Abstract
Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets.
Highlights
Sound decision-making in the geographical domain involves answering to complex queries, which requires inferring facts from available geospatial data sources
Geospatial information retrieval is an integral part of routing and navigation services, Forfind this issue, in this paper, we focus of on interests the problem of how to support topological notably to help the relevant landmarks and points that should be displayed on the map queries over features that are only implicitly defined
While [37] assumed that the semantics is shared by all requestors and providers, in our approach, we do not make this assumption and we rather address the issue of employing ontologies by proposing a query enrichment approach based on a framework of semantic annotations and mappings among various resources. In addition to this first contribution, we propose an Semantic Web Rule Language (SWRL)-based information retrieval approach that will enable the retrieval of implicit information, i.e., geospatial entities that do not exist in the available source, but which existence can be inferred from existing data
Summary
Sound decision-making in the geographical domain involves answering to complex queries, which requires inferring facts from available geospatial data sources. A well-known example is OpenStreetMap (OSM), which has become an experimental platform to study the VGI phenomena and demonstrate all of the opportunities of VGI (as a subset of open geospatial data) for a plethora of applications, especially in urban studies [1,2]. With these new promises, users are expecting that they will have access to large datasets, but more importantly, they will be able to pose more complex queries and infer more information than ever. Data completeness is one of the spatial data quality elements according to ISO 19157 standard [7], which refers to the presence or lack of certain information in a dataset
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