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Hi All,<br><br><div>We are pleased to announce GenAIK-NORA: The Joint 
Workshop on Generative AI and Knowledge Graphs and KNOwledge GRaphs 
& Agentic Systems Interplay, which will be co-located with 
IJCAI-ECAI 2026 in Bremen, Germany.</div><div><br></div><div>More details below.</div><div><br></div><div><br></div>

<b style="font-weight:normal" id="m_-3876976961725951748m_773160434350482659m_8042265549148283346m_-1475434555928246383gmail-docs-internal-guid-b7d7af0b-7fff-7f09-5c12-88fd686276ac"><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><b><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Joint Call for Papers</span></b></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Joint Workshop on Generative AI and Knowledge Graphs (GenAIK) and KNOwledge GRaphs & Agentic Systems Interplay (NORA),</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">15-17 August 2026,  Bremen, Germany</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Web: <a href="https://genetasefa.github.io/GenAIK2026/" target="_blank">https://genetasefa.github.io/GenAIK2026/</a> and <a href="https://nora-workshop.github.io/IJCAIECAI2026/" target="_blank">https://nora-workshop.github.io/IJCAIECAI2026/</a></span></p><br><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">X: @GenAIK26 </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">LinkedIn: <a href="https://www.linkedin.com/groups/9868047" target="_blank">https://www.linkedin.com/groups/9868047</a> and <a href="https://www.linkedin.com/company/nora-knowledge-graphs-agentic-systems-interplay" target="_blank">https://www.linkedin.com/company/nora-knowledge-graphs-agentic-systems-interplay</a></span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Mastodon: <a href="https://sigmoid.social/@GenAIK" target="_blank">https://sigmoid.social/@GenAIK</a> </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">In conjunction with <b>IJCAI-ECAI 2026</b>, August 15-19</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><b><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Workshop Overview</span></b></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Recent advances in Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) have transformed the AI landscape, enabling systems to generate multimodal content and perform increasingly complex reasoning and decision-making tasks. Despite these advances, generative models still face important challenges, including hallucinations, limited interpretability, and difficulties in grounding outputs in reliable domain knowledge.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Knowledge Graphs (KGs) provide a principled framework for representing structured and interconnected knowledge through entities, relations, and formal ontologies. They enable interpretability, reasoning, and the integration of domain expertise, making them an important component for building reliable and trustworthy AI systems. At the same time, LLM-based agents are emerging as a powerful paradigm for building autonomous systems capable of planning, tool use, and long-term task execution, often requiring structured representations of knowledge and memory.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">The interaction between generative models, agentic systems, and knowledge graphs is therefore becoming an important research direction in contemporary AI. This workshop aims to bring together researchers and practitioners from AI, NLP, Knowledge Graphs, Semantic Web, and Hybrid AI to explore methods, systems, and applications that combine these paradigms.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">This edition represents a joint workshop, bringing together the communities of GenAIK (Generative AI and Knowledge Graphs) and NORA (Knowledge Graphs and Agentic Systems Interplay) to foster collaboration across these complementary research areas.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><b><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Topics of Interest</span></b></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- KG construction, completion, and refinement with GenAI and Agents</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Text-to-KG extraction using LLMs (multilingual, multimodal)</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— KG completion, cleaning, and refinement (deduplication, entity resolution)</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Fact verification, contradiction detection, and consistency checking</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Human-in-the-loop KG curation and interactive refinement</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- KG grounding for information retrieval, including generation, querying, and dialogue</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— KG-grounded generation / GraphRAG (subgraph retrieval, path-based evidence)</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Natural language querying of KGs via GenAI (e.g., NL-to-SPARQL) </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Hybrid QA and dialogue systems combining KGs and GenAI (e.g., Agents) </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Prompting / controllable generation using KG structure and constraints</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Context and memory indexing and retrieval for GenAI and agents</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Neuro-symbolic methods, reasoning, and explainability</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Hybrid reasoning with rules, constraints, and structured evidence </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Explainability and verifiable reasoning with provenance/evidence graphs </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Cross-domain knowledge transfer with KGs and GenAIK</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Representations, embeddings, and temporal/evolving KGs</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— GenAI for KG embeddings and hybrid vectorgraph representations </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Temporal KGs, dynamic updates, continual learning, concept drift </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Ontology learning, schema induction, alignment, and schema evolution</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Trustworthiness, safety, and governance</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Bias mitigation using KGs in GenAI and Agentic Systems</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Hallucination reduction via grounding/constraints; robustness to attacks </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Uncertainty estimation and calibrated confidence </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Privacy, access control, and policy-aware KG-grounded generation</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Agentic KGs and real-world systems </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Agentic KGs: KGs as long-term memory/state for LLM agents</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— KGs serving agents' memories: Episodic (experiences, events, etc.), Semantic (facts, concepts, etc.), and — Procedural (skills, tasks, etc.)</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— KG-aware planning, tool use (query/update), and multi-agent coordination</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Collaborative & shared agent memories and contexts.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Context Engineering enhanced by KGs</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- GenAI/Agents and KG Applications</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Efficient and proactive personal assistance & Personalisation</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Multi-Lingual & Multi-modal integrations and enablement</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Personalisation vs Generalisation in GenAI and Agentic systems memory</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Domain-specific applications: scholarly knowledge, biomedicine & healthcare, finance, education, etc.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Task-specific applications: personal assistance, dialogue systems, recommender systems, customer service, etc.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Architectures and pipelines </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Datasets, benchmarks, and evaluation</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Benchmark datasets for GenAI or Agents plus KG tasks</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Evaluation of grounding/faithfulness, factuality, reasoning, robustness</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">— Evaluation pitfalls: data leakage, LLM-as-a-judge bias, reproducibility, and reporting standards</span></p><br><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">------------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><b><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Important Dates (AoE)</span></b></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">------------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Submission Deadline: 7 May 2026</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Notification of Acceptance: 10 June 2026</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Camera-ready Paper Due: 25 June 2026</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Workshop date (In-Person): 15, 16, or 17 August 2026</span></p><br><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">------------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Submissions Guidelines, Policies, and Awards are available on the website.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">------------------------------------------------------------------------------------</span></p><br><br><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><b><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Organization</span></b></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">---------------------------------------------------------------------------------</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">GenAIK Team:</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Genet Asefa Gesese, FIZ Karlsruhe, KIT, Germany</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Angelo Salatino, The Open University, UK.</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Blerina Spahiu,  University of Milano-Bicocca, Italy</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Shenghui Wang, University of Twente, The Netherlands </span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Heiko Paulheim, University of Mannheim, Germany</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">NORA Team:</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Btissam Er-Rahmadi, Independent Researcher, UK</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Sebastien Montella, Huawei Technologies R&D UK Ltd, UK</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Damien Graux, EcoVadis, UK</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Andre Melo,  Huawei Technologies R&D UK Ltd, UK</span></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:"Times New Roman",serif;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Hajira Jabeen, University Hospital Cologne, Germany</span></p></b><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><span>Btissam Er-Rahmadi<br></span></div><div><br></div><div><span></span></div></div></div></div>