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Dynamic relationship and event discovery

Published: 09 February 2011 Publication History

Abstract

This paper studies the problem of dynamic relationship and event discovery. A large body of previous work on relation extraction focuses on discovering predefined and static relationships between entities. In contrast, we aim to identify temporally defined (e.g., co-bursting) relationships that are not predefined by an existing schema, and we identify the underlying time constrained events that lead to these relationships. The key challenges in identifying such events include discovering and verifying dynamic connections among entities, and consolidating binary dynamic connections into events consisting of a set of entities that are connected at a given time period. We formalize this problem and introduce an efficient end-to-end pipeline as a solution. In particular, we introduce two formal notions, global temporal constraint cluster and local temporal constraint cluster, for detecting dynamic events. We further design efficient algorithms for discovering such events from a large graph of dynamic relationships. Finally, detailed experiments on real data show the
effectiveness of our proposed solution.

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Cited By

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  • (2021)Event Detection in Wikipedia Edit History Improved by Documents Web Based Automatic AssessmentBig Data and Cognitive Computing10.3390/bdcc50300345:3(34)Online publication date: 4-Aug-2021
  • (2020)Span-core Decomposition for Temporal NetworksACM Transactions on Knowledge Discovery from Data10.1145/341822615:1(1-44)Online publication date: 7-Dec-2020
  • (2020)A History and Theory of Textual Event Detection and RecognitionIEEE Access10.1109/ACCESS.2020.30349078(201371-201392)Online publication date: 2020
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    cover image ACM Conferences
    WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining
    February 2011
    870 pages
    ISBN:9781450304931
    DOI:10.1145/1935826
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 09 February 2011

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    Author Tags

    1. dynamic events
    2. entity relationships
    3. event discovery

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    WSDM '11 Paper Acceptance Rate 83 of 372 submissions, 22%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

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    Cited By

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    • (2021)Event Detection in Wikipedia Edit History Improved by Documents Web Based Automatic AssessmentBig Data and Cognitive Computing10.3390/bdcc50300345:3(34)Online publication date: 4-Aug-2021
    • (2020)Span-core Decomposition for Temporal NetworksACM Transactions on Knowledge Discovery from Data10.1145/341822615:1(1-44)Online publication date: 7-Dec-2020
    • (2020)A History and Theory of Textual Event Detection and RecognitionIEEE Access10.1109/ACCESS.2020.30349078(201371-201392)Online publication date: 2020
    • (2019)The importance of unexpectedness: Discovering buzzing stories in anomalous temporal graphsWeb Intelligence10.3233/WEB-19041217:3(177-198)Online publication date: 16-Aug-2019
    • (2019)Time-series as Background Data for Relating Medical Diagnoses TermsProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3342145(243-252)Online publication date: 4-Sep-2019
    • (2019)Research on Topic Mining Algorithm Based on Deep Learning ExtensionJournal of Physics: Conference Series10.1088/1742-6596/1345/4/0420341345(042034)Online publication date: 28-Nov-2019
    • (2019)Event modeling and mining: a long journey toward explainable eventsThe VLDB Journal10.1007/s00778-019-00545-0Online publication date: 1-Jul-2019
    • (2018)Mining (maximal) Span-cores from Temporal NetworksProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271767(107-116)Online publication date: 17-Oct-2018
    • (2018)Exploring Entity-centric Networks in Entangled News StreamsCompanion Proceedings of the The Web Conference 201810.1145/3184558.3188726(555-563)Online publication date: 23-Apr-2018
    • (2018)A Multi-view Clustering Model for Event Detection in TwitterComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-77116-8_27(366-378)Online publication date: 10-Oct-2018
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