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An Ontology Based Approach for Geospatial Data Integration of Authoritative and Crowd Sourced Datasets
| Content Provider | Semantic Scholar |
|---|---|
| Abstract | BACKGROUND AND OBJECTIVES The progress of national and international spatial data infrastructures such as the UK Location Programme and European Commission INSPIRE SDI, contrasted against crowd-sourced geospatial databases such as OpenStreetMap, creates a promising opportunity for exploring data integration between crowd sourced information and authoritative data. A further aim of this research was to look into the mid-term and long-term effects of crowd sourcing technologies on the change intelligence operations of national mapping agencies (NMAs). This paper explains an ontology-based approach for geospatial data integration between crowd sourced and authoritative data. An algorithm for feature matching based on ontology matching concepts is presented. An implementation of the algorithm is also presented, together with initial experimental results. This research has been carried out to understand the issues of data integration between crowd-sourced information and authoritative data. Ordnance Survey (OS), as the national mapping agency of Great Britain, provides authoritative datasets with published data specifications, driven by a combination of user need and the history of national mapping with a remit to ensure real-world feature changes are reflected in the OS large-scale data within 6 months. OpenStreetMap (OSM), in contrast, relies on the availability of local mapping enthusiasts to capture changes, but through its more informal structure, can capture a broader range of features of interest to different sub-communities, such as cyclists or horse riders (Anand et al, 2010). Geospatial data integration (GDI) in this context refers to combining geographic data, including spatial and non-spatial data, from disparate sources, with differing conceptual, contextual and topographical representations. The paper investigates feature matching using a geosemantic algorithm for position and high level ontological description. The algorithm can be qualified as fuzzy as it combines probabilistic answers obtained from conceptual matching functions. This research used OS and OSM datasets as a case study for exploring techniques for integrating the authoritative and crowd sourced data. An open source software application was developed to integrate geospatial data from disparate sources. The methodology, implementation and experimentation details of this research are described in this paper. CASE STUDY Ordnance Survey " s Integrated Transport Network (ITN) data and OpenStreetMap (OSM) road data for Portsmouth, UK were used as a case study to explore ontology based methodologies for integrating the two heterogeneous data sources. The two input data sets used are shown in Figure 1 and Figure 2. The OSM data has been filtered to select only the road features … |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://icaci.org/files/documents/ICC_proceedings/ICC2011/Oral%20Presentations%20PDF/B2-Ontology%20and%20data%20mining%20for%20integration/CO-118.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
