stRDFS: Spatiotemporal Knowledge Graph Modeling

التفاصيل البيبلوغرافية
العنوان: stRDFS: Spatiotemporal Knowledge Graph Modeling
المؤلفون: Lin Zhu, Nan Li, Luyi Bai, Yunqing Gong, Yizong Xing
المصدر: IEEE Access, Vol 8, Pp 129043-129057 (2020)
بيانات النشر: Institute of Electrical and Electronics Engineers (IEEE), 2020.
سنة النشر: 2020
مصطلحات موضوعية: Knowledge graph, Focus (computing), General Computer Science, Computer science, General Engineering, 02 engineering and technology, computer.file_format, computer.software_genre, spatiotemporal data, stRDFS, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Overhead (computing), 020201 artificial intelligence & image processing, General Materials Science, lcsh:Electrical engineering. Electronics. Nuclear engineering, ComputingMethodologies_GENERAL, Data mining, RDF, lcsh:TK1-9971, computer, Semantic Web
الوصف: In Semantic Web, modeling knowledge graph based on RDF becomes more and more popular. There is quite a lot of spatiotemporal information in Semantic Web, and recent works focus on not only general data but also spatiotemporal data. Existing efforts are mainly to add spatiotemporal labels to RDF, which expand RDF triple into quad or quintuple. However, extra labels often cause additional overhead for the system and lead to inefficient information organization management. In order to overcome this limitation, we propose an stRDFS model by labeling properties with spatiotemporal features and the corresponding determination methods of topological relations among different spatiotemporal entities. stRDFS considers spatiotemporal attribute as a part of the RDF model, which can record spatiotemporal information without changing the current RDF standard. Our approach improves the ability of recording and linking spatiotemporal data. More importantly, depending on formatting of spatiotemporal attributes in stRDFS, it will improve the semantic inferring ability, and the users are not required to be familiar with the underlying representations of spatiotemporal data.
تدمد: 2169-3536
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69e2bc51028584e3c1a09a3d020bd005Test
https://doi.org/10.1109/access.2020.3008688Test
حقوق: OPEN
رقم الانضمام: edsair.doi.dedup.....69e2bc51028584e3c1a09a3d020bd005
قاعدة البيانات: OpenAIRE