Tagging of Multimedia Contents on the Web 3.0 using Semantic Artificial Intelligence: A Systematic Literature Review | Zendy

M Hemashree | Zendy; Shreya Banerjee | Zendy; R Rashmi | Zendy

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Tagging of Multimedia Contents on the Web 3.0 using Semantic Artificial Intelligence: A Systematic Literature Review

Author(s) -

M Hemashree,

Shreya Banerjee,

R Rashmi

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3597801

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

With the World Wide Web transitioning from Web 2.0 to the more intelligent and reliable Web 3.0, the importance of effective tagging methods for multimedia data has become increasingly important. In contrast to text documents, where keyword extraction is quite straightforward, tagging multimedia objects such as images, audio, video, and infographics is computationally expensive and highly demanding. The current review paper thoroughly discusses the prevalent methods for tagging multimedia data, analyzes their efficiency in semantic intelligence frameworks, and their usage in general and domain-specific applications. This paper concentrates specifically on semantic-focused artificial intelligence (AI) and factual knowledge as major enablers towards enhancing the scalability and accuracy of multimedia tagging in the highly coherent Web 3.0 environment. The survey points out the implications of knowledge graphs (KG), ontologies, and semantic reasoning (SR) in boosting semantic comprehension, as well as outlines how these technologies tackle issues such as serendipity, overspecialization, and the cold start problem. Through the synthesis of insights from current work, this paper points out gaps in prevailing strategies and outlines paths towards the construction of computationally feasible and trustworthy semantic models for multimedia tagging in the future. The survey seeks to offer a general perspective on the state-of-the-art in multimedia tagging and contribute to the development of the Semantic Web as a web of interconnected and contextually informative data.

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