Knowledge graph for safety management standards of water conservancy construction engineering

Abstract

With the increasing demand for water conservancy engineering (WCE), the number of safety accidents during construction has continued to rise, requiring an urgent improvement in construction safety. The existing safety management regulations for water conservancy construction engineering (WCCE) comprise a considerable amount of text, with cross-references between different standards severely reducing their use efficiency. To address this issue, this paper proposes an ALBERT-BiLSTM-CRF model based on textual data from WCCE safety management standards. ALBERT, a lightweight pretrained language model, is integrated with the BiLSTM-CRF to construct an intelligent text entity recognition method. Association rules are used to extract entity relationships, and a knowledge graph representing the WCCE safety management standards is established. The results show that the ALBERT-BiLSTM-CRF algorithm improves the precision, with a recognition accuracy exceeding 85 %. Case studies validate that the constructed knowledge graph can quickly query safety standard knowledge, aiding in the generation of safety measures.