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About this paper
Wen Zhang, Yajing Xu, Peng Ye, Zhiwei Huang, Zezhong Xu, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen
Introduces Triple Set Prediction (TSP) as a new Knowledge Graph Completion task that automatically predicts entire sets of missing triples without requiring prior information. The proposed GPHT method efficiently addresses TSP by progressively reducing the candidate triple space and demonstrates improved prediction speed and competitive performance on benchmark datasets.
Key insights
- The core challenge of Triple Set Prediction (TSP) lies in efficiently navigating a vast search space of potential triples, requiring effective candidate reduction.
- A progressive, three-stage approach combining graph structural partitioning, meta-learning for head-tail entity pair prediction, and knowledge graph embedding for relation prediction can effectively tackle the TSP task.
- Evaluating TSP under an open-world assumption necessitates new metrics and a framework like Relation Similarity-based Partial Open-World Assumption (RS-POWA) to handle unknown true triples.
Summary by alphaXiv.
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