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.
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|Titolo:||Enhancing DLV for Large-Scale Reasoning|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|