情报科学 ›› 2021, Vol. 39 ›› Issue (7): 75-82.

• 业务研究 • 上一篇    下一篇

基于BERT的领域本体分类关系自动识别研究

  

  • 出版日期:2021-07-16 发布日期:2021-07-16

  • Online:2021-07-16 Published:2021-07-16

摘要: 【目的/意义】实现对领域本体分类关系的自动学习识别,解决领域本体知识框架结构体系的自动化构建问
题。【方法/过程】通过对领域本体分类关系自动识别的国内外研究现状及存在问题进行分析总结,以当前开源的先
进的深度学习文本预训练模型BERT为基础,研究构建了基于BERT的领域本体分类关系自动识别模型,并以资源
环境学科领域为例进行了实验研究和评估分析。【结果/结论】基于BERT构建的分类模型能够实现对领域本体分类
关系的自动识别,识别方法和流程具有极大地通用性和可移植性,识别精度比传统方法有了较大提升。【创新/局
限】微调与泛化了BERT,提高了领域本体分类关系识别模型的通用性和精度。但由于受分类标注语料的质量限
制,模型精度尚未达到峰值,有待进一步优化提升。

Abstract: 【Purpose/significance】Realize the automatic learning and identification of domain ontology classification relations, and
solve the problem of the automatic construction of domain ontology knowledge framework structure.【Method/process】Through the anal? ysis and summary of the research status and existing problems of automatic recognition of domain ontology classification relationship at home and abroad, the paper studies and builds the domain ontology classification relationship automatic recognition model based on the open source and advanced deep learning pre-trained models BERT. Then, the paper takes the field of resources and environment as an example to conduct experimental research and evaluation analysis.【Result/conclusion】The classification model based on BERT can realize the automatic identification of domain ontology classification relationships. The identification methods and processes are extremely versatile and portable, and the identification accuracy is greatly improved compared to traditional methods.【Innovation/limi? tation】The paper fines tuned and generalized BERT,improves the generality and accuracy of domain ontology classification relation recognition model. However, Due to the quality constraints of the classified annotation corpus, the model accuracy has not yet reached its peak and needs to be further optimized and improved.