A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data

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Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain‐geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)‐(Bidirectional gated recurrent unit network) BiGRU‐ (Conditional random field) CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models.

Section: Introductionmentioning

confidence: 99%

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Xie

Xu³

et al. 2022

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“…It means an urgent need to improve information extraction, knowledge mining, and knowledge association in heterogeneous geological data (Li & Shao, 2009). Recent studies have shown that the performance of downstream tasks for text mining, such as part‐of‐speech tagging, retrieve text from images (Shao et al., 2020; Zhou et al., 2017) and named entity recognition (Ma et al., 2018; Qiu et al., 2018, 2019; L. Wu et al., 2017), strongly depends on high precision geological word segmentation methods. This is because all of these downstream tasks, beside the extraction of multi‐level and multi‐dimensional image features required in retrieving text from images, require the system to have a good understanding of the text, which is the cornerstone of Chinese word segmentation (CWS).…”

Section: Introductionmentioning

confidence: 99%

Chinese Word Segmentation Based on Self‐Learning Model and Geological Knowledge for the Geoscience Domain

Qiu

et al. 2021

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“…The textual content from studies is recorded and presented in a variety of types, such as journal papers, technical documents/reports, and monographs and books (Huang et al, ). 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, ).…”

Section: Introductionmentioning

confidence: 99%

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

Qiu

et al. 2020

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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.