Tool for materializing OWL ontologies in a column‐oriented database

Reyes-Álvarez, Liudmila; Roldán‐García, María del Mar; Aldana‐Montes, José F.

doi:10.1002/spe.2645

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…NORA persists OWL data in a NoSQL database, Apache Cassandra. The idea of working with NoSQL databases for storing large volumes of RDF‐like data has been explored in the past 17,20,29‐31 . We have chosen Apache Cassandra over other software implementations of NoSQL databases for several reasons.…”

Section: System Architecturementioning

confidence: 99%

“…The idea of working with NoSQL databases for storing large volumes of RDF-like data has been explored in the past. 17,20,[29][30][31] We have chosen Apache Cassandra over other software implementations of NoSQL databases for several reasons. None of these reasons is unique to Cassandra, but it is the only system that incorporates many of them.…”

Section: Data Materialisationmentioning

confidence: 99%

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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: System Architecturementioning

confidence: 99%

Section: Data Materialisationmentioning

confidence: 99%

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

Benítez‐Hidalgo,

Navas‐Delgado,

Roldán‐García

2023

Softw Pract Exp

Self Cite

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“…Mountasser et al 49 presented a semantic‐based big data integration framework that relies on large‐scale ontology matching and probabilistic‐logical‐based assessment strategies; they proved its efficiency in terms of accuracy, performance, and scalability. Moreover, Reyes‐Álvarez et al 50 presented a novel approach that enables the distributed storage of ontology‐based data by exploiting the inherent distribution of NoSQL database nodes. Finally, authors in Reference 19 presented SANSA, a big data engine for scalable processing of large‐scale RDF data using Spark and Flink.…”

Section: Related Workmentioning

confidence: 99%

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

Gomez,

Ruipérez‐Valiente,

García Clemente

2023

Softw Pract Exp

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During the last few years, there has been increasing attention paid to serious games (SGs), which are games used for non‐entertainment purposes. SGs offer the potential for more valid and reliable assessments compared to traditional methods such as paper‐and‐pencil tests. However, the incorporation of assessment features into SGs is still in its early stages, requiring specific design efforts for each game and adding significant time to the design of Game‐based Assessments (GBAs). In this research, we present a completely novel framework that aims to perform interoperable GBAs by: (a) integrating a common GBA ontology model to process RDF data; (b) developing in‐game metrics to infer useful information and assess learners; (c) integrating a service API to provide an easy way to interact with the framework. We then validate our approach through performance evaluation and two use cases, demonstrating its effectiveness in real‐world scenarios with large‐scale datasets. Our results show that the developed framework achieves excellent performance, replicating metrics from previous literature. We anticipate that our work will help alleviate current limitations in the field and facilitate the deployment of GBAs as a Service.

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“…Other properties of data placement, partition, replication, consistency, fault tolerance, as well as lack of global query language are fully explored for all NoSQL data models. NoSQL databases and in particular the column-based Cassandra database were used for storing linked data and OWL ontology [44]. Given that linked data storage necessitate scalable databases and distributed parallel algorithms, a schema was designed to store the ontology classification in the Cassandra database and help OWL scalability.…”

Section: Review Of the Selected Articlesmentioning

confidence: 99%

A Systematic Review of Data Models for the Big Data Problem

Mostajabi¹,

Safaei

Sahafi

2021

IEEE Access

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Nowadays, data are generated in a continuous streaming manner as the inputs of various applications. The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, ecommerce, social networks, and online communities. The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem. The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on. Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems' design, DB languages, etc.) rely on. So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data. It is also the essence in various areas of cloud migration, web-scale, and so forth. In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another. To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.

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Tool for materializing OWL ontologies in a column‐oriented database

Cited by 4 publications

References 28 publications

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

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

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

A Systematic Review of Data Models for the Big Data Problem

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