A Unified Access to Heterogeneous Big Data through Ontology-Based Semantic Integration

Fathy, Naglaa; Gad, Walaa; Badr, Nagwa L.

doi:10.1109/icicis46948.2019.9014856

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…52 Finally, integrating GBAs in different environments generates a diverse range of data from various sources, 37 emphasizing the need for a unified and secure way to access GBA data. Therefore, we identified the following requirements: Requirement 1-Semantic layer between the event data and a common knowledge model: As previous literature reported, 11,53,54 there are heterogeneity issues with the collected data. In fact, most previous studies did not report any specific format for the collected data.…”

Section: Framework Requirementsmentioning

confidence: 99%

“…Ontologies capture domain-specific knowledge and offer an explicit common conceptualization. 11 Nowadays, ontologies are used in various application areas involving artificial intelligence, natural language processing, data integration, and knowledge management. 12 Using ontologies to define a standard knowledge model in the context of Game-based Assessments (GBAs) could alleviate the time-consuming and costly step of creating GBAs.…”

Section: Introductionmentioning

confidence: 99%

“…Semantic web technologies, and particularly ontologies, can address these heterogeneity problems. Ontologies capture domain‐specific knowledge and offer an explicit common conceptualization 11 . Nowadays, ontologies are used in various application areas involving artificial intelligence, natural language processing, data integration, and knowledge management 12 .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Framework Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…The approach created by Fathy, Gad and Badr (2019) aims to translate queries sent in SPARQL Protocol and RDF Query Language (SPARQL) to queries in NoSQL graphs. In the first step, an extension of RDB-to-RDF mapping language (xR2RML) mapping file that includes the triple maps of the semantic model in question for the NoSQL graph is used as an input, creating an intermediate representation between SPARQL and NoSQL graph queries.…”

Section: Resolution Of Heterogeneity Using Ontologies and Technologies Of The Web Semanticsmentioning

confidence: 99%

“…compares the semantic integration approaches based on Ontologies and Technologies of the Web Semantics.It can be mentioned as limitations presented in the work byFathy, Gad and Badr (2019), the data sources that define the same domain are often incomplete and may be incompatible with each other, making the mapping need adjustments. For this problemNadal et al (2019) created a semi-automatic algorithm for updating the ontology; if a data source is updated, the algorithm updates the ontology.The work that is still needed is to improve the semantics of the data sets, even though some industrial ontologies already exist.Based on the analysis of the articles, possible research directions are suggested:• Automation the comparison of ontologies in realtime.• Creation of more complete industrial ontologies.Resolution of semantic heterogeneity through data analysis Pelekis, Theodoridis and Janssens (2014) present a unified structure for management and analysis of data objects that include both trajectories and their semantic counterpart.…”

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confidence: 99%

Review and comparison of works on heterogeneous data and semantic analysis in Big Data

et al. 2021

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In integration approaches, heterogeneity is one of the main challenging factors on the task of providing integration among different data sources, whose solution lies in the search for equality among them. This work describes the state of the art and theoretical foundation involved in the structural and semantic analysis of heterogeneous data and information. The work aims to review methods and techniques used in data integration in Big Data, considering data heterogeneity, reviewing techniques that use the concepts of Semantic Web, Cloud Computing, Data Analysis, Big Data, Data Warehouse and other technologies to solve the problem of data heterogeneity. The research was divided into three stages. In the first stage, articles were selected from digital libraries according to their titles and keywords. In the second stage, the papers went through a second filter based on their summary, and, besides that, duplicate articles were also removed. The works’ introduction and conclusion were analyzed in the third stage to select the articles belonging to this systematic review. Throughout the study, articles were analyzed, compared and categorized. At the end of each section, the interrelationships and possible areas for future work were shown.

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Ontology Augmented Data Lake System for Policy Support

Kulkarni¹,

Bassin²,

Parasa³

et al. 2023

Lecture Notes in Computer Science

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A Unified Access to Heterogeneous Big Data through Ontology-Based Semantic Integration

Cited by 8 publications

References 17 publications

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

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

Review and comparison of works on heterogeneous data and semantic analysis in Big Data

Ontology Augmented Data Lake System for Policy Support

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