Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards
| العنوان: | Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards |
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| المؤلفون: | Chen Xiaodao, Yan Jining, Lizhe Wang, Yingqian Zhu, Song Weijing, Runyu Fan |
| المصدر: | ISPRS International Journal of Geo-Information, Vol 9, Iss 1, p 15 (2019) ISPRS International Journal of Geo-Information Volume 9 Issue 1 |
| بيانات النشر: | MDPI AG, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Conditional random field, Computer science, Geography, Planning and Development, named entity recognition, lcsh:G1-922, Context (language use), 02 engineering and technology, computer.software_genre, Semantics, Named-entity recognition, 0202 electrical engineering, electronic engineering, information engineering, Earth and Planetary Sciences (miscellaneous), Computers in Earth Sciences, Layer (object-oriented design), geological hazards, ComputingMethodologies_COMPUTERGRAPHICS, 020203 distributed computing, business.industry, Deep learning, InformationSystems_DATABASEMANAGEMENT, deep learning, Construct (python library), knowledge graph, Core (graph theory), 020201 artificial intelligence & image processing, Data mining, Artificial intelligence, business, computer, lcsh:Geography (General) |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2220-9964 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::814ea6c825b7c78a67093cb768c4477aTest https://www.mdpi.com/2220-9964/9/1/15Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....814ea6c825b7c78a67093cb768c4477a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22209964 |
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