Abstract
At present, most of the methods for knowledge graph completion (KGC) task highly rely on external knowledge base or graph representation learning. However, how to complete this task without using any external prior knowledge is still a huge challenge and difficulty. To this end, we propose a novel framework which converts the plausibility evaluation of knowledge triple task to the question and answer (QA) task with the thought of KG-BERT and prompt learning. We also test the effect of different question types on the results. Secondly, by fine-tuning two pre-trained language models BERT-wwm-ext and ERNIE-Gram on these generated sequences, so that they can complete the QA task. We won the 5th place at CCKS 2022 track 1 rematch stage, which proved the effectiveness of our method.
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Jia, S., Cao, J. (2022). The Method for Plausibility Evaluation of Knowledge Triple Based on QA. In: Zhang, N., Wang, M., Wu, T., Hu, W., Deng, S. (eds) CCKS 2022 - Evaluation Track. CCKS 2022. Communications in Computer and Information Science, vol 1711. Springer, Singapore. https://doi.org/10.1007/978-981-19-8300-9_25
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DOI: https://doi.org/10.1007/978-981-19-8300-9_25
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