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Knowledge-graph-based explainable AI: A systematic review
Cape Breton University, Sydney, Canada.ORCID iD: 0000-0002-9557-0043
Halmstad University, School of Information Technology.ORCID iD: 0000-0003-2006-6229
2024 (English)In: Journal of information science, ISSN 0165-5515, E-ISSN 1741-6485, Vol. 50, no 4, p. 1019-1029Article in journal (Refereed) Published
Abstract [en]

In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their relationships. The construction of large KGs can enable the integration of heterogeneous information sources and help Artificial Intelligence (AI) systems be more explainable and interpretable. This systematic review examines a selection of recent publications to understand how KGs are currently being used in eXplainable AI systems. To achieve this goal, we design a framework and divide the use of KGs into four categories: extracting features, extracting relationships, constructing KGs, and KG reasoning. We also identify where KGs are mostly used in eXplainable AI systems (pre-model, in-model, and post-model) according to the aforementioned categories. Based on our analysis, KGs have been mainly used in pre-model XAI for feature and relation extraction. They were also utilised for inference and reasoning in post-model XAI. We found several studies that leveraged KGs to explain the XAI models in the healthcare domain. © The Author(s) 2022.

Place, publisher, year, edition, pages
London: Sage Publications, 2024. Vol. 50, no 4, p. 1019-1029
Keywords [en]
artificial intelligence, explainable AI, Knowledge graph, systematic review
National Category
Computer Sciences
Research subject
Health Innovation, IDC
Identifiers
URN: urn:nbn:se:hh:diva-48791DOI: 10.1177/01655515221112844ISI: 000857784800001PubMedID: 39135903Scopus ID: 2-s2.0-85138794892OAI: oai:DiVA.org:hh-48791DiVA, id: diva2:1718356
Note

Funding: The work conducted in the study has been partially funded by NSERC (Natural Sciences and Engineering Research Council) Discovery Grant (RGPIN-2020-05869).

Available from: 2022-12-12 Created: 2022-12-12 Last updated: 2024-08-15Bibliographically approved

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Rajabi, EnayatEtminani, Kobra

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