This is a small selection of recommended books on topics such as knowledge management, the Semantic Web, knowledge graphs, taxonomy and ontology, and artificial intelligence.
The KM Cookbook
, Chris J. Collison et al., Facet Publishing, 2019.
Stories and strategies for organizations exploring the ISO 30401 Knowledge Management Standard
Knowledge Management Matters
, John & JoAnn Girard, Sagology, 2018.
Words of Wisdom from Leading Practitioners
The Knowledge Graph Cookbook
, Andreas Blumauer, Helmut Nagy, 2020.
The Knowledge Graph Cookbook explains why your organization should invest in the development of knowledge graphs, and most importantly, what methods are available for developing and integrating them in an efficient, successful, and sustainable way.
Graph-Powered Machine Learning
, Alessandro Negro, Manning Publications, 2020*.
Graph-Powered Machine Learningintroduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll gain an in-depth understanding of techniques such as data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls.
* The book will be published in December 2020—an abridged preview version, “Essential Excerpts,” was released in July 2020.
Ontology Engineering
, Elisa F. Kendall et al., Morgan & Claypool Publishers, 2019.
Synthesis Lectures on the Semantic Web: Theory and Technology. Ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded.
An Introduction to Ontology Engineering
C. Maria Keet et al., CC BY 4.0, 2020.
The primary goal of this textbook is to provide students with a comprehensive
introductory overview of ontology engineering. A secondary goal is to provide
hands-on experience in ontology development that illustrates the theory, such as
language features, automated reasoning, and top-down and bottom-up ontology
development using various methods and methodologies.
Semantic Web for the Working Ontologist: Effective Modeling for Linked Data, RDFs, and OWL
, Dean Allemang et al., ACM Books, 2020.
Enterprises have made remarkable strides by leveraging data about their businesses to generate predictions and gain insights into their customers, markets, and products. But as the business world becomes increasingly interconnected and global, enterprise data is no longer a monolith; it is merely a part of a vast web of data. Managing data on a global scale is a key capability for any business today.
Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas
Panos Alexopoulos, O’Reilly, 2020
What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business value—but you still fail. The reason? Poor data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome when building high-quality and valuable semantic representations of data.