NORA: Scalable OWL reasoner based on NoSQL databases and Apache Spark

Abstract Reasoning is the process of inferring new knowledge and identifying inconsistencies within ontologies. Traditional techniques often prove inadequate when reasoning over large Knowledge Bases containing millions or billions facts. This article introduces NORA, a persistent scalable OWL reasoner built on top Apache Spark, designed to address challenges extensive complex NORA exploits scalability NoSQL databases effectively apply inference rules Big Data ontologies with ABoxes. To facilitate reasoning, data, including class property hierarchies instances, are materialized in Cassandra database. Spark programs then evaluated iteratively, uncovering implicit from dataset leading enhanced performance more efficient large‐scale has undergone thorough evaluation different benchmarking varying sizes assess developed solution.