Knowledge-graph-based explainable AI : A systematic review
© The Author(s) 2022.
| Veröffentlicht in: | Journal of information science. - 1998. - 50(2024), 4 vom: 13. Aug., Seite 1019-1029 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Journal of information science |
| Schlagworte: | Journal Article Knowledge graph artificial intelligence explainable AI systematic review |
| Zusammenfassung: | © The Author(s) 2022. 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 |
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| Beschreibung: | Date Revised 14.08.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0165-5515 |
| DOI: | 10.1177/01655515221112844 |