ArticleJournal of information science2024
Knowledge-graph-based explainable AI: A systematic review.
Article in Journal of information science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Explainable/interpretable artificial intelligence and knowledge graph neural network for synthetic lethality prediction: overview, challenges, and future directions.Briefings in bioinformatics · 2026Review
- DentalEdu-AI graph: mapping evidence-grounded and knowledge-structured AI for dental education, assessment, and accuracy.The Saudi dental journal · 2026Article
- Reimagining plant science training in the era of generative artificial intelligence: a global perspective.The Plant cell · 2026Review
- BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.Bioinformatics (Oxford, England) · 2026Article
- Perspective on Better Access to Data and Data Integration in Pharmacovigilance: Information from a Focus Group.Drug safety · 2026Article
- Knowledge graph for multi-source data: optimizing the systematic layout and application of reference materials for edible agricultural products.Analytical and bioanalytical chemistry · 2026Review
- Explainable AI augmented retailer segmentation using knowledge graph.Scientific reports · 2026Article
- An evidence-weighted knowledge framework with path-aware reasoning for personalized lifestyle management in chronic disease.Frontiers in public health · 2026Article
- How generative AI shapes designers' cultural heritage revitalization intention: process and content cues in AI-assisted cultural heritage design.Frontiers in psychology · 2026Article
- Systematic discovery of disease-modifying targets by prediction from knowledge graph-based AI model and experimental validation: Parkinson's disease case.Computational and structural biotechnology journal · 2026Article
- Transforming customer experience in social robotics through explainable and interpretable artificial intelligence over a decade.Frontiers in robotics and AI · 2026Article
- ZebraMap: A Multimodal Rare Disease Knowledge Map with Automated Data Aggregation & LLM-Enriched Information Extraction Pipeline.Diagnostics (Basel, Switzerland) · 2025Article
- Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency.Bioengineering (Basel, Switzerland) · 2025Review
- BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.bioRxiv : the preprint server for biology · 2025Article
- Interpretable AI-assisted diagnosis of papillary thyroid cancer cytopathology using graph neural networks and knowledge graphs.Scientific reports · 2025Article
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
- Core reference ontology for individualized exercise prescription.Scientific data · 2024Article
- Augmented non-hallucinating large language models as medical information curators.NPJ digital medicine · 2024Article
- Knowledge graph augmentation: consistency, immutability, reliability, and context.PeerJ. Computer science · 2023Article
- Trustworthy AI in Telehealth: Navigating Challenges, Ethical Considerations, and Future Opportunities for Equitable Healthcare Delivery.Healthcare technology lettersReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.