Papers with ranking evaluation metrics

1 papers
Complex Logical Query Answering by Calibrating Knowledge Graph Completion Models (2024.findings-acl)

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Challenge: Existing methods to solve complex logical queries are not well-calibrated . CKGC is lightweight and effective, allowing the model to quickly converge .
Approach: They propose a method for calibrating KGC models to adapt to complex logical queries . they map the values of predictions of KGC to the range [0, 1] .
Outcome: The proposed method can significantly boost model performance in complex logical query answering task.

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