Visual analytics and information extraction of geological content for text-based mineral exploration reports

Wang, Bin; Ma, Kai; Wu, Liang; Qiu, Qinjun; Xie, Zhen; Tao, Liufeng

doi:10.1016/j.oregeorev.2022.104818

Cited by 22 publications

(4 citation statements)

References 38 publications

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“…We conducted a frequency analysis of word usage using text mining based on these transcriptions. This method is one of several analytical techniques used in text mining and helps to analyze descriptive data and quickly understand key information within data [44], [45].…”

Section: ) Interviewsmentioning

confidence: 99%

Evaluating the Impact of Presentation on Learning and Narrative in AR of Cultural Heritage

Yoo,

2024

IEEE Access

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Cultural heritage exhibits an inherent relationship among various historical concepts and events. Previous studies that integrate augmented reality with cultural heritage have recognized AR as an effective tool for experiencing and learning about cultural heritage. In relation to cultural heritage AR, many researchers use presentations to provide users with diverse historical information and stories. However, too few cases have evaluated the influence of the use of presentations in cultural heritage AR on users' learning and narratives. Therefore, our study aimed to investigate the impact of presentation methods on users' learning achievements and narrative formation outcomes in the context of cultural heritage AR. To conduct this study, we divided users into three groups and presented them with AR experiences specifically designed with different types of presentations. We conducted uniform user evaluations, assessments of learning abilities, and in-depth interviews across all three groups, comparing the results. Ultimately, the method that had the most significant impact on both user learning and the narrative involved placing key cultural heritage information within virtual environments and repeatedly utilizing visual presentations. In conclusion, the group that adopted this approach exhibited significantly better results in terms of the evaluation of learning effectiveness and learning achievements compared to the other methods. Furthermore, it demonstrated a clear difference in visual graph representations for confirming narratives and word usage frequency, establishing it as a superior presentation method compared to the alternatives. Based on these findings, we offer practical guidelines for enhancing the use of presentations in cultural heritage AR experiences.

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Section: ) Interviewsmentioning

confidence: 99%

Evaluating the Impact of Presentation on Learning and Narrative in AR of Cultural Heritage

Yoo,

2024

IEEE Access

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“…The British and United States Geological Surveys have developed problem-driven big data research and application programs in response to societal needs, and scientists often use a data-driven scientific paradigm to carry out their work using massive, real-time, synthetic, geospatial data for data mining, discovering knowledge, modeling, generating hypotheses and validating results [19,20]. China is also following the trend of the big data era by applying geological big data in various applications such as digital mineral exploration, smart city construction, and disaster prevention and mitigation [21,22]. The variability, robustness, relevance, and composition of geological data are inherently influenced by temporal, spatial, and geological factors.…”

Section: Introductionmentioning

confidence: 99%

Developing a Base Domain Ontology from Geoscience Report Collection to Aid in Information Retrieval towards Spatiotemporal and Topic Association

Tao,

Ma,

Tian

et al. 2023

IJGI

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The efficient and precise retrieval of desired information from extensive geological databases is a prominent and pivotal focus within the realm of geological information services. Conventional information retrieval methods primarily rely on keyword matching approaches, which often overlook the contextual and semantic aspects of the keywords, consequently impeding the retrieval system’s ability to accurately comprehend user query requirements. To tackle this challenge, this study proposes an ontology-driven information-retrieval framework for geological data that integrates spatiotemporal and topic associations. The framework encompasses the development of a geological domain ontology, extraction of key information, establishment of a multi-feature association and retrieval framework, and validation through a comprehensive case study. By employing the proposed framework, users are empowered to actively and automatically retrieve pertinent information, simplifying the information access process, mitigating the burden of comprehending information organization and software application models, and ultimately enhancing retrieval efficiency.

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“…Natural language processing (NLP) is a subfield of artificial intelligence focused on interpreting human language by learning the meaning of words and sentences (i.e., text semantics; Bengio et al, 2000;Mikolov et al, 2013aMikolov et al, , 2013bPennington et al, 2014;Devlin et al, 2019;Chowdhary, 2020). The application of NLP to geoscience text data has so far included summarizing articles (Ma et al, 2021), translating languages (Qiu et al, 2018;Consoli et al, 2020;Gomes et al, 2021), generating keywords (Qiu et al, 2018(Qiu et al, , 2019, and information discovery (Peters et al, 2014(Peters et al, , 2018Wang et al, 2018;Holden et al, 2019;Enkhsaikhan et al, 2021aEnkhsaikhan et al, , 2021bMa 2022;Wang et al, 2022). These and other geoscience NLP applications are possible because of recent opensource tools developed by the artificial intelligence community, improved access to high-performance cloud computing, and the increased availability of internet text-data for training state-of-the-art language models (e.g., Open AIÕs GPT-3 and -4;Floridi & Chiriatti, 2020;Dale, 2021).…”

Section: Introductionmentioning

confidence: 99%

Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling

et al. 2023

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Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of “pegmatitic” (AUC = 0.962) and “alkalic” (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials.

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Visual analytics and information extraction of geological content for text-based mineral exploration reports

Cited by 22 publications

References 38 publications

Evaluating the Impact of Presentation on Learning and Narrative in AR of Cultural Heritage

Evaluating the Impact of Presentation on Learning and Narrative in AR of Cultural Heritage

Developing a Base Domain Ontology from Geoscience Report Collection to Aid in Information Retrieval towards Spatiotemporal and Topic Association

Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling

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