Construction of Meteorological Simulation Knowledge Graph Based on Deep Learning Method
With the maturity of meteorological simulation technology, the research literature in this field is undergoing a rapid increase. The published literature can provide useful guidance for current research to get scientific results; however, it tends to be rather time consuming to obtain exact knowledg...
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| Veröffentlicht in: | Sustainability 2021-02, Vol.13 (3), p.1311 |
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| description | With the maturity of meteorological simulation technology, the research literature in this field is undergoing a rapid increase. The published literature can provide useful guidance for current research to get scientific results; however, it tends to be rather time consuming to obtain exact knowledge from massive literature, and it is necessary to transform the literature into structured knowledge to meet the efficient management, sharing, and reuse of meteorological simulation knowledge. In this paper, methods of meteorological simulation knowledge extraction and knowledge graph construction are proposed. A deep learning model based on bilateral long short-term memory-conditional random field (BiLSTM-CRF) is used to extract the meteorological simulation knowledge from the massive literature. Then, the Neo4j graph database is used to construct the meteorological simulation knowledge graph. Based on the meteorological simulation knowledge graph, it can realize the structured storage and integration of meteorological simulation knowledge, which can bridge the gap in the transformation of massive literature to sharable and reusable knowledge. Furthermore, the meteorological simulation knowledge graph can be used as an expert resource and contribute to sustainable guidance and optimization for meteorological simulation research. |
| doi_str_mv | 10.3390/su13031311 |
| format | Article |
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The published literature can provide useful guidance for current research to get scientific results; however, it tends to be rather time consuming to obtain exact knowledge from massive literature, and it is necessary to transform the literature into structured knowledge to meet the efficient management, sharing, and reuse of meteorological simulation knowledge. In this paper, methods of meteorological simulation knowledge extraction and knowledge graph construction are proposed. A deep learning model based on bilateral long short-term memory-conditional random field (BiLSTM-CRF) is used to extract the meteorological simulation knowledge from the massive literature. Then, the Neo4j graph database is used to construct the meteorological simulation knowledge graph. Based on the meteorological simulation knowledge graph, it can realize the structured storage and integration of meteorological simulation knowledge, which can bridge the gap in the transformation of massive literature to sharable and reusable knowledge. Furthermore, the meteorological simulation knowledge graph can be used as an expert resource and contribute to sustainable guidance and optimization for meteorological simulation research.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su13031311</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Additive manufacturing ; Air pollution ; Artificial intelligence ; Big Data ; Conditional random fields ; Construction ; Deep learning ; Geographic information science ; Geography ; Knowledge bases (artificial intelligence) ; Knowledge management ; Knowledge representation ; Learning ; Long short-term memory ; Machine learning ; Medical research ; Methods ; Ontology ; Optimization ; Semantics ; Simulation ; Software engineering ; Sustainability</subject><ispartof>Sustainability, 2021-02, Vol.13 (3), p.1311</ispartof><rights>2021. 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| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Additive manufacturing Air pollution Artificial intelligence Big Data Conditional random fields Construction Deep learning Geographic information science Geography Knowledge bases (artificial intelligence) Knowledge management Knowledge representation Learning Long short-term memory Machine learning Medical research Methods Ontology Optimization Semantics Simulation Software engineering Sustainability |
| title | Construction of Meteorological Simulation Knowledge Graph Based on Deep Learning Method |
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