A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets

Mohamed, Heba; Fathalla, Said; Lehmann, Jens; Jabeen, Hajira

doi:10.5220/0010656800003064

Cited by 3 publications

(1 citation statement)

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“…More recently, Mohamed et al introduced the SANSA framework. 28 Their approach shares many similarities with ours, combining multiple techniques to achieve scalable reasoning over large-scale OWL datasets using Spark. However, they use data stored in memory.…”

Section: Related Workmentioning

confidence: 98%

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

Benítez‐Hidalgo,

Navas‐Delgado,

Roldán‐García

2023

Softw Pract Exp

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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.

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Section: Related Workmentioning

confidence: 98%

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

Benítez‐Hidalgo,

Navas‐Delgado,

Roldán‐García

2023

Softw Pract Exp

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CECR: Collaborative Semantic Reasoning on the Cloud and Edge

Sun,

Ren,

Zhang

et al. 2023

Lecture Notes in Computer Science

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Construction of Knowledge Graphs: Current State and Challenges

Hofer,

Obraczka,

Saeedi

et al. 2024

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With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources (e.g., text) and structured data sources (e.g., databases) are mostly well researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the main graph models for KGs and introduce the major requirements for future KG construction pipelines. Next, we provide an overview of the necessary steps to build high-quality KGs, including cross-cutting topics such as metadata management, ontology development, and quality assurance. We then evaluate the state of the art of KG construction with respect to the introduced requirements for specific popular KGs, as well as some recent tools and strategies for KG construction. Finally, we identify areas in need of further research and improvement.

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A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets

Cited by 3 publications

References 0 publications

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

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

CECR: Collaborative Semantic Reasoning on the Cloud and Edge

Construction of Knowledge Graphs: Current State and Challenges

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