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<p>With the advancement of intelligent operation and maintenance (O&amp;M) for gas polyethylene pipelines, effectively managing and utilizing pipeline data has become crucial for the development of information technology. However, the current O&amp;M process faces challenges, including weak information relevance and low decision-making efficiency. In contrast to the well-established O&amp;M knowledge system for metal pipelines, failure modes in polyethylene pipelines—such as material ageing and third-party damage—lack systematic knowledge modelling. Additionally, the multidimensional correlations among key entities (e.g., pipe segments, welded joints, and cathodic protection devices) remain unexplored. To address this, this paper proposes a knowledge graph construction method based on ALBERT-BiGRU-CRF for gas polyethylene pipeline operation and maintenance. First, structured, unstructured and semi-structured gas polyethylene pipeline operation and maintenance data were preprocessed and integrated to construct a high-quality 3D training data set containing 18 different parameter types. Next, the improved ALBERT, incorporating features from various levels, generates high-quality word vectors, which serve as input feature vectors for the bidirectional gated recurrent unit (BiGRU) model to improve the model’s semantic comprehension ability in processing pipeline O&amp;M data. Additionally, the CRF layer captures dependencies within entity sequences and optimizes label prediction results, addressing the sequence dependency issue in entity recognition. Finally, the model extracts knowledge to form a ternary structure, which is imported into the Neo4j graph database for storage and visualization. Experimental results demonstrate that the ALBERT-BiGRU-CRF model performs well in recognizing the dataset, with accuracy, recall, and F1 scores reaching 95.35, 96.51, and 95.93%, respectively. In comparison with other models, the accuracy, recall, and F1 scores show significant improvements.</p>

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Construction of knowledge graph for gas polyethylene pipelines based on ALBERT-BiGRU-CRF

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月11日

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  • Zhilong Yu,
  • Juan Zhou,
  • Qiang Wang,
  • Haiting Zhou,
  • Yun Song,
  • Chenjia Zong

摘要

With the advancement of intelligent operation and maintenance (O&M) for gas polyethylene pipelines, effectively managing and utilizing pipeline data has become crucial for the development of information technology. However, the current O&M process faces challenges, including weak information relevance and low decision-making efficiency. In contrast to the well-established O&M knowledge system for metal pipelines, failure modes in polyethylene pipelines—such as material ageing and third-party damage—lack systematic knowledge modelling. Additionally, the multidimensional correlations among key entities (e.g., pipe segments, welded joints, and cathodic protection devices) remain unexplored. To address this, this paper proposes a knowledge graph construction method based on ALBERT-BiGRU-CRF for gas polyethylene pipeline operation and maintenance. First, structured, unstructured and semi-structured gas polyethylene pipeline operation and maintenance data were preprocessed and integrated to construct a high-quality 3D training data set containing 18 different parameter types. Next, the improved ALBERT, incorporating features from various levels, generates high-quality word vectors, which serve as input feature vectors for the bidirectional gated recurrent unit (BiGRU) model to improve the model’s semantic comprehension ability in processing pipeline O&M data. Additionally, the CRF layer captures dependencies within entity sequences and optimizes label prediction results, addressing the sequence dependency issue in entity recognition. Finally, the model extracts knowledge to form a ternary structure, which is imported into the Neo4j graph database for storage and visualization. Experimental results demonstrate that the ALBERT-BiGRU-CRF model performs well in recognizing the dataset, with accuracy, recall, and F1 scores reaching 95.35, 96.51, and 95.93%, respectively. In comparison with other models, the accuracy, recall, and F1 scores show significant improvements.