Popular knowledge bases that provide SPARQL endpoints for the web are usually experiencing a high number of requests, which often results in low availability of their interfaces. A common approach to counter the availability issue is to run a local mirror of the knowledge base. Running a SPARQL endpoint is currently a complex task which requires a lot of effort and technical support for domain experts who just want to use the SPARQL interface. With our approach of containerised knowledge base shipping we are introducing a simple to setup methodology for running a local mirror of an RDF knowledge base and SPARQL endpoint with interchangeable exploration components. The flexibility of the presented approach further helps maintaining the publication infrastructure for dataset projects. We are demonstrating and evaluating the presented methodology at the example of the dataset projects DBpedia, Catalogus Professorum Lipsiensium and Sächsisches Pfarrerbuch.
%0 Conference Paper
%1 arndt-n-2015--k
%A Arndt, Natanael
%A Ackermann, Markus
%A Brümmer, Martin
%A Riechert, Thomas
%B 11th International Conference on Semantic Systems Proceedings
%C Vienna, Austria
%D 2015
%K dbpedia dbpedia_core dbpedia_scipub
%P 73-80
%R 10.1145/2814864.2814885
%T Knowledge Base Shipping to the Linked Open Data Cloud
%U http://svn.aksw.org/papers/2015/SEMANTICS_DockerizingLOD/public.pdf
%X Popular knowledge bases that provide SPARQL endpoints for the web are usually experiencing a high number of requests, which often results in low availability of their interfaces. A common approach to counter the availability issue is to run a local mirror of the knowledge base. Running a SPARQL endpoint is currently a complex task which requires a lot of effort and technical support for domain experts who just want to use the SPARQL interface. With our approach of containerised knowledge base shipping we are introducing a simple to setup methodology for running a local mirror of an RDF knowledge base and SPARQL endpoint with interchangeable exploration components. The flexibility of the presented approach further helps maintaining the publication infrastructure for dataset projects. We are demonstrating and evaluating the presented methodology at the example of the dataset projects DBpedia, Catalogus Professorum Lipsiensium and Sächsisches Pfarrerbuch.
@inproceedings{arndt-n-2015--k,
abstract = {Popular knowledge bases that provide SPARQL endpoints for the web are usually experiencing a high number of requests, which often results in low availability of their interfaces. A common approach to counter the availability issue is to run a local mirror of the knowledge base. Running a SPARQL endpoint is currently a complex task which requires a lot of effort and technical support for domain experts who just want to use the SPARQL interface. With our approach of containerised knowledge base shipping we are introducing a simple to setup methodology for running a local mirror of an RDF knowledge base and SPARQL endpoint with interchangeable exploration components. The flexibility of the presented approach further helps maintaining the publication infrastructure for dataset projects. We are demonstrating and evaluating the presented methodology at the example of the dataset projects DBpedia, Catalogus Professorum Lipsiensium and S{\"a}chsisches Pfarrerbuch.},
added-at = {2017-06-19T08:17:31.000+0200},
address = {Vienna, Austria},
author = {Arndt, Natanael and Ackermann, Markus and Br{\"u}mmer, Martin and Riechert, Thomas},
biburl = {https://www.bibsonomy.org/bibtex/2b1e393a0bfd62e83b99704a52c20c877/dbpedia-pub},
booktitle = {11th International Conference on Semantic Systems Proceedings},
doi = {10.1145/2814864.2814885},
interhash = {a3f75e06862072243dd5f3215afb3b93},
intrahash = {b1e393a0bfd62e83b99704a52c20c877},
issn = {1613-0073},
keywords = {dbpedia dbpedia_core dbpedia_scipub},
month = sep,
owner = {natanael},
pages = {73-80},
series = {International Conference on Semantic Systems Proceedings},
timestamp = {2017-06-27T15:01:28.000+0200},
title = {Knowledge Base Shipping to the Linked Open Data Cloud},
url = {http://svn.aksw.org/papers/2015/SEMANTICS_DockerizingLOD/public.pdf},
year = 2015
}