MetaKG: From Data Collection to User Interaction

The emergence of the Metaverse as a new platform for human interaction could revolutionize several sectors. This paper provides a new “Metaverse Knowledge Graph” (MateKG) framework to enhance user interaction, data management, and knowledge representation in the virtual environment. The building and interacting MetaKGs is proposed to allow for efficient and successful data management, organization, and exploitation. The novel framework comprises several layers: infrastructure, data gathering, Knowledge Graph (KG), user interaction, evaluation and enhancement, and applications. We discuss several approaches, storage options, ontology designs for KG creation, and data-collecting sources. We look at customization strategies, accessibility concerns for the user interaction layer, and user interface design. Furthermore, we showcase case studies and MetaKG applications in e-commerce, smart cities, healthcare, and entertainment. We provide a starting point for creating reliable and efficient MetaKGs that serve a range of applications and domains.

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Publication Details

Published
2025-04-13
DOI
https://doi.org/10.1109/iccit63348.2025.10989297
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

MetaKG: From Data Collection to User Interaction

Saeed Hamood Alsamhi, Mohan Timilsina, Edward Curry, Ammar Hawbani
Advanced Graph Neural Networks
article

MetaKG: From Data Collection to User Interaction

Saeed Hamood Alsamhi, Mohan Timilsina, Edward Curry, Ammar Hawbani
article en

Abstract

The emergence of the Metaverse as a new platform for human interaction could revolutionize several sectors. This paper provides a new “Metaverse Knowledge Graph” (MateKG) framework to enhance user interaction, data management, and knowledge representation in the virtual environment. The building and interacting MetaKGs is proposed to allow for efficient and successful data management, organization, and exploitation. The novel framework comprises several layers: infrastructure, data gathering, Knowledge Graph (KG), user interaction, evaluation and enhancement, and applications. We discuss several approaches, storage options, ontology designs for KG creation, and data-collecting sources. We look at customization strategies, accessibility concerns for the user interaction layer, and user interface design. Furthermore, we showcase case studies and MetaKG applications in e-commerce, smart cities, healthcare, and entertainment. We provide a starting point for creating reliable and efficient MetaKGs that serve a range of applications and domains.

Shenyang Aerospace University (CN), Ollscoil na Gaillimhe – University of Galway (IE)
Science Foundation Ireland
Openalex Percentile: Top 95%
Advanced Graph Neural Networks
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