Book ChapterDOI
A Literature Survey: Semantic Technology Approach in Machine Learning
L. Rachana,S. Shridevi +1 more
- Vol. 688, pp 467-477
Reads0
Chats0
TLDR
This paper condenses the work of semantic technology approach in machine learning and its idea put forward and contains summarized view of the papers with a graph plotted on the analysis of paper throughout the decade; a table with summary of the related works and a review analysis.Abstract:
Semantic technology approach in machine learning is an emerging technique to solve the problems in the machine learning. Semantic technology has been the improvised from decades according to the human needs and industrial demands. This new era is all about teaching a machine to learn on its own and to make it understand the concept and the purpose for what it is used, using algorithms. This paper, condenses the work of semantic technology approach in machine learning and its idea put forward. The introduction details with brief explanation followed by description of the semantic technology and machine learning, important role. The literature survey contains summarized view of the papers with a graph plotted on the analysis of paper throughout the decade; a table with summary of the related works and concluded with review analysis.read more
Citations
More filters
Journal ArticleDOI
Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges
Wan Qing Yang,Hongyang Du,Zi Qin Liew,Wei Yang Bryan Lim,Zehui Xiong,Dusit Niyato,Xuefen Chi,Xuemin Sherman Shen,Chunyan Miao +8 more
TL;DR: In this paper , the authors provide a holistic review of SemCom, its applications in 6G networks, and the existing challenges and open issues with insights for further in-depth investigations.
Journal ArticleDOI
Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges
Wan Qing Yang,Hongyang Du,Zi Qin Liew,Wei Yang Bryan Lim,Zehui Xiong,Dusit Niyato,Xuefen Chi,Xuemin Sherman Shen,Chunyan Miao +8 more
TL;DR: In this paper , the authors highlight the motivations and compelling reasons of SemCom in 6G and outline the major 6G visions and key enabler techniques which lay the foundation of semantic communication, and present a SemCom-native 6G network architecture.
Journal ArticleDOI
Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges
TL;DR: In this paper , the authors provide a holistic review of SemCom, its applications in 6G networks, and the existing challenges and open issues with insights for further in-depth investigations.
Journal ArticleDOI
Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks
Shiva Raj Pokhrel,Jinho Choi +1 more
TL;DR: A new hashing-based semantic extraction approach to SC framework is developed, where the learning objective is to generate one time signatures (hash codes) using supervised learning for low latency, security and efficient management of the SC dynamics.
Proceedings ArticleDOI
Understanding Before Transmission (UBT): Hashing-based Semantic Communication Model
TL;DR: In this paper , a new hashing-based semantic communication, where the learning objective is the "semantic extraction" to produce optimal binary signatures (hash codes) by supervised learning, is proposed.
References
More filters
Book ChapterDOI
Towards Machine Learning on the Semantic Web
TL;DR: Existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of theSemantic Web's data structure are discussed.
Book ChapterDOI
Semi-automatic semantic annotation of images using machine learning techniques
Oge Marques,Nitish Barman +1 more
TL;DR: This work proposes a three-layer architecture, in which the bottom layer organizes visual information extracted from the raw image contents, which is mapped to semantically meaningful keywords in the middle layer, which are then connected to schemas and ontologies on the top layer.
Journal ArticleDOI
Ontology alignment using machine learning techniques
TL;DR: Experimental results demonstrate that the F-measure criterion improves up to 99% using feature selection and combination of AdaBoost and DT classifiers, which is highly comparable, and outperforms the previous reported F-measures.
Proceedings Article
Ontology based combined approach for sentiment classification
TL;DR: This paper proposed an ontology based combination approach to enhance the exits approaches of sentiment classifications and use supervised learning techniques for classifications.
Proceedings Article
Autonomous Classification of Knowledge into an Ontology
TL;DR: Three well-established machine learning techniques are compared and shown that they can be applied successfully to the problem of how to automa tically determine where to place new knowledge into an existing ontology.