基于复合语义特征的事件图谱构建技术研究进展

doi:10.11896/jsjkx.230400046

Abstract

Abstract: The world is made up of countless interconnected events and the social activities of human beings are often driven by these various events.Research on the process of evolution and influence of events can not only helps us understand the evolution laws of human behaviors and social activities,but also provide a strategy for reasoning and thinking about artificial intelligence techniques,which has been paid a lot attention and becomes one of the new hottest research field.Unlike traditional knowledge graph,event graphs can abstract various events from the real world as nodes and recognize the logical relationships between events,such as state transforms or action sequences between different events,to form an innovation knowledge network with some composite semantic features.From the higher-level semantic viewpoints,the evolution of the complex events reflects the process of social activity with a certain of hidden logical relationships behind of them.In this paper,some critical challenges in the process of event graph construction have been analyzed,i.e.,how to extract the event in open domain,to establish a common event standards,to extract the relationship between events,to fusion and optimize the event graph,and to build a strategy for event graph representation learning.In addition,this paper also overviews and summarizes some core technologies,public evaluation data sets,related measure indicators,and then some research directions in future have been illustrated.

Key words: Knowledge graph, Event extraction, Relation extraction, Event graph, Representation learning

CLC Number:

TP391

ZHAI Lizhi, LI Ruixiang, YANG Jiabei, RAO Yuan, ZHANG Qitan, ZHOU Yun. Overview About Composite Semantic-based Event Graph Construction[J].Computer Science, 2023, 50(9): 242-259.

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