An Evolutionary Perspective on Approximate RDF Query Answering
C. Guéret, E. Oren, S. Schlobach, and M. Schut. Proceedings of the 2nd International Conference on Scalable Uncertainty Management, volume 5291 of Lecture Notes in Computer Science, page 215--228. Berlin, Heidelberg, Springer, (October 2008)
DOI: 10.1007/978-3-540-87993-0_18
Abstract
RDF is increasingly being used to represent large amounts of data on the Web. Current query evaluation strategies for RDF are inspired by databases, assuming perfect answers on finite repositories. In this paper, we focus on a query method based on evolutionary computing, which allows us to handle uncertainty, incompleteness and unsatisfiability, and deal with large datasets, all within a single conceptual framework. Our technique supports approximate answers with “anytime” behaviour. We present scalability results and next steps for improvement.
%0 Conference Paper
%1 Gueret2008EvolutionaryRDF
%A Guéret, Christophe
%A Oren, Eyal
%A Schlobach, Stefan
%A Schut, Martijn
%B Proceedings of the 2nd International Conference on Scalable Uncertainty Management
%C Berlin, Heidelberg
%D 2008
%E Greco, Sergio
%E Lukasiewicz, Thomas
%I Springer
%K
%P 215--228
%R 10.1007/978-3-540-87993-0_18
%T An Evolutionary Perspective on Approximate RDF Query Answering
%V 5291
%X RDF is increasingly being used to represent large amounts of data on the Web. Current query evaluation strategies for RDF are inspired by databases, assuming perfect answers on finite repositories. In this paper, we focus on a query method based on evolutionary computing, which allows us to handle uncertainty, incompleteness and unsatisfiability, and deal with large datasets, all within a single conceptual framework. Our technique supports approximate answers with “anytime” behaviour. We present scalability results and next steps for improvement.
@inproceedings{Gueret2008EvolutionaryRDF,
abstract = {RDF is increasingly being used to represent large amounts of data on the Web. Current query evaluation strategies for RDF are inspired by databases, assuming perfect answers on finite repositories. In this paper, we focus on a query method based on evolutionary computing, which allows us to handle uncertainty, incompleteness and unsatisfiability, and deal with large datasets, all within a single conceptual framework. Our technique supports approximate answers with “anytime” behaviour. We present scalability results and next steps for improvement.},
added-at = {2011-12-12T19:01:27.000+0100},
address = {Berlin, Heidelberg},
author = {Gu{\'e}ret, Christophe and Oren, Eyal and Schlobach, Stefan and Schut, Martijn},
biburl = {https://www.bibsonomy.org/bibtex/22e165ce3ac84bea51285743c678277ec/gergie},
booktitle = {Proceedings of the 2nd International Conference on Scalable Uncertainty Management},
doi = {10.1007/978-3-540-87993-0_18},
editor = {Greco, Sergio and Lukasiewicz, Thomas},
file = {:Gueret2008EvolutionaryRDF.pdf:PDF},
groups = {public},
interhash = {794f40b171e92c1f7f4b4e48fd00de20},
intrahash = {2e165ce3ac84bea51285743c678277ec},
keywords = {},
month = {October},
pages = {215--228},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2011-12-12T19:01:27.000+0100},
title = {{An Evolutionary Perspective on Approximate RDF Query Answering}},
username = {gergie},
volume = 5291,
year = 2008
}