https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo/Head https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo/assertion https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo/provenance https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo/pubinfo https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RA0vrf7LNKkIs3ccb_KfyeHjFLcYOxSEHsWf5wEoqzDvo/assertion https://w3id.org/spaces/iswc/2026/kg-nesy http://purl.org/dc/terms/description The Knowledge Graphs and Neurosymbolic AI Systems (KG-NeSy) workshop aims to bring together researchers and practitioners who are actively working on combining KGs and Neurosymbolic AI, exploring how these two research areas can mutually enhance one another. In particular, the workshop addresses the following complementary strands of research: (1) KGs within Neurosymbolic (NeSy) AI systems, either as (a) deeply integrated components supporting hybrid reasoning, as (b) auxiliary structures improving explainability, interpretability, robustness, generalization, and transferability of deep learning models, or as (c) structured inputs enabling neurosymbolic prediction and inference. (2) NeSy AI to support KG engineering. Neurosymbolic AI approaches can assist in the Knowledge Graphs engineering lifecycle, e.g., ontology construction, entity and relation extraction, knowledge integration and mapping, completion via logical reasoning or validation, refinement via learning-based methods, and continuous evaluation and maintenance. Even when KGs are not explicitly embedded inside Neurosymbolic AI approaches, these approaches can improve methods and tools for knowledge engineering, paving the way for the next generation of Knowledge Graphs development practices. (3) KG-based approaches for understanding and systematizing NeSy AI, e.g., through design patterns, taxonomies, conceptual frameworks, or boxology representations. 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