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Deriving human activity from geo-located data by ontological and statistical reasoning

Authors
Dashdorj, ZolzayaSobolevsky, StanislavLee, SangKeunRatti, Carlo
Issue Date
1-3월-2018
Publisher
ELSEVIER
Keywords
Ontology; Spatial data; Human activity recognition; Knowledge management
Citation
KNOWLEDGE-BASED SYSTEMS, v.143, pp.225 - 235
Indexed
SCIE
SCOPUS
Journal Title
KNOWLEDGE-BASED SYSTEMS
Volume
143
Start Page
225
End Page
235
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/76789
DOI
10.1016/j.knosys.2017.11.038
ISSN
0950-7051
Abstract
Every day, billions of geo-referenced data (e.g., mobile phone data records, geo-tagged social media, gps records, etc.) are generated by user activities. Such data provides inspiring insights about human activities and behaviors, the discovery of which is important in a variety of domains such as social and economic development, urban planning, and health prevention. The major challenge in those areas is that interpreting such a big stream of data requires a deep understanding of context where each activity occurs. In this study, we use a geographical information data, OpenStreetMap (OSM) to enrich such context with possible knowledge. We build a combined logical and statistical reasoning model for inferring human activities in qualitative terms in a given context. An extensive validation of the model is performed using separate data-sources in two different cities. The experimental study shows that the model is proven to be effective with a certain accuracy for predicting the context of human activity in mobile phone data records. (C) 2017 Elsevier B.V. All rights reserved.
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