Abstract
Several real-world applications of made evident the need for efficiently handling multiple queries and reasoning tasks over large-sized knowledge bases. In this paper we present some recent enhancements in the ASP system for enabling reasoning over large-scale domains. In particular, we developed both an optimized implementation, sensibly reducing memory consumption, and a server-like behaviour to support efficiently multiple-query scenarios. The high potential of for large-scale reasoning is outlined by the results of an experiment on data-intensive benchmarks. The applicability of the system in real-world scenarios is demonstrated employing as reasoning service to query, in natural language, the large DBpedia knowledge base. The relevance and the high potential industrial value of this research is also confirmed by the direct interest of a major international industrial player, which has stimulated and partially supported this work.
Keywords
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Acknowledgments
This work has been partially supported by Samsung under project “Enhancing the DLV system for large-scale ontology reasoning”, by MISE under project “S2BDW” (F/050389/01-03/X32) – “Horizon2020” PON I&C2014-20 and by Regione Calabria under project “DLV LargeScale” (CUP J28C17000220006) – POR Calabria 2014-20.
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Leone, N. et al. (2019). Enhancing DLV for Large-Scale Reasoning. In: Balduccini, M., Lierler, Y., Woltran, S. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2019. Lecture Notes in Computer Science(), vol 11481. Springer, Cham. https://doi.org/10.1007/978-3-030-20528-7_23
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DOI: https://doi.org/10.1007/978-3-030-20528-7_23
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