Abstract
Spatial association analysis is an important task in spatial data mining. The co-location pattern, an important expression form of spatial association analysis, has guided users’ decisions in many aspects, such as city service, business, etc. However, current methods aim at mining co-location patterns consisting of fine-grained features, they ignore the background knowledge including known relationships of things in a certain domain. Moreover, current methods generate numerous and independent patterns, which causes users to be confused when making the following decisions. Unlike existing works, in this demonstration, we present a system called OIIKM (Ontology-based Interesting Implied Knowledge Miner), which employs ontology to integrate user knowledge during the mining process, to discover more knowledge (represented it as co-location patterns consisting of ontology concepts) implied in the spatial datasets. Besides, to alleviate user confusion, OIIKM provides a visual OntologyTree for the user to select ontology concepts she/he is interested in, by which only a few patterns are displayed to provide more guidance for better decisions.
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Acknowledgements
This work was supported in part by grants (No. U1811264, No. U1711263, No. 61966009, No. 62006057, 61762027) from the National Natural Science Foundation of China, in part by grants (No. 2018GXNSFDA281045, No. 2019GXNSFBA245059) from the Natural Science Foundation of Guangxi Province, and in parts by grants (No. AD19245011) from the Key Research and Development Program of Guangxi Province.
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Chang, L., Wang, L., Bao, X., Gu, T. (2022). OIIKM: A System for Discovering Implied Knowledge from Spatial Datasets Using Ontology. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_46
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DOI: https://doi.org/10.1007/978-3-031-00129-1_46
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