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Machine learning-based query augmentation for SPARQL endpoints
dc.contributor.author | Rico Almodóvar, Mariano |
dc.contributor.author | Touma, Rizkallah |
dc.contributor.author | Queralt Calafat, Anna |
dc.contributor.author | Pérez Hernandez, María S. |
dc.contributor.other | Barcelona Supercomputing Center |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació |
dc.date.accessioned | 2019-06-14T08:44:38Z |
dc.date.available | 2019-06-14T08:44:38Z |
dc.date.issued | 2018 |
dc.identifier.citation | Rico, M. [et al.]. Machine learning-based query augmentation for SPARQL endpoints. A: International Conference on Web Information Systems and Technologies. "Proceedings of the 14th International Conference on Web Information Systems and Technologies: September 18-20, 2018, Seville, Spain". 2018, p. 57-67. |
dc.identifier.isbn | 978-989-758-324-7 |
dc.identifier.uri | http://hdl.handle.net/2117/134453 |
dc.description.abstract | Linked Data repositories have become a popular source of publicly-available data. Users accessing this data through SPARQL endpoints usually launch several restrictive yet similar consecutive queries, either to find the information they need through trial-and-error or to query related resources. However, instead of executing each individual query separately, query augmentation aims at modifying the incoming queries to retrieve more data that is potentially relevant to subsequent requests. In this paper, we propose a novel approach to query augmentation for SPARQL endpoints based on machine learning. Our approach separates the structure of the query from its contents and measures two types of similarity, which are then used to predict the structure and contents of the augmented query. We test the approach on the real-world query logs of the Spanish and English DBpedia and show that our approach yields high-accuracy prediction. We also show that, by caching the results of the predicted (More) |
dc.description.sponsorship | This work has been supported by the European Union's Horizon 2020 research and innovation program (grant H2020-MSCA-ITN-2014-642963), the Spanish Ministry of Science and Innovation (contract TIN2015-65316, project RTC-2016-4952-7 and contract TIN2016-78011-C4-4-R), the Spanish Ministry of Education, Culture and Sports (contract CAS18/00333) and the Generalitat de Catalunya (contract 2014-SGR-1051). The authors would also like to thank Toni Cortes for his feedback. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Semantic web |
dc.subject.other | New trends in ontology management and the semantic web |
dc.subject.other | Ontology discovering |
dc.subject.other | Modelling |
dc.subject.other | Retrieving and the semantic web |
dc.title | Machine learning-based query augmentation for SPARQL endpoints |
dc.type | Conference lecture |
dc.subject.lemac | Web semàntica |
dc.identifier.doi | 10.5220/0006925300570067 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006925300570067 |
dc.rights.access | Open Access |
local.identifier.drac | 34164977 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/642963/EU/BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data/BigStorage |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/PE2013-2016/TIN2015-65316 |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/PE2013-2016/TIN2016-78011-C4-4-R |
local.citation.contributor | International Conference on Web Information Systems and Technologies |
local.citation.publicationName | Proceedings of the 14th International Conference on Web Information Systems and Technologies: September 18-20, 2018, Seville, Spain |
local.citation.startingPage | 57 |
local.citation.endingPage | 67 |