scheme of the Dean’s Research Fund 2018–19 (FLASS/DRF/IDS-3), Funding Support to General Research Fund Proposal (RG 39/2019-2020R), Departmental Collaborative Research Fund 2019 (MIT/DCRF-R2/18-19) of The Education University of Hong Kong.

Keywords

Event relation
Event search
Temporal event map
Web mining

Access to Document

10.1016/j.asoc.2019.105750

Cite this

@article{5a1958a5028f4722a67621a458f1dd7c,

title = "Temporal event searches based on event maps and relationships",

abstract = "To satisfy a user{\textquoteright}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{\textquoteright}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.",

keywords = "Event relation, Event search, Temporal event map, Web mining",

author = "Yi CAI and Haoran XIE and LAU, \{Raymond Y.K.\} and Qing LI and Tak-lam WONG and WANG, \{Fu Lee\}",

note = "This article is an extension version of a conference proceeding published in Cai et al. (2013) [1], and some notations, definitions, formulations, descriptions, figures, tables and so on are reused to make new contributions smoothly in this article. The new contributions of this article are (1) new methods of event ranking and temporal event map construction; (2) algebraic operations of the event map; (3) more examples, case studies, and metrics in experiments; and (4) more comprehensive literature review and descriptions. {\textcopyright} 2019 Elsevier B.V. All rights reserved.",

year = "2019",

month = dec,

doi = "10.1016/j.asoc.2019.105750",

language = "English",

volume = "85",

journal = "Applied Soft Computing",

issn = "1568-4946",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Temporal event searches based on event maps and relationships

AU - CAI, Yi

AU - XIE, Haoran

AU - LAU, Raymond Y.K.

AU - LI, Qing

AU - WONG, Tak-lam

AU - WANG, Fu Lee

N1 - This article is an extension version of a conference proceeding published in Cai et al. (2013) [1], and some notations, definitions, formulations, descriptions, figures, tables and so on are reused to make new contributions smoothly in this article. The new contributions of this article are (1) new methods of event ranking and temporal event map construction; (2) algebraic operations of the event map; (3) more examples, case studies, and metrics in experiments; and (4) more comprehensive literature review and descriptions. © 2019 Elsevier B.V. All rights reserved.

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

KW - Event relation

KW - Event search

KW - Temporal event map

KW - Web mining

UR - https://www.scopus.com/pages/publications/85073832758

U2 - 10.1016/j.asoc.2019.105750

DO - 10.1016/j.asoc.2019.105750

M3 - Journal Article (refereed)

C2 - 32288693

SN - 1568-4946

VL - 85

JO - Applied Soft Computing

JF - Applied Soft Computing

M1 - 105750

ER -

Temporal event searches based on event maps and relationships

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this