[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121155-en":3,"doc-seo-121155-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121155,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Machine Learning Approach to Enhance Semantic Understanding in Knowledge Engineering","Developing complex systems across domains requires effective methods to share, capture, and integrate expert knowledge. Modern Knowledge Engineering systems use dedicated languages and environments, yet their domain commitment limits wider application. Semantic Understanding offers a domain-neutral way to formalize knowledge and integrate data, reducing effort across domains. The paper reviews machine learning approaches supporting semantic technology for better, more accurate knowledge engineering and meaning-focused processing.","A Machine Learning Approach to Enhance Semantic Understanding in Knowledge Engineering  \nBharatA Shelke 1 *†, Vikas T Humbe2 , C Namrata Mahender3  \n1 Department of Computer Science, S C S College, Dist, Osmanabad, Omerga, India  \n2 School of Technology, SRTM University, Sub Campus, Latur, India  \n3 Department of Computer Science and IT, Dr, BAM University, Aurangabad, India  \nAbstract  \nDeveloping 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”.  \nKeywords: Semantic Understanding, Machine Learning, Semantic Technology, Algorithms  \n1 Introduction  \nSemantic understanding is a machine learning approach which is merely an icing on a cake. It provides an interface to model their knowledge and review the system's knowledge. The semantic systems are also excellent in provability (14) . The semantic system is well-structured to get logical proofs in an easy-to-understand term and back up the answers generated by the  \n* Corresponding author.  \n†E-mail: [bashelke@gmail.com](bashelke@gmail.com)  \ncomputer. One can have high level of confidence in the answers produced by the system. There are definitely the flaws in the system or loopholes in the ontology. But they are usually easier to identify (1) .  \nSemantic technology has given a lot of opportunity for human-machine interaction. The datasets are processed and categorized along with finding relationship of the data. This paper discusses a machine learning approach to enhance semantic understanding in knowledge engineering and solve problems for more accurate values (2) .  \n“Semantic understanding refers to the meaning of words. It uses AI for simulation of processing and understanding the information of language used by people. Semantic software reads and grasps words and language. This method is technically based on different research levels, i.e., analyzing grammar and morphology. It is easy to understand words' meanings and language on daily basis. It is quite challenging to pass the same skills to a system. Learning takes some time for the machine. There is no magic formula or shortcut (15) . It is not easy to learn a language as it takes work and time for automated process(18). Machine learning is an AI application which enables machines to learn from experience and skills automatically without proper programming. Machine learning is based on developing software for processing and using data for their own (3) .  \nThe process of learning starts from observations, direct experience, evidence, and preparation on the basis of examples. It is helpful to interpret patterns of knowledge and make better decisions in future. The key here is to make smart machines without human interaction to change and understand the behavior (16) :Some other machine learning approaches are usually known as non-supervised or supervised machine learning models (4) .  \nSupervised machine learning techniques can use specific occurrences for prediction of futu","cbCaia3H2yhLvHOz","https://ap.wps.com/l/cbCaia3H2yhLvHOz","pdf",1461129,1,17,"English","en",105,"# Introduction\n## Semantic Understanding in Knowledge Engineering\n## Machine Learning Foundations\n## Supervised Learning\n## Unsupervised and Semi-Supervised Learning","[{\"question\":\"Why do knowledge engineering systems face limitations across domains?\",\"answer\":\"Modern Knowledge Engineering systems are tied to specific application domains due to their dedicated languages and environments, which restricts broader incorporation of knowledge engineering.\"},{\"question\":\"How does Semantic Understanding support domain-neutral knowledge integration?\",\"answer\":\"Semantic Understanding formalizes knowledge in a domain-neutral way and integrates data, reducing the effort needed to represent knowledge from different domains together.\"},{\"question\":\"What role do supervised and unsupervised machine learning play in semantic systems?\",\"answer\":\"Supervised learning uses labeled occurrences to predict future events and evaluate performance, while unsupervised learning analyzes unlabeled data to extract information and identify underlying patterns and features.\"}]","A Machine Learning Approach to Enhance Semantic Understanding in Knowledge Engineering | 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do knowledge engineering systems face limitations across domains?","Question",{"text":76,"@type":77},"Modern Knowledge Engineering systems are tied to specific application domains due to their dedicated languages and environments, which restricts broader incorporation of knowledge engineering.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does Semantic Understanding support domain-neutral knowledge integration?",{"text":81,"@type":77},"Semantic Understanding formalizes knowledge in a domain-neutral way and integrates data, reducing the effort needed to represent knowledge from different domains together.",{"name":83,"@type":74,"acceptedAnswer":84},"What role do supervised and unsupervised machine learning play in semantic systems?",{"text":85,"@type":77},"Supervised learning uses labeled occurrences to predict future events and evaluate performance, while unsupervised learning analyzes unlabeled data to extract information and identify underlying patterns and 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