Название: Data Science with Semantic Technologies: New Trends and Future DevelopmentsАвтор: Archana Patel, Narayan C. Debnath
Издательство: CRC Press
Год: 2023
Страниц: 315
Язык: английский
Формат: pdf (true)
Размер: 14.1 MB
As data is an important asset for any organization, it is essential to apply semantic technologies in Data Science to fulfill the need of any organization. This first volume of a two-volume handbook set provides a roadmap for new trends and future developments of Data Science with semantic technologies.
Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as: Can semantic technologies facilitate Data Science? Which type of Data Science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is the role of semantic technologies in Data Science? What is the current progress and future of Data Science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for Data Science?
Natural Language Processing (NLP) and Machine Learning (ML) are major subfields in Artificial Intelligence (AI) that have recently received wider appeal and application in several fields. ML and NLP are critical components in converting an artificial agent into an AI-powered agent. As a result of advances in NLP, AI systems can process information more intuitively from the environment. Using Machine Learning techniques, an AI system may evaluate received data and make better predictions for its actions.
Learning from examples and previous experiences is the capability of Machine Learning. Genetic algorithms (GA) carry out a predetermined set of operations in accordance with their programming, and therefore are unable to address unforeseen issues. The majority of problems encountered in the real world have a large number of unknown variables, making standard algorithms ineffective. Here is where Machine Learning comes into play; it is significantly more capable to handle such unknown issues with the help of previous examples.
This volume can serve as an important guide toward applications of Data Science with semantic technologies for the upcoming generation and, thus, it is a unique resource for scholars, researchers, professionals, and practitioners in this field.
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