青岛大学学报（自然科学版）2026，Vol.39Issue(3)：24-31,8.DOI:10.3969/j.issn.1006-1037.2026.03.05
基于本体与知识增强的军事命名实体识别模型
Military Named Entity Recognition Model Based on Ontology and Knowledge Enhancement
摘要
Abstract
To address persistent issues of knowledge sparsity and semantic ambiguity in military named entity recognition(NER),a domain-specialized ontology and knowledge enhanced model termed OKR-NER(Ontology-Knowledge-enhanced Representation for NER)was proposed.The model extends the BERT-BiLSTM-Attention-CRF architecture by incorporating structured domain knowledge,and achieves deep semantic-knowledge in-tegration through a dual-stream encoding strategy,an ontology-guided attention mecha-nism(OGA),and a knowledge-aware conditional random field(CRF)decoding module.The proposed model achieves F1-score gains of 2.41%～7.30%over strong baselines,in-cluding BERT-CRF,SpanNER,and GlobalPointer,on a self-constructed military corpus.All three proposed mechanisms contribute to performance improvements,with the ontolo-gy-guided attention(OGA)mechanism playing the most prominent role.The model effec-tively mitigates knowledge sparsity and semantic ambiguity in military named entity rec-ognition and improves military entity recognition performance.关键词
命名实体识别/军事领域/深度学习/本体增强/双向编码器表示模型Key words
named entity recognition/military domain/deep learning/ontology enhance-ment/bidirectional encoder representations from transformers分类
信息技术与安全科学引用本文复制引用
王华伟,云红艳,张栋..基于本体与知识增强的军事命名实体识别模型[J].青岛大学学报（自然科学版）,2026,39(3):24-31,8.基金项目
山东省高校教改项目重点项目(批准号:Z2023238)资助. （批准号:Z2023238）
