Papers by Xuelu Chen

2 papers
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning (2021.naacl-main)

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Challenge: Existing methods to generalize knowledge bases model triple-level uncertainty . Existing models only model triple level uncertainty, and reasoning results lack global consistency.
Approach: They propose a method to embed knowledge graphs with calibrated probabilistic semantics . they model each entity as a box and relations between two entities as affine transforms based on affinity transforms.
Outcome: Experiments show that the proposed method outperforms baseline methods on confidence prediction and fact ranking.
Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer (2020.findings-emnlp)

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Challenge: Existing methods for learning missing facts in knowledge graphs are limited by insufficiency of alignment information and inconsistency of described facts.
Approach: They propose a framework for embedding learning and ensemble knowledge transfer across KGs.
Outcome: The proposed framework improves state-of-the-art methods on language-specific KGs.

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