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Project SLIPO Official End

4 years ago by

The EU project, “Scalable Linking and Integration of Big POI Data” (SLIPO) was completed on November 31st, 2019. Over the last 36 months, 6 partners coordinated their efforts to achieve the project goals. SLIPO extends and integrates leading open source software for semantic integration of geospatial data, aiming to develop effective, efficient and scalable tools for POI integration and enrichment. We are pleased to announce that during the SLIPO project, we successfully published more than 30 publications.

In A Nutshell

SLIPO is a Horizon 2020 Innovation Action (IA) developing linked data technologies for the scalable and quality-assured integration of Big POI Data assets. SLIPO develops software, models and processes for:

  1. Transforming conventional POI formats and schemas into RDF data;
  2. Interlinking POI entities from different datasets;
  3. Enriching POI entities with additional metadata, including temporal, thematic and semantic properties;
  4. Fusing Linked POI data in order to produce more complete and accurate POI profiles;
  5. Assessing the quality of the integrated POI data;
  6. Offering value-added services based on spatial aggregation, association extraction and spatiotemporal prediction.

The SLIPO Toolkit

SLIPO extends a series of tools, creating robust, efficient and scalable commercial-level software that focuses on the requirements and particularities of the POI integration and enrichment process.

  • Sparqlify and TripleGeo are powerful tools that handle the transformation of geospatial data from several sources and formats into RDF triples.
  • LIMES (from the DICE group) is the state-of-the-art tool for interlinking RDF data, taking also into account spatial dimensions of entities.
  • FAGI is the first platform to allow fusion of geospatial Linked Data, supporting several thematic and spatial fusion actions.
  • DEER (from the DICE group) is a data enrichment framework that applies enrichment functions and operators to discover implicit or explicit references of entities to external datasets.
  • OSMRec is a framework for the semantic enrichment and classification of geospatial entities.
  • SANSA is distributed in memory framework for RDF, providing scalable inference and analytics capabilities for Linked Data.
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