A Machine Learning Approach to Enhance Semantic Understanding in Knowledge Engineering


  • Bharat A. Shelke Department of Computer Science, S C S College, Omerga, Dist. Osmanabad, India
  • Vikas T. Humbe School of Technology, SRTM University, Sub Campus, Latur, India.
  • C. Namrata Mahender Department of Computer Science and IT, Dr. BAM University, Aurangabad, India.


Knowledge Engineering, Semantic Understanding, Machine Learning, Semantic Technology, Algorithms


Developing complex systems in environments of various domains need effective way to share, capture, and integrate knowledge of experts. “Modern Knowledge Engineering (KE)” systems meet this function to execute dignified knowledge with highly dedicated languages and environments. However, commitment of such environments to their application domain poses restrictions on incorporation of KE across the domain. Using Semantic Understanding (SU) can deliver a domain-neutral option to formalize knowledge and integrate data to reduce the effort needed for integration of knowledge of various domains in one representation.

This paper discusses machine learning approaches used to solve problems related to knowledge engineering. Semantic Understanding has seen a lot of improvements over the decades as per industrial demands and human needs. This new era is related to teaching machine to learn itself and understand the purpose and concept of its use with algorithms. This paper discusses semantic technology used in machine learning and its idea. It briefly discusses the important role of “machine learning and semantic technology”.


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How to Cite

Bharat A. Shelke, Vikas T. Humbe, & C. Namrata Mahender. (2024). A Machine Learning Approach to Enhance Semantic Understanding in Knowledge Engineering. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(3). Retrieved from http://mail.joaasr.com/index.php/joaasr/article/view/941