%0 Conference Paper %1 prudhomme2017automatic %A Prudhomme, Claire %A Homburg, Timo %A Jean-Jacques, Ponciano %A Boochs, Frank %A Roxin, Ana %A Cruz, Christophe %B Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, %D 2017 %I SciTePress %K Data, Geospatial Language Linked Natural Ontology, Processing, R2RML, SDI, Semantic Semantification Web, %P 107-115 %R 10.5220/0006306601070115 %T Automatic Integration of Spatial Data into the Semantic Web %U http://www.scitepress.org/digitalLibrary/PublicationsDetail.aspx?ID=9PVXQr5fDjQ=&t=1 %X For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e.g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources. For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach. %@ 978-989-758-246-2

@inproceedings{prudhomme2017automatic, abstract = {For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e.g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources. For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach.}, added-at = {2019-09-17T13:26:44.000+0200}, author = {Prudhomme, Claire and Homburg, Timo and Jean-Jacques, Ponciano and Boochs, Frank and Roxin, Ana and Cruz, Christophe}, biburl = {https://www.bibsonomy.org/bibtex/20854ee592715fc755ec153bb9ef76407/situx}, booktitle = {Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,}, day = 26, doi = {10.5220/0006306601070115}, interhash = {24f2c4c834b6e22cbcfa584006adb7b7}, intrahash = {0854ee592715fc755ec153bb9ef76407}, isbn = {978-989-758-246-2}, keywords = {Data, Geospatial Language Linked Natural Ontology, Processing, R2RML, SDI, Semantic Semantification Web,}, language = {english}, location = {Porto, Portugal}, month = {April}, note = {Best Student Paper Award}, organization = {INSTICC}, pages = {107-115}, publisher = {SciTePress}, timestamp = {2019-09-17T13:27:01.000+0200}, title = {Automatic Integration of Spatial Data into the Semantic Web}, url = {http://www.scitepress.org/digitalLibrary/PublicationsDetail.aspx?ID=9PVXQr5fDjQ=&t=1}, year = 2017 }