Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Machine Learning-Based Query Augmentation for SPARQL Endpoints
Año:2018
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
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 ?nd the information they need through trial-and-error or to query related resources. However, instead of executing eachindividualqueryseparately,queryaugmentationaimsatmodifyingtheincomingqueriestoretrievemore 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 augmented queries, we can retrieve data relevant to several subsequent queries at once, achieving a higher cache hit rate than previous approaches.
Internacional
Si
Nombre congreso
14th International Conference on Web Information Systems and Technologies WEBIST 2018
Tipo de participación
960
Lugar del congreso
Sevilla
Revisores
Si
ISBN o ISSN
978-989-758-324-7
DOI
10.5220/0006925300570067
Fecha inicio congreso
18/09/2018
Fecha fin congreso
20/09/2018
Desde la página
57
Hasta la página
67
Título de las actas
Proceedings of the 14th International Conference on Web Information Systems and Technologies
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Mariano Rico Almodovar (UPM)
  • Autor: Maria de los Santos Perez Hernandez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
  • Departamento: Inteligencia Artificial
S2i 2021 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)