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

Research output: Contribution to journalArticles

2 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. Copyright © 2019 Elsevier B.V. All rights reserved.
Original languageEnglish
Article number105750
JournalApplied Soft Computing
Volume85
Early online dateSep 2019
DOIs
Publication statusPublished - Dec 2019

Citation

Cai, Y., Xie, H., Lau, R. Y. K., Li, Q., Wong, T.-L., & Wang, F. L. (2019). Temporal event searches based on event maps and relationships. Applied Soft Computing, 85. Retrieved from https://doi.org/10.1016/j.asoc.2019.105750

Keywords

  • Temporal event map
  • Event search
  • Event relation
  • Web mining

Fingerprint Dive into the research topics of 'Temporal event searches based on event maps and relationships'. Together they form a unique fingerprint.