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ABOUT THE BOOK

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics.

Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source.

This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

TABLE OF CONTENTS

Vol 1: 1. What is Data Science. 2. Big Data and its Future. 3. Smart Warehouse Testbed: From Conceptual Framework to a Real Project. 4. Empirical Study on Sentiment Analysis. 5. Forecasting on Covid-19 data using ARIMAX model. 6. ML-Based Method for Detecting and Alerting to Cyber Attacks. 7. Machine Learning in Natural Language Processing- Emerging Trends and Challenges. 8. Machine Learning and Future Directions. 9. Towards a Web Standard for Neurosymbolic Integration and Knowledge Representation Using Model Cards. 10. Semantic Web Technologies. 11. Data Science with Semantic Technologies. 12. Ontological Perspective in Cancer Care system. 13. Interoperability Frameworks: Data Fabric and Data Mesh Architectures. 14. Recommender System for E-commerce: How Ontologies Support Recommendations.

Vol 2: 1. Machine Learning Meets the Semantic Web. 2. Knowledge Graphs: Connecting Information over the Semantic Web. 3. Latest Trends In Language Processing To Make Semantic Search More Semantic. 4. Semantic Information Retrieval Models. 5. Enterprise Application Development Using Semantic Web Languages for Semantic Data Modeling. 6. The Metadata Management for Semantic Ontology in Big Data. 7. Role of Knowledge Data Science During Covid-19. 8. Semantic Technologies in Next Era of Industry Revolution: Industry 4.0. 9. Semantic Interoperability Framework and its Application in Agriculture. 10. Design and Implementation of a Short Circuit Detection System using Data Stream and Semantic Web techniques. 11. Semantic-Based Access Control for Data Resources. 12. Ontological Engineering: Research Directions and A Real-life Applications. 13. Expert Systems in AI - Components, Applications, and Characteristics focusing on Chatbot.