Li Songjiang, Cao Jinming, Yang Jiao, He Yunjiangcan, Wang Peng
College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
Chongqing Research Institute, Changchun University of Science and Technolo-gy, Chongqing, China.
PLoS One. 2025 Aug 5;20(8):e0329120. doi: 10.1371/journal.pone.0329120. eCollection 2025.
Traditional entity-relationship joint extraction models are typically designed to address generic domain data, which limits their effectiveness when applied to domain-specific applications such as manufacturing. This study presents the DeBERTa-based Potential Relationship Multi-Headed Self-Attention Joint Extraction Model (DPRM), which has been specifically designed to enhance the accuracy and efficiency of entity-relationship extraction in manufacturing knowledge graphs. The model is comprised of three core components: a semantic representation module, a relationship extraction and entity recognition module, and a global entity pairing module. In the semantic representation module, a DeBERTa encoder is employed to train the input sentences, thereby generating word embeddings. The capture of word dependencies is achieved through the utilization of Bi-GRU and Multi-Headed Self-Attention mechanisms, which serve to enhance the overall representation of the sentence. The relationship extraction and entity recognition module is responsible for identifying potential relationships within the sentences and integrating a relational gated mechanism to minimize the interference of irrelevant information during the entity recognition process. The global entity pairing module simplifies the model's architecture by extracting potential relationships and constructing a matrix of global pairing entity pairs based on fault-specific data. The efficacy of the proposed model is validated through experiments conducted on fault datasets. The results demonstrate that the DPRM achieves superior performance, with an F1 score that surpasses that of existing models, thereby highlighting its effectiveness in the fault domain.
传统的实体关系联合提取模型通常设计用于处理通用领域数据，这限制了它们在应用于特定领域应用（如制造业）时的有效性。本研究提出了基于DeBERTa的潜在关系多头自注意力联合提取模型（DPRM），该模型专门设计用于提高制造知识图谱中实体关系提取的准确性和效率。该模型由三个核心组件组成：语义表示模块、关系提取与实体识别模块以及全局实体配对模块。在语义表示模块中，使用DeBERTa编码器对输入句子进行训练，从而生成词嵌入。通过利用双向门控循环单元（Bi-GRU）和多头自注意力机制来捕获词依赖关系，这有助于增强句子的整体表示。关系提取与实体识别模块负责识别句子中的潜在关系，并集成关系门控机制以最小化实体识别过程中无关信息的干扰。全局实体配对模块通过提取潜在关系并基于特定故障数据构建全局配对实体对矩阵来简化模型架构。通过在故障数据集上进行的实验验证了所提出模型的有效性。结果表明，DPRM取得了卓越的性能，其F1分数超过了现有模型，从而突出了其在故障领域的有效性。
