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InfoResearchPeer-reviewed
An Ensemble Link Prediction Model for Sparse Knowledge Graphs With Drifting Entity
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Summary
Researchers propose TEDD, an ensemble model for link prediction in dynamic knowledge graphs with drifting entities, aimed at sparse knowledge graph completion. The model combines a double scoring mechanism that uses both text semantics and triple structure, and a dual-BERT design with an auxiliary candidate entity set to avoid redundant embedding calculations. The authors report that TEDD outperforms state-of-the-art methods on benchmark datasets.