Event Prediction in Event-Centric Knowledge Graphs Using BERT

被引：0
|
作者：
Ugai, Takanori [1 ,2 ]
Egami, Shusaku [1 ]
Fukuda, Ken [1 ]
机构：
[1] Natl Inst Adv Ind Sci & Technol, Tokyo, Japan
[2] Fujitsu, Kawasaki, Kanagawa, Japan
来源：
18TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, ICSC 2024 | 2024年
关键词：
D O I：
10.1109/ICSC59802.2024.00055
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The dataset provided by the Knowledge Graph Reasoning Challenge is an event-centric knowledge graph representation of avatar behavior in a virtual space. Therefore, it contains information about the entire virtual space, including parts of the virtual space that are hidden in the video. However, some information cannot be obtained by any means; for example, when sensor data from the real world are collected. In this paper, we report a method for predicting hidden parts using BERT and the results of experiments using this method, using the Challenge dataset as complete data for training.
引用
收藏
页码：306 / 310
页数：5
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