Knowledge Base Construction from Pre-trained Language Models by Prompt learning

Pre-trained language models (LMs) have advanced the state-of-the-art for many semantic tasks and have also been proven effective for extracting knowledge from the models itself. Although several works have explored the capability of the LMs for constructing knowledge bases, including prompt learning, this potential has not yet been fully explored. In this work, we propose a method of extracting factual knowledge from LMs for given subject-relation pairs and explore the most effective strategy to generate blank object entities for each relation of triples. We design prompt templates for each relation using personal knowledge and the descriptive information available on the web such as WikiData. The probing approach of our proposed LMs is tested on the dataset provided by the International Semantic Web Conference (ISWC 2022) LM-KBC Challenge. To cope with the problem of varying performance for each relation, we designed a parameter selection strategy for each relation. Using the test dataset, we obtain an F1-score of 0.4935%, which is higher than the baseline of 31.08%.