Semantic enrichment and analysis of movement data

Abstract

The widespread use of sensors and information systems, frequently via mobile devices, allows gathering large amounts of movement data, such as trajectories of moving objects and sequences of users posts on social media. These data can enable several applications, but some of them involve understanding what is going on with moving objects (e.g., exact places and/or events of interest, activities performed, reasons for stops and moves). Thus, there is a demand to enrich with well-defined semantics the potentially imprecise spatiotemporal coordinates of movement data, which are sometimes tied together with text (e.g., comments, tags). This paper provides an overview of proposals and possible developments in semantic enrichment and analysis of movement data. It also presents some details of our current methods to associate movement data with concepts and/or instances described in ontologies or Linked Open Data (LOD). Our experiments with methods to associate tweets with places visited by the users who posted them show that textual contents of some tweets can contribute to make correct associations. In addition, the experience suggests that a variety of techniques can be helpful for semantically enriching movement data in several analysis dimensions. It poses many research challenges, some of them multidisciplinary.