图 书 馆
利用预训练语言模型进行文本增强时态知识图中的时间间隔预测Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs |
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| 课程网址: | https://videolectures.net/eswc2024_sezen_islakoglu_knowledge_grap... |
| 主讲教师: | Duygu Sezen Islakoglu |
| 开课单位: | 2024年上海世博会 |
| 开课时间: | 2024-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Most knowledge graph completion (KGC) methods rely solely on structural information, even though a large number of publicly available KGs contain additional temporal (validity time intervals) and textual data (entity descriptions). While recent temporal KGC methods utilize time information to enhance link prediction, they do not leverage textual descriptions or support inductive inference (prediction for entities that have not been seen during training). In this work, we propose a novel framework called TEMT that exploits the power of pre-trained language models (PLMs) for temporal KGC. TEMT predicts time intervals of facts by fusing their textual and temporal information. It also supports inductive inference by utilizing PLMs. In order to showcase the power of TEMT, we carry out several experiments including time interval prediction, both in transductive and inductive settings, and triple classi cation. The experimental results demonstrate that TEMT is comp. |
| 关 键 词: | 预训练语言模型; 文本增强; 时间间隔预测 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-08-05：liyq |
| 最后编审: | 2024-09-24：liyy |
| 阅读次数: | 18 |