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Descripción
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| 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. | |
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Internacional
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Si |
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Nombre congreso
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14th International Conference on Web Information Systems and Technologies WEBIST 2018 |
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Tipo de participación
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960 |
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Lugar del congreso
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Sevilla |
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Revisores
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Si |
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ISBN o ISSN
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978-989-758-324-7 |
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DOI
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10.5220/0006925300570067 |
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Fecha inicio congreso
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18/09/2018 |
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Fecha fin congreso
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20/09/2018 |
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Desde la página
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57 |
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Hasta la página
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67 |
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Título de las actas
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Proceedings of the 14th International Conference on Web Information Systems and Technologies |