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Integrating entity description for enhanced temporal knowledge graph reasoning
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PRE
AI
DOI：10.1007/s11280-026-01433-8.png)
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Abstract
En 中文
Temporal knowledge graphs are usually incomplete, and representation learning techniques can be used for temporal knowledge graph reasoning. Important semantic information can be provided by entity descriptions for the learning of temporal knowledge graph representations. Most conventional approaches ignore the valuable semantic information derived from entity descriptions and only learn entity representations from structured quadruples. In this study, we offer the ED-mod temporal knowledge graph inference model, which is enhanced with entity descriptions. It can utilize the information from the graph structure and entity descriptions to perform the task of temporal knowledge graph reasoning. More specifically, by taking into account entity names and quadruple relation kinds, we first extract raw entity descriptions. Then, accurate summary data is extracted from the original entity descriptions using a unique Transformer network. Finally, we employ BiLSTM to encode entity descriptions in order to extract enough semantic information. We evaluate ED-mod on temporal knowledge graph reasoning and entity classification tasks. According to the experimental findings, our model outperforms cutting-edge baselines on the majority of criteria. The efficiency and significance of Transformer models and quadruple relation types in entity description extraction are further demonstrated by analysis of ablation experiments.
Keywords:
Entity descriptions
Knowledge graph embedding
Neural network
Semantic information
Temporal knowledge graph
Journal
W
IF:
3.4
Papers:
52
Citations:
2.3K
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Cited Papers
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