QAVAN: Query-answering approach for actionable numerical relationships over Knowledge Graphs

Abstract

Semantic Web (SW) technologies are suitable to represent taxonomic knowledge in Knowledge Graphs (KG). However, cases that do not fall in this category such as numerical relationships (e.g., algebraic operations or unit conversion) that can enrich the KGs data are poorly represented. Current approaches mostly trigger these relationships in a materialised fashion (i.e., perform all the relationships in advance), which leads to storage and updating problems. Besides, these relationships are often not represented using linked data formats ignoring the FAIR and LOD guidelines, which leads to issues of interoperability, reproducibility and adoption. In this work, we propose QAVAN, a query-answering approach that computes efficiently only the necessary numerical relationships relevant to answer to a given query. QAVAN exploits SW technologies such as SHACL for representing and computing these relationships while plays in favour of FAIR and LOD principles. We evaluated and compared QAVAN against a materialised approach using two real datasets extracted from two very well-known data sources, DBpedia and MeteoFrance. Results expose the numerous benefits of QAVAN in terms of execution time, memory usage, and disk space in different scenarios for example against big datasets. We also evaluated two QAVAN extensions, the nested numerical relationships and the inference services. Our results show that these extensions allow to apply the numerical relationships for a greater number of instances to provide more query results.