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

Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition

“…Nonetheless, building image-based databases generates significant problems in finding the desired image. The semantic web is playing a very imperative role in information access by using sources like OWL, RDFs, and SPARQL (Benítez-Hidalgo et al, 2023). To address the intricacies of semantics, advanced semantic technologies using ontology is front running candidate.…”