2021/03/15 by Teodoro Baldazzi, Luigi Bellomarini, Baldazzi, Teodoro +5
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Databases (cs.DB) #FOS: Computer and information sciences #H.2.3 #H.2.4 #Logic, Reasoning, and Knowledge #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2103.08588
openalex publication_date 2021/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent years have seen increasing popularity of logic-based reasoning\nsystems, with research and industrial interest as well as many flourishing\napplications in the area of Knowledge Graphs. Despite that, one can observe a\nsubstantial lack of specific tools able to generate nontrivial reasoning\nsettings and benchmark scenarios. As a consequence, evaluating, analysing and\ncomparing reasoning systems is a complex task, especially when they embody\nsophisticated optimizations and execution techniques that leverage the\ntheoretical underpinnings of the adopted logic fragment. In this paper, we aim\nat filling this gap by introducing iWarded, a system that can generate very\nlarge, complex, realistic reasoning settings to be used for the benchmarking of\nlogic-based reasoning systems adopting Datalog+/-, a family of extensions of\nDatalog that has seen a resurgence in the last few years. In particular,\niWarded generates reasoning settings for Warded Datalog+/-, a language with a\nvery good tradeoff between computational complexity and expressive power. In\nthe paper, we present the iWarded system and a set of novel theoretical results\nadopted to generate effective scenarios. As Datalog-based languages are of\ngeneral interest and see increasing adoption, we believe that iWarded is a step\nforward in the empirical evaluation of current and future systems.\n