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arXiv · 2208.12539

Task-specific Pre-training and Prompt Decomposition for Knowledge Graph Population with Language Models

Tianyi Li·Wenyu Huang·Nikos Papasarantopoulos·Pavlos Vougiouklis·Jeff Z. Pan
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Abstract

We present a system for knowledge graph population with Language Models, evaluated on the Knowledge Base Construction from Pre-trained Language Models (LM-KBC) challenge at ISWC 2022. Our system involves task-specific pre-training to improve LM representation of the masked object tokens, prompt decomposition for progressive generation of candidate objects, among other methods for higher-quality retrieval. Our system is the winner of track 1 of the LM-KBC challenge, based on BERT LM; it achieves 55.0% F-1 score on the hidden test set of the challenge.

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Tianyi Li, Wenyu Huang, Nikos Papasarantopoulos, Pavlos Vougiouklis, Jeff Z. Pan. 2022-08-26. Task-specific Pre-training and Prompt Decomposition for Knowledge Graph Population with Language Models. https://arxiv.org/abs/2208.12539

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