Task-specific Pre-training and Prompt Decomposition for Knowledge Graph Population with Language Models
This addresses knowledge base construction for AI applications, but it is incremental as it builds on existing BERT-based methods with task-specific optimizations.
The paper tackled knowledge graph population using language models, winning track 1 of the LM-KBC challenge with a 55.0% F-1 score on the hidden test set.
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.