Dynamic Knowledge Graph based Multi-Event Forecasting

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.

[1]  Xiaofeng Wang,et al.  Automatic Crime Prediction Using Events Extracted from Twitter Posts , 2012, SBP.

[2]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[3]  Huzefa Rangwala,et al.  Learning Dynamic Context Graphs for Predicting Social Events , 2019, KDD.

[4]  Diego Marcheggiani,et al.  Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling , 2017, EMNLP.

[5]  Julien Leblay,et al.  Deriving Validity Time in Knowledge Graph , 2018, WWW.

[6]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[7]  Kaiming He,et al.  Exploring the Limits of Weakly Supervised Pretraining , 2018, ECCV.

[8]  Partha Talukdar,et al.  HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding , 2018, EMNLP.

[9]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[10]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[11]  Alex Fout,et al.  Protein Interface Prediction using Graph Convolutional Networks , 2017, NIPS.

[12]  Ankit Singh Rawat,et al.  Multilabel reductions: what is my loss optimising? , 2019, NeurIPS.

[13]  Naren Ramakrishnan,et al.  Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning , 2016, KDD.

[14]  Vikram Nitin,et al.  Composition-based Multi-Relational Graph Convolutional Networks , 2020, ICLR.

[15]  Grigorios Tsoumakas,et al.  An Empirical Study of Lazy Multilabel Classification Algorithms , 2008, SETN.

[16]  Yanfang Ye,et al.  Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting , 2019, AAAI.

[17]  Kenneth Ward Church,et al.  Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.

[18]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[19]  Yuxiao Dong,et al.  DeepInf : Modeling Influence Locality in Large Social Networks , 2018 .

[20]  Hans-Peter Kriegel,et al.  A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.

[21]  Naren Ramakrishnan,et al.  STAPLE: Spatio-Temporal Precursor Learning for Event Forecasting , 2018, SDM.

[22]  Jianfeng Gao,et al.  Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.

[23]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[24]  Max Welling,et al.  Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.

[25]  Mathias Niepert,et al.  Learning Sequence Encoders for Temporal Knowledge Graph Completion , 2018, EMNLP.

[26]  Fernando Benites,et al.  HARAM: A Hierarchical ARAM Neural Network for Large-Scale Text Classification , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[27]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[28]  Jieping Ye,et al.  Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting , 2016, KDD.

[29]  Guillaume Bouchard,et al.  Complex Embeddings for Simple Link Prediction , 2016, ICML.

[30]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[31]  Yuxiao Dong,et al.  DeepInf: Social Influence Prediction with Deep Learning , 2018, KDD.

[32]  Pedro A. Szekely,et al.  Recurrent Event Network for Reasoning over Temporal Knowledge Graphs , 2019, ArXiv.

[33]  Le Song,et al.  Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs , 2017, ICML.

[34]  Jieping Ye,et al.  Multi-Task Learning for Spatio-Temporal Event Forecasting , 2015, KDD.

[35]  Sashank J. Reddi,et al.  Stochastic Negative Mining for Learning with Large Output Spaces , 2018, AISTATS.

[36]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[37]  Yu Liu,et al.  T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction , 2018, IEEE Transactions on Intelligent Transportation Systems.

[38]  Bu-Sung Lee,et al.  Event Detection in Twitter , 2011, ICWSM.

[39]  Pasquale Minervini,et al.  Convolutional 2D Knowledge Graph Embeddings , 2017, AAAI.