Machine Learning-based Query Augmentation for SPARQL Endpoints
Document typeConference lecture
Rights accessOpen Access
European Commisision's projectBigStorage - BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data (EC-H2020-642963)
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)
CitationRico, M. [et al.]. Machine Learning-based Query Augmentation for SPARQL Endpoints. A: 14th International Conference on Web Information Systems and Technologies - Seville (Spain) - 2018. "Proceedings of the 14th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST". 2018, p. 57-67.