T3.1 – Spatial knowledge mapping

One goal of this task is to develop algorithms and services to map spatial (datasets with geometric information) and non-spatial RDF data. For example, often datasets only contain implicit geographic references, such as city names. However, this information alone is not sufficient for unambiguously identifying geographic entities.A further goal is therefore to provide a tool assisted way of providing context information and parameterizing transformations for the generation of high quality RDF datasets. The following components will be developed: a component to lift implicit geographical references in data sets. Given an RDF data set with implicit geographical references, the module will automatically detect potential types of geospatial information and the relations to existing vocabularies. For instance, a dataset containing address information could be automatically geocoded using a service such as Nominatim. The address information itself can be identified either by the used vocabulary or by crosschecking the used values against reference lists of e.g. country, city and street names. Additionally, this enables interlinking with spatial datasets, such as GeoNames, DBpedia or LinkedGeoData. The component will be manually evaluated (a) using information retrieval measures, such as precision and recall, and (b) by comparison with reference datasets. For large datasets, sampling methods will be applied. a component to configure transformation of data in conventional formats into RDF using existing vocabularies. Based on the detected implicit information,the user will be able to specify the type, format, granularity and amount of data being transformed. a distributed system for continuously processing a large amount of conventional data into an RDF representation based on the configuration. The challenge here is the expected amount of data to be processed. Since the original data sets remain unchanged and evolve over time it is required to be able to quickly re-process large amount of RDF data. This component will be evaluated for its performance, such as by measuring the process time for different datasets and dataset sizes.


D3.1.1 Development of first prototype for spatially interlinking data sets
D3.1.2 System to Integrate Large-Scale Data Sets Based on Implicit Spatial Relations
D3.1.3 Evaluation of Spatial Interlinking

Other Tasks in this Workpackage

T3.2 Spatial knowledge fusing
T3.3 Quality aware spatial knowledge aggregation
T3.4 Metrics for Volunteered Geographic Information

Hosted by

Universität Leipzig , InfAI: Institut für Angewandte Informatik

Funded by

EU Seventh Framework Programme (FP7)

Community and Social Media



EDF2015 and Linked Data Europe: Big Geospatial Data Workshop ( 2015-11-24T23:46:33+01:00 Alejandra Garcia Rojas)

2015-11-24T23:46:33+01:00 Alejandra Garcia Rojas

In 2015, the European Data Forum took place in Luxembourg on the 16th and 17th November. GeoKnow team had the pleasure to be present at the event with a booth for showing GeoKnow results. The conference welcomed over 700 participants from industry, research, policy makers, and community initiatives form all over Europe. Read more about "EDF2015 and Linked Data Europe: Big Geospatial Data Workshop"

Linked Open Data Switzerland at SWBI2015 ( 2015-10-12T09:56:15+02:00 Daniel Hladky)

2015-10-12T09:56:15+02:00 Daniel Hladky

Daniel Hladky from Ontos presented GeoKnow at the SWBI2015 conference two talks. The first talk was the keynote on October 7, 2015 with the title “Linked Data Service (LINDAS): Status quo of the Linked Data life-cycle and lessons learned“. Read more about "Linked Open Data Switzerland at SWBI2015"

FAGI-gis: fusing geospatial RDF data ( 2015-10-05T13:08:20+02:00 Giorgos Giannopoulos)

2015-10-05T13:08:20+02:00 Giorgos Giannopoulos

GeoKnow introduces the latest version of FAGI-gis, a framework for fusing Linked Data, that focuses on the geospatial properties of the linked entities. Read more about "FAGI-gis: fusing geospatial RDF data"

GeoKnow Public Datasets ( 2015-09-19T16:15:29+02:00 Alejandra Garcia Rojas)

2015-09-19T16:15:29+02:00 Alejandra Garcia Rojas

In this blogpost we want to present three public datasets that were improved/created in GeoKnow project. LinkedGeoData Size: 177GB zipped turtle file URL: http://linkedgeodata.org/ LinkedGeoData is the RDF version of Open Street Map (OSM), which covers the entire planet geospatial data information. Read more about "GeoKnow Public Datasets"

GeoKnow at Semantics 2015, Vienna ( 2015-09-18T15:12:48+02:00 Alejandra Garcia Rojas)

2015-09-18T15:12:48+02:00 Alejandra Garcia Rojas

Several partners of GeoKnow were present this year at the Semantics conference 2015. The previous day of the conference we organised a workshop about the work done during these last three years in GeoKnow. Read more about "GeoKnow at Semantics 2015, Vienna"