Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling com...Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.展开更多
Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semanti...Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information.Current datasets for event prediction,naturally,can be used for supervised learning.Event chains are either from document-level procedural action flow,or from news sequences under the same column.This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus,and adopts the standard multiple choice narrative cloze task evaluation.The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck.Based on trigger-guided structural relations in the event chains,we construct trigger evolution graph,and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy.Then there are features of two levels for each event,namely,text level semantic feature and trigger level structural feature.We design the attention mechanism to learn the features of event segments derived in term of event major subjects,and integrate relevance between event segments and the candidate event.The most possible next event is picked by the relevance.Experimental results on the real-world news corpus verify the effectiveness of the proposed model.展开更多
基金supported by the National Natural Science Foundation of China under grants U19B2044National Key Research and Development Program of China(2021YFC3300500).
文摘Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.
基金supported by the National Natural Science Foundation of China(71731002,71971190).
文摘Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information.Current datasets for event prediction,naturally,can be used for supervised learning.Event chains are either from document-level procedural action flow,or from news sequences under the same column.This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus,and adopts the standard multiple choice narrative cloze task evaluation.The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck.Based on trigger-guided structural relations in the event chains,we construct trigger evolution graph,and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy.Then there are features of two levels for each event,namely,text level semantic feature and trigger level structural feature.We design the attention mechanism to learn the features of event segments derived in term of event major subjects,and integrate relevance between event segments and the candidate event.The most possible next event is picked by the relevance.Experimental results on the real-world news corpus verify the effectiveness of the proposed model.
