NarGNN: Narrative Graph Neural Networks for New Script Event Prediction Problem

Yang, Shuang; Wang, Fali; Zha, Daren; Xue, Cong; Tang, Zhihao Gavin

doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00086

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…SGNN [17] models event interactions on large-scale dense directional graphs and learns better event representations for event prediction. NarGNN [32] integrates factual knowledge and experiential knowledge to predict events and considers both historical and relative information. CircEvent [28] retrieves circumstances at both local and global levels using multi-head attention, considering not only inter-event relationships but also how the events happened.…”

Section: Methodsmentioning

confidence: 99%

Script event prediction by meta-memory low resource network

Chu

Hou

Weng

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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Script event prediction involves building a model using recent event sequence records to capture the correlation between events and predict future ones. However, existing methods require a large amount of training data to perform well. In many real-world scenarios, labeled data is scarce and data distribution is uneven, making it challenging to achieve satisfactory performance. Additionally, the learning process of many models demands significant computational resources, which can render them unsuitable for low-resource settings. In order to fill the task blank of event prediction in low resource scenarios, a general framework called Meta-memory low resource network learning model(MLN) is proposed. Firstly, MLN captures the memory of events that occurred in the history through trend learner, learns the context-implied representation between events, and can predict future events based on the implied representation. Secondly, a Meta-memory predictor is proposed, which can further aggregate similar contextual implicit representations to form meta-memory representations, To realize the long-term memory learning process under the condition of few-shot. Finally, a gated network combination method combines the trend learner with the Meta-memory predictor to improve the effect of event prediction. We conducted experiments on four real data sets, and the results showed the effectiveness of MLN.

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Section: Methodsmentioning

confidence: 99%

Script event prediction by meta-memory low resource network

Chu

Hou

Weng

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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Integrating Relational Knowledge With Text Sequences for Script Event Prediction

Wang

Zeng

2024

IEEE Trans. Neural Netw. Learning Syst.

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Sudden Event Prediction Based on Event Knowledge Graph

Liu

2022

Applied Sciences

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Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. Early work on event prediction typically modeled the event context to predict what would happen next. Moreover, the predicted outcome was often singular. These studies had difficulty coping with both the problems of predicting sudden events in unknown contexts and predicting outcomes consisting of multiple events. To address these two problems better, we present the heterogeneous graph event prediction model (HGEP), which is based on an event knowledge graph. To cope with the situation of missing contexts, we propose a representation learning method based on the heterogeneous graph transformer. We generate event scenario representations from arguments of the initial event and other related concurrent events for the event prediction. We improve the prediction ability of the HGEP model through prior knowledge provided by scenario models in the event knowledge graph. To obtain multiple prediction outcomes, we design a scoring function to calculate the score of the occurrence probability of each event class. The event classes with scores higher than a priori values are adopted as prediction outcomes. In this paper, we create an event knowledge graph in the domain of transportation for an event prediction experiment. The experimental results show that the HGEP model can effectively make predictions with event scenario representations and has a more accurate matching rate and higher precision than the baseline model.

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NarGNN: Narrative Graph Neural Networks for New Script Event Prediction Problem

Cited by 3 publications

References 12 publications

Script event prediction by meta-memory low resource network

Script event prediction by meta-memory low resource network

Integrating Relational Knowledge With Text Sequences for Script Event Prediction

Sudden Event Prediction Based on Event Knowledge Graph

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