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Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges

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Title: Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges
Description: 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.
Authors: Freddy Lécué
Resource Type: eBook.
Subjects: Information visualization, Semantic computing, Artificial intelligence, Electronic books
Categories: COMPUTERS / Artificial Intelligence / General
Database: eBook Collection (EBSCOhost)
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PubType: eBook
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  Data: 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.
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      – Code: 006.3
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Information visualization
        Type: general
      – SubjectFull: Semantic computing
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Electronic books
        Type: general
    Titles:
      – TitleFull: Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges
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            NameFull: Freddy Lécué
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            – D: 01
              M: 01
              Type: published
              Y: 2020
            – D: 08
              M: 07
              Type: profile
              Y: 2020
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              Value: 9781643680804
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              Value: 9781643680811
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              Value: 00047
          Titles:
            – TitleFull: Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges
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