https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc/Head https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc/assertion https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc/provenance https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc/pubinfo https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAGQa-jFnuqXuKvoPUNTP8urvve-7BbtxNCEfs4CaZ_Yc/assertion https://w3id.org/spaces/iswc/2026/aiaa4ke http://purl.org/dc/terms/description The AI Assistants for Knowledge Engineering (AIAA4KE) 2026 workshop explores the potential of generative AI and agentic workflows in Knowledge Engineering. While AI-assisted approaches have revolutionised general software engineering, knowledge engineering — including ontology modelling and knowledge graph (KG) construction — remains a bottleneck, reliant on scarce human expertise. AIAA4KE provides a platform to discuss topics such as multi-modal user intent analysis, automated ontology matching, and KG curation from diverse data sources. Key focus areas include developing evaluation benchmarks, integrating emerging standards like MCP-based agentic systems, and implementing human-in-the-loop governance for auditing AI-driven changes. We welcome presentations across the RDF/OWL and labelled property graph communities, aiming to scale knowledge engineering through robust, industry-ready AI assistants. AIAA4KE seeks to connect three key communities: Research communities at the intersection of AI assistants and Semantic Web technologies focus on developing technically sound methods, but often lack opportunities to validate their work in real-world settings. Industry practitioners are actively building AI assistants for ontology and KG creation, yet face challenges around the accessibility of Semantic Web technologies and the difficulty of evaluating the quality and alignment of generated outputs. Standardisation experts, active in both academia and industry, are well-positioned to bridge these communities but are often underrepresented in the conversation. 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