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

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

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arxiv 2208.12539 v2 pith:KTTZWWC7 submitted 2022-08-26 cs.CL

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 This is my paper

classification cs.CL

keywords challengeknowledgelanguagemodelssystemdecompositiongraphlm-kbc

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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.

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

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