Dies ist eine kleine Auswahl empfohlener Buchtitel im Kontext von Knowledge Management, Semantic Web, Knowledge Graph, Taxonomy & Ontologie und Artificial Intelligence.
The KM Cookbook
Chris J. Collison et al., facet publishing, 2019.
Stories and strategies for organisations exploring Knowledge Management Standard ISO 30401
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 organisation should invest in the development of knowledge graphs, and most importantly, what recipes exist for developing and integrating them in an efficient, successful and sustainable way.
Graph Powered Machine Learning
Alessandro Negro, Manning Publications, 2020*.
Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including 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.
* Buch erscheint im Dezember 2020 – eine gekürzte Vorabversion „Essential Excerpts“ wurde im Juli 2020 veröffentlicht.
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 principal aim of this textbook is to provide the student with a comprehensive
introductory overview of ontology engineering. A secondary aim is to provide
hands-on experience in ontology development that illustrate the theory, such as
language features, automated reasoning, and top-down and bottom-up ontology
development with 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 amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide 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 gold&;but you still fail. The reason? Bad 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 for building high-quality and valuable semantic representations of data.