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

• URL: http://arxiv.org/abs/2208.12539v1
• Date: Fri, 26 Aug 2022 09:56:27 GMT
• Title: Task-specific Pre-training and Prompt Decomposition for Knowledge Graph Population with Language Models
• Authors: Tianyi Li, Wenyu Huang, Nikos Papasarantopoulos, Pavlos Vougiouklis, Jeff Z. Pan
• Abstract summary: 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 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.
• Score: 15.164149482966296
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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