Knowledge Graphs for eXplainable Artificial Intelligence | Foundations, Applications and Challenges

Ilaria Tiddi (u. a.)

ISBN 10: 1643680803 / ISBN 13: 9781643680804

Published by IOS Press, 2020

About this Item

Knowledge Graphs for eXplainable Artificial Intelligence | Foundations, Applications and Challenges | Ilaria Tiddi (u. a.) | Taschenbuch | Englisch | 2020 | IOS Press | EAN 9781643680804 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 127163692

Bibliographic Details

• Title: Knowledge Graphs for eXplainable Artificial Intelligence | Foundations, Applications and Challenges
• Publisher: IOS Press
• Publication Date: 2020
• Language: English
• ISBN 10: 1643680803
• ISBN 13: 9781643680804
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 240W x 160H x 20D millimeters 240 millimeters width by 160 millimeters height by 20 millimeters depth
• Weight: 741 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.