[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117424-en":3,"doc-seo-117424-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117424,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Ontology Driven Autonomous Machine Learning Framework - Research Paper","Artificial intelligence capable of recognizing, learning, inferring, and responding to external stimuli has become a major research focus, especially as machine learning tools are increasingly adopted across industries. This paper addresses the standardization of machine learning concepts by examining autonomous machine learning and ontology construction. It proposes a problem-solving process where common autonomous machine learning steps are modeled as tasks, forming a task-ontology workflow that groups UML activities and enables self-evolving model creation using rule-based adaptation.","International Journal of Advanced Research in Education and TechnologY (IJARETY)  \nVolume 12, Issue 2 , March-April 2025  \nImpact Factor: 8 . 152  \n[www.ijarety.in](www.ijarety.in)  editor.ijarety@gmail.com  \nOntology Driven Autonomous Machine Learning Framework  \nSindhu Priyanka Chadalavada1, Kotharu Lalitha Lakshmi2, Gajulavarthi Seyonu Grace3, Koyyuru Abhiram4 , Agniparthi Vijaya Kumar5  \nAssociate Professor, Department ofCSE, Eluru College of Engineering & Technology, Eluru, India 1  \nB. Tech Student, Department ofCSE, Eluru College of Engineering & Technology, Eluru, India2,3,4,5  \nABSTRACT: Artificial intelligence technology that recognizes, learns, infers, and responds to external stimuli has recently attracted a lot of research interest. Information in a variety of domains by fusing big data, machine learning algorithms, and computing technologies. Nowadays, practically every industry uses artificial intelligence technology, and a large number of machine learning specialists are attempting to standardize and integrate different machine learning tools so that non-experts can use them with ease in their field. In order to standardize the concepts of machine learning, the researchers are also investigating autonomous machine learning and ontology construction. In this paper, we present a problem solving process and categorize common steps in autonomous machine learning problem solving as tasks. We suggest a way to model self-learning machines using a workflow of machine learning tasks. Our proposed machine learning model based on task ontology, sets up a way to group UML activities by task. It will also create and grow machine learning models on its own using rules to change common parts and structures (how elements connect and work together).  \nKEYWORDS: Machine Learning, Artificial Intelligence, TensorFlow and Ontology.  \nI. INTRODUCTION  \nArtificial intelligence technology has become one of the most essential tools in research and business context recently. Most of the machine learning frameworks are open-source, so the entry of barriers into machine learning are lowered. The typical machine learning frameworks include Tensorflow, Keras[, Caffe, Scikit-learn, and Theano implemented in programming languages such as Python, Java, and R. In this respect, many machine learning experts are working on integrating and standardizing various tools so that machine learning nonexperts can easily apply them to their domains. On the other hand, an autonomous machine learning is still in its infancy, and some techniques provide the ability to reduce the unnecessary tasks that are progressively refined to prepare the model and improve its accuracy. The tools of autonomous machine learning provide an optimal algorithm for machine learning tasks and functions to determine the hyper-parameter setting through self-analysis. The typical tools include Auto sklearn, Auto-Weka, H2o Driverless AI and Google's Auto ML. In this paper, we describe a typical problem solving process for the machine learning as tasks, present their procedure, and propose the modeling method of an autonomous machine learning for using task execution processes. The modeling method of autonomous machine learning based on the task ontology define a structure based grouping method of the UML(Unified Modeling Language) activities and implement a function to automatically generate models based on common elements and structures. The purpose of the proposed autonomous machine learning model is to model autonomous machine learning by reusing existing resources and producing new knowledge through relearning it.  \nTask Ontologies: Ontology is defined in various fields depending on the field of applications. In the field of artificial intelligence, it is an explicit and formal specification of how objects and concepts described in the field of interest. In the Semantic Web, an ontology plays a very important role in processing, sharing, and reusing the knowledge for exchanging ","cbCaikPc04eFHxPD","https://ap.wps.com/l/cbCaikPc04eFHxPD","pdf",1430499,1,9,"English","en",105,"# Abstract\n# Introduction\n## Task Ontologies\n## Machine Learning (ML) Schema and Related Ontologies\n## Machine Learning Ontologies (ML Schema, MEX, PROV, and OntoMD)\n## Ontological Modeling for Autonomous Execution","[{\"question\":\"What problem does the paper address in autonomous machine learning?\",\"answer\":\"It focuses on standardizing autonomous machine learning concepts by modeling common problem-solving steps as tasks and organizing them into a reusable workflow.\"},{\"question\":\"How does the proposed framework use task ontologies?\",\"answer\":\"It defines a task ontology structure to group UML activities and automatically generate machine learning models based on shared elements and structures.\"},{\"question\":\"Which technologies or tools are referenced for machine learning and ontology-related work?\",\"answer\":\"The paper mentions TensorFlow and several autonomous ML tools such as Auto-sklearn, Auto-Weka, H2o Driverless AI, and Google’s Auto ML, along with ontology frameworks like ML Schema and PROV.\"}]","Ontology Driven Autonomous Machine Learning Framework - 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