Information extraction and knowledge graph construction from geoscience literature

Wang, Chengbin; Ma, Xiaogang; Chen, Jianguo; Chen, Jingwen

doi:10.1016/j.cageo.2017.12.007

Cited by 176 publications

(64 citation statements)

References 19 publications

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“…In order to better extract knowledge from the unstructured geoscience literature, Wang et al [7] designed a workflow for knowledge extraction and construction of knowledge graph for geoscience literature. First, a corpus containing domain corpus and general domain corpus were constructed for word segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Shi et al [11] also used TF-IDF to extract keywords to construct a knowledge graph. However, unlike Wang et al [7], Shi et al [11] trained a CNN-based classifier that automatically divides the geoscience literature into four categories (geophysics, geology, remote sensing, and geochemistry) and then constructs the corresponding knowledge graph.…”

Section: Related Workmentioning

confidence: 99%

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

Fan

Wang

Yan

et al. 2019

IJGI

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Fan

Wang

Yan

et al. 2019

IJGI

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“…The recognition effect of named entities is greatly affected by the results of automatic word segmentation. Research on Chinese naming recognition based on neural networks has received much attention (C. Wang, Ma, & Chen, ; Zhang, Wu, Liu, et al, ). A Chinese named entity recognition based on deep learning (G. Y. Wang, ; Zhu, Yang, & Wang, ) has begun to emerge.…”

Section: Related Researchmentioning

confidence: 99%

Bi‐LSTM+CRF‐based named entity recognition in scientific papers in the field of ecological restoration technology

Yuan

2019

Proceedings of the Association for Information Science and Tech

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We carried out an experiment to extract ecological‐restoration‐technology‐related entities (i.e., entities related to the fragile ecological remediation technology of desertification, rocky desertification, water and soil erosion control areas, place entities, and time entities related to the technology performed,) from full‐text documents published in the CNKI from 1978 to 2017. Based on the extraction of the time entities, place entities, and fragile ecological remediation technology entities, we analyzed the technologies with the greatest potential from among China's fragile ecological restoration technologies using the category clustering of fragile ecological restoration technologies with LDA and the place distribution and evolution of fragile ecological treatment technologies. This research shows that the proposed Bi‐LSTM+CRF combined with the feature‐based named entity knowledgebase can be used to perform geographical knowledge discovery from text, and it has has good prospects for applications in intelligence analysis based on text mining.

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“…Many national geological survey agencies focus on handling georeferenced quantitative data/information, including rock structures, geochemical anomalies, satellite imagery, and geophysical surveys (Qiu et al, ; Wu et al, ). As a key component of open and published data, geoscience documents/reports often contain potential and valuable information for further research (Wang et al, ). Because these geoscience reports are usually embedded with rich topics about various forms of objects, such as stratum, geological history, paleobionts, and geological structure, analyzing such topical texts could provide useful insights for practitioners and help them perform further research (Qiu et al, ).…”

Section: Introductionmentioning

confidence: 99%

Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

Qiu

et al. 2020

Earth and Space Science

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Massive unstructured geoscience data are buried in geological reports. Geological text classification provides opportunities to leverage this wealth of data for geology and mineralization research. Existing studies of massive geoscience documents/reports have not provided effective classification results for further knowledge discovery and data mining and often lack adequate domain‐specific knowledge. In this paper, we present a novel and unified framework (namely, Dic‐Att‐BiLSTM) that combines domain‐specific knowledge and bidirectional long short‐term memory (BiLSTM) for effective geological text classification. Dic‐Att‐BiLSTM benefits from a matching strategy by incorporating domain‐specific knowledge developed based on geoscience ontology to grasp the linguistic geoscience clues. Furthermore, Dic‐Att‐BiLSTM brings together the capacity of a geoscience dictionary matching approach and an attention mechanism to construct a dictionary attention layer. Finally, the network framework of Dic‐Att‐BiLSTM can utilize domain‐specific knowledge and classify geological text automatically. Experimental verifications are conducted on two constructed data sets, and the results clearly indicate that Dic‐Att‐BiLSTM outperforms other state‐of‐the‐art text classification models.

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Information extraction and knowledge graph construction from geoscience literature

Cited by 176 publications

References 19 publications

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

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

Bi‐LSTM+CRF‐based named entity recognition in scientific papers in the field of ecological restoration technology

Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

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