- Contact person: Spiros Athanasiou
- Relevant publications
- Thematic tags: Big Data , Semantic web , Geospatial data , Data integration , RDF
- Start date: 01-01-2017
- Duration: 36 months
- Programme: IA, H2020-ICT-14-2016
- Funding: 2.64 MEuro
- IMSI funding: 748 KEuro
- Project webpage: http://www.slipo.eu
- Partners: Athena Research and Innovation Center (IMIS), Institute for Applied Informatics e.V. (InfAI), Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), TomTom Development Germany GmbH (TomTom), WIGeoGIS mbH (WiGeoGIS), Geospatial Enabling Technologies LtD (GET),
SLIPO (www.slipo.eu) is an Horizon 2020 Innovation Action (IA) developing technologies for the scalable and quality assured integration of Points of Interest (POI) Big Data assets.
POI data are the cornerstone of any application, service, and product even remotely related to our physical surroundings. From navigation applications, to social networks, to tourism, and logistics, we use POIs to search, communicate, decide, and plan our actions. The creation, update, and provision of POIs consists a multi-billion cross-domain and cross-border industry, with a value chain natively incorporating most domains of our economy. The evolved POI value chain introduces opportunities for growth, but also complexity, intensifying the challenges relating to their quality-assured integration, enrichment, and data sharing. However, the integration of POIs using current approaches remains labor-intensive and scalable only for domain-specific or small-scale efforts.
In SLIPO, we argue that linked data technologies can address the limitations, gaps and challenges of the current landscape in integrating, enriching, and sharing POI data. Our goal is to transfer the research output generated by our work in project GeoKnow, to the specific challenge of POI data, introducing validated and cost-effective innovations across their value chain. Specifically, SLIPO develops effective and scalable software and processes for: transforming conventional POI formats and schemas into RDF data; interlinking POI entities from different datasets; enriching POI entities with additional metadata, including temporal, thematic and semantic properties; fusing Linked POI data in order to produce more complete and accurate POI profiles; assessing the quality of the integrated POI data; offering value added services based on spatial aggregation, association extraction and spatiotemporal prediction.