Knowledge graphs as tools for explainable machine learning: A survey

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, bu...

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Vydáno v:Artificial intelligence Ročník 302; s. 103627
Hlavní autoři: Tiddi, Ilaria, Schlobach, Stefan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.01.2022
Elsevier Science Ltd
Témata:
Domains
Explainable AI
Explainable artificial intelligence
Explainable systems
Explanations
Graphs
Hybrid systems
Knowledge graphs
Knowledge representation
Literature reviews
Machine learning
Neuro-symbolic integration
Subsymbolic AI
Symbolic AI
Symbolic AI
Explainable AI
Explanations
Knowledge graphs
Explainable systems
Neuro-symbolic integration
Subsymbolic AI
ISSN:0004-3702, 1872-7921
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Shrnutí:This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, but hardly scrutable and interpretable decisions. An increasing interest has also been shown in the integration of Knowledge Representation techniques in Machine Learning applications, mostly motivated by the complementary strengths and weaknesses that could lead to a new generation of hybrid intelligent systems. Following this idea, we hypothesise that knowledge graphs, which naturally provide domain background knowledge in a machine-readable format, could be integrated in Explainable Machine Learning approaches to help them provide more meaningful, insightful and trustworthy explanations. Using a systematic literature review methodology we designed an analytical framework to explore the current landscape of Explainable Machine Learning. We focus particularly on the integration with structured knowledge at large scale, and use our framework to analyse a variety of Machine Learning domains, identifying the main characteristics of such knowledge-based, explainable systems from different perspectives. We then summarise the strengths of such hybrid systems, such as improved understandability, reactivity, and accuracy, as well as their limitations, e.g. in handling noise or extracting knowledge efficiently. We conclude by discussing a list of open challenges left for future research.
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ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2021.103627