https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg/Head https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg/assertion https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg/provenance https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg/pubinfo https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RApF5Zwx6E4cx1IrKSpgAkA6IqQsX7zW4HsCCiO6gqadg/assertion https://w3id.org/spaces/iswc/2026/xai-kg http://purl.org/dc/terms/description In recent years, the synergy between eXplainable AI (XAI) and Knowledge Graphs (KGs) has gained momentum as an essential approach for achieving trust and improving understanding in AI systems. KGs provide structured and interconnected representations of (domain-specific) knowledge, XAI aims to provide insight for predicted results and/or to clarify how numeric machine/deep learning models function internally. By leveraging KGs within XAI, research can enhance the understanding and interpretability of AI models, making it easier for users to trust AI predictions. KGs can offer insights into the relationships and logic that AI systems use to reach conclusions. This can be particularly valuable in fields requiring high levels of transparency, such as healthcare, finance, and law, where understanding the rationale behind AI predictions and actions is crucial. Conversely, XAI can assist in constructing and refining KGs, helping to identify which aspects of KGs contribute most to accurate, reliable suggestions/conclusions. This workshop aims to bring together researchers, practitioners, and industry experts to explore challenges and opportunities of combining XAI with KGs, discussing novel methodologies, applications, and case studies. 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