Knowledge Graphs in the Big Data Era: Navigating the Confluence of Distribution, Visualization, and Advanced Computational Models

Dear Colleagues,

In the current landscape of technological advancement, the role of knowledge graphs (KGs) in the big data era has become increasingly significant, as they can reshape various aspects of our daily and professional lives. The expanding volume of data, characterised by its variety, velocity, and complexity, presents both opportunities and challenges in data management and analysis. This Special Issue, titled "Knowledge Graphs in the Big Data Era: Navigating the Confluence of Distribution, Visualisation, and Advanced Computational Models", aims to delve into the innovative approaches and solutions for harnessing the power of KGs amid the challenges posed by big data.

The integration of KGs with big data analytics is not without its difficulties. The vast amount and heterogeneity of data require advanced, scalable, and distributed frameworks to effectively manage and interpret this information. Furthermore, the visualization and user interface design for KGs in big data scenarios demand innovative approaches to facilitate user interaction and data comprehension. The combination of these factors calls for a nuanced understanding of how KGs can be optimised and utilised in a big data context.

Moreover, the intersection of KGs with emerging computational models, such as large language models (LLMs), adds another layer of complexity and potential for growth. These models offer new ways to process, analyse, and draw insights from extensive datasets, making the study of their integration with KGs a promising research area.

This Special Issue seeks contributions that explore the multifaceted dynamics of KGs in big data era. We request papers that address the challenges involved in distributed KGs, innovative visualization techniques, the impacts of LLMs on KGs in big data, and other relevant topics. Our goal is to compile a comprehensive collection of studies that not only addresses technical aspects but also considers the broader implications of KGs in this rapidly evolving digital landscape. 

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following subjects:

• Distributed knowledge graphs in big data;
• Visualisation UI for KGs in big data;
• Integration of LLMs with KGs in big data;
• Semantic web and ontology engineering in big data;
• Machine learning and AI in enhancing KGs;
• Natural language processing (NLP) for KGs in big data;
• Role of KGs in predictive analytics;
• Graph databases and big data;
• KGs for IoT and sensor data;
• Ethical and societal implications of KGs in big data;
• Interoperability of KGs across diverse data sources;
• Domain-specific applications of knowledge graphs in big data. 

We look forward to receiving your contributions. 

Dr. Amna Dridi
Dr. Edlira Kalemi Vakaj
Prof. Dr. Mohamed Medhat Gaber
Guest Editors

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• knowledge graphs
• big data analytics
• distributed computing
• data visualisation
• semantic web technologies
• machine learning integration
• natural language processing
• graph databases
• domain-specific applications