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Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

by Runyu Fan 1,2, Lizhe Wang 1,2,*, Jining Yan 1,2, Weijing Song 1,2, Yingqian Zhu 1,2 and Xiaodao Chen 1,2
1
School of Computer Science, China University of Geosciences, Wuhan 430074, China
2
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 15; https://doi.org/10.3390/ijgi9010015
Received: 14 November 2019 / Revised: 17 December 2019 / Accepted: 23 December 2019 / Published: 27 December 2019
Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations. View Full-Text
Keywords: named entity recognition; knowledge graph; deep learning; geological hazards named entity recognition; knowledge graph; deep learning; geological hazards
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Fan, R.; Wang, L.; Yan, J.; Song, W.; Zhu, Y.; Chen, X. Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards. ISPRS Int. J. Geo-Inf. 2020, 9, 15.

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