Data Science with Semantic Technologies

Theory, Practice and Application

Archana Patel, Narayan C. Debnath, Bharat Bhusan

• Publisher: Wiley-Scrivener
• Year: 2022
• Print ISBN: 9781119864981
• eBook ISBN: 9781119865315
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

DATA SCIENCE WITH SEMANTIC TECHNOLOGIES

This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field.

To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization.

Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to 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 knowledge data science? How does knowledge data science relate to other domains? 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 researchers?

Audience

Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• 1 A Brief Introduction and Importance of Data Science
• 2 Exploration of Tools for Data Science
• 3 Data Modeling as Emerging Problems of Data Science
• 4 Data Management as Emerging Problems of Data Science
• 5 Role of Data Science in Healthcare
• 6 Partitioned Binary Search Trees (P(h)-BST): A Data Structure for Computer RAM
• 7 Security Ontologies: An Investigation of Pitfall Rate
• 8 IoT-Based Fully-Automated Fire Control System
• 9 Phrase Level-Based Sentiment Analysis Using Paired Inverted Index and Fuzzy Rule
• 10 Semantic Technology Pillars: The Story So Far
• 11 Evaluating Richness of Security Ontologies for Semantic Web
• 12 Health Data Science and Semantic Technologies
• 13 Hybrid Mixed Integer Optimization Method for Document Clustering Based on Semantic Data Matrix
• 14 Role of Knowledge Data Science During COVID-19 Pandemic
• 15 Semantic Data Science in the COVID-19 Pandemic
• Index
• Wiley End User License Agreement