Knowledge Graphs and Big Data Processing

1. Auflage 2020
ISBN: 978-3-030-53198-0
Verlag: Springer International Publishing

Buch, Englisch, Band 12072, 209 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 347 g

Reihe: Lecture Notes in Computer Science

This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others.

The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.

This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

Zielgruppe

Research

Autoren/Hrsg.

Janev, Valentina

Sallinger, Emanuel

Graux, Damien

Jabeen, Hajira

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data

Inhaltsverzeichnis

Foundations.- Chapter 1. Ecosystem of Big Data.- Chapter 2. Knowledge Graphs: The Layered Perspective.- Chapter 3. Big Data Outlook, Tools, and Architectures.- Architecture.- Chapter 4. Creation of Knowledge Graphs.- Chapter 5. Federated Query Processing.- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight.- Methods and Solutions.- Chapter 7. Scalable Knowledge Graph Processing using SANSA.- Chapter 8. Context-Based Entity Matching for Big Data.- Applications.- Chapter 9. Survey on Big Data Applications.- Chapter 10. Case Study from the Energy Domain.