Yuanwei Qu

• Qu, Yuanwei; Kamburjan, Eduard; Torabi, Anita & Giese, Martin (2024). Semantically triggered qualitative simulation of a geological process. Applied Computing and Geosciences. ISSN 2590-1974. 21. doi: 10.1016/j.acags.2023.100152.
• Qu, Yuanwei; Zhou, Baifan; Waaler, Arild Torolv Søetorp & Cameron, David B. (2023). Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry. In Liu, Fenrong; Sadanandan, Arun Anand; Pham, Duc Nghia; Mursanto, Petrus & Lukose, Dickson (Ed.), 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023 (PRICAI 2023): Trends in Artificial Intelligence . Springer. ISSN 978-981-99-7024-7. p. 466–473. doi: 10.1007/978-981-99-7025-4_41. Full text in Research Archive
• Qu, Yuanwei; Kamburjan, Eduard & Giese, Martin (2023). A Geological Case Study on Semantically Triggered Processes. In Pesquita, Catia; Jimenez-Ruiz, Ernesto; McCusker, Jamie; Faria, Daniel; Dragoni, Mauro; Dimou, Anastasia; Troncy, Raphaël & Hertling, Sven (Ed.), The Semantic Web: 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28–June 1, 2023, Proceedings. Springer. ISSN 978-3-031-33455-9.
• Qu, Yuanwei; Perrin, Michel; Torabi, Anita; Abel, Mara & Giese, Martin (2023). GeoFault: A well-founded fault ontology for interoperability in geological modeling. Computers & Geosciences. ISSN 0098-3004. 182. doi: 10.1016/j.cageo.2023.105478. Full text in Research Archive

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• Perrin, Michel; Rainaud, Jean-François; Qu, Yuanwei & Abel, Mara (2024). Converting the Geology Knowledge Model Embedded in RESQML into a Well-Conformed Domain Ontology.
• Qu, Yuanwei & Cordeiro, Fábio Corrêa (2023). Term Frequency Analysis for Semantic Modeling of Geological Fault Knowledge in the Energy Industry.
• Qu, Yuanwei; Zheng, Zhuoxun; Zhou, Baifan; Zhou, Yan; Santos, Nicolau & Savkovic, Ognjen [Show all 8 contributors for this article] (2023). Towards Data Integrity Verification for More Sustainable Petroleum Industry.
• Qu, Yuanwei (2022). Geological Information Capture with Sketches and Ontologies.
• Qu, Yuanwei; Zhou, Baifan; Kharlamov, Evgeny & Giese, Martin (2022). Industrial Geological Information Capture with GeoStructure Ontology. Show summary

  Understanding geological structures plays an essential role in a wide range of industries. Various tools and software have been developed to support the observation and characterisation of the geological structures and they produce a large volume of heterogeneous data. Thanks to the development of semantic technologies, plenty of works have investigated industrial geological data. In contrast, geological sketches, figure interpretations, and illustrations as the initial data collection and analysis steps have received little attention. Most structure information is still reported as PDF files with forms, tables, and raster images with interpretations. We attempt to fill this gap by providing an ontology- based industrial geological information capture method to allow users to input and store structure information formally. Following users’ sketches, the corresponding knowledge graphs will be generated. This work aims to make the first-hand geological structure information findable, accessible, interoperable, reusable (FAIR), and to support qualitative information reasoning, from a user-driven perspective, and to meet the needs of digitised industry.

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