Mapping a Knowledge Graph of Flooding in Academic Literature Through Full‐Text Entity Extraction
Year: 2025
Type: article
Abstract: ABSTRACT Academic literature with long‐tail characteristics contains rich knowledge resources but is not easily discovered through limited manual knowledge extraction capabilities. This study proposed a refined extraction strategy, transitioning from “full text to sentence,” in the field of flood disaster risk reduction. Sentences describing research methods were identified from the full texts of 5180 articles published between 1990 and 2020. Research method entities—including algorithms, software, and data—were extracted using optimal deep learning models. A flooding knowledge graph was constructed and applied to several flood control scenarios. The results showed that the BiLSTM‐CRF model outperformed more complex alternatives. In all, 2144 research methods, 291 software tools, and six types of remote sensing data sources were obtained based on extracted usage method sentences. The flooding knowledge graph contained 42,420 nodes and 78,242 edges. The proposed refined knowledge entity extraction method provides a reference for related knowledge graph mapping based on big data.
Source: Transactions in GIS
DOI: 10.1111/tgis.70045
Authors: Min Zhang, Juanle Wang, Xiaodong Zhang
Institutions: Chinese Academy of Sciences, Peking University, Institute of Geographic Sciences and Natural Resources Research, State Key Laboratory of Resources and Environmental Information System, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, University of Chinese Academy of Sciences
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,800
Awards: G1546816848, G3802419807, G8436140462
OpenAlex ID: W4409295015