Dynamic Knowledge Graph based Multi-Event Forecasting

Songgaojun Deng, Huzefa Rangwala, Yue Ning

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

76 Scopus citations

Abstract

Modeling concurrent events of multiple types and their involved actors from open-source social sensors is an important task for many domains such as health care, disaster relief, and financial analysis. Forecasting events in the future can help human analysts better understand global social dynamics and make quick and accurate decisions. Anticipating participants or actors who may be involved in these activities can also help stakeholders to better respond to unexpected events. However, achieving these goals is challenging due to several factors: (i) it is hard to filter relevant information from large-scale input, (ii) the input data is usually high dimensional, unstructured, and Non-IID (Non-independent and identically distributed) and (iii) associated text features are dynamic and vary over time. Recently, graph neural networks have demonstrated strengths in learning complex and relational data. In this paper, we study a temporal graph learning method with heterogeneous data fusion for predicting concurrent events of multiple types and inferring multiple candidate actors simultaneously. In order to capture temporal information from historical data, we propose Glean, a graph learning framework based on event knowledge graphs to incorporate both relational and word contexts. We present a context-aware embedding fusion module to enrich hidden features for event actors. We conducted extensive experiments on multiple real-world datasets and show that the proposed method is competitive against various state-of-the-art methods for social event prediction and also provides much-need interpretation capabilities.

Original languageEnglish
Title of host publicationKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages1585-1595
Number of pages11
ISBN (Electronic)9781450379984
DOIs
StatePublished - 23 Aug 2020
Event26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States
Duration: 23 Aug 202027 Aug 2020

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Country/TerritoryUnited States
CityVirtual, Online
Period23/08/2027/08/20

Keywords

  • knowledge graphs
  • multi-event forecasting
  • word graphs

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