An Intelligent Retrieval Method of Building Fire Safety Knowledge Based on Knowledge Graph
Authors: Fengyang Sun, Beibei Sun
Abstract: Aiming at the difficulty of searching standards and specifications due to the huge and fragmented data in the field of fire safety in China, an intelligent retrieval method of building fire safety knowledge based on knowledge graph is proposed. First, ontology is used to construct conceptual schemas from top down, and knowledge is extracted using rule templates and stored in the Neo4j graph database to complete the construction of knowledge graph. Then, on the basis of the knowledge graph, BERT-BiLSTM-CRF model and BERT classifier are used to process complex questions with multiple constraints, so as to extract key entities in the question and identify query intention. Finally, according to the key entities and query intention, an algorithm is used to generate a Cypher query statement, which is used to obtain the answer in Neo4j. The intelligent retrieval method based on knowledge graph standardizes the building fire safety knowledge, solves the problem of scattered distribution and greatly improves the efficiency of knowledge retrieval.
Keywords: knowledge graph, intelligent retrieval, deep learning, building fire safety
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