TY - GEN
T1 - Dynamic Knowledge Graph based Multi-Event Forecasting
AU - Deng, Songgaojun
AU - Rangwala, Huzefa
AU - Ning, Yue
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/8/23
Y1 - 2020/8/23
N2 - 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.
AB - 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.
KW - knowledge graphs
KW - multi-event forecasting
KW - word graphs
UR - http://www.scopus.com/inward/record.url?scp=85090422145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090422145&partnerID=8YFLogxK
U2 - 10.1145/3394486.3403209
DO - 10.1145/3394486.3403209
M3 - Conference contribution
AN - SCOPUS:85090422145
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1585
EP - 1595
BT - KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
T2 - 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Y2 - 23 August 2020 through 27 August 2020
ER -