- From: Shimizu, Cogan Matthew <cogan.shimizu@wright.edu>
- Date: Wed, 21 Jan 2026 19:30:02 +0000
- To: W3C Semantic Web IG <semantic-web@w3.org>
- Message-ID: <CYYPR01MB8290AC0225F8E4FC834D8C038496A@CYYPR01MB8290.prod.exchangelabs.com>
[apologies for cross-listing] Dear everyone, The First Workshop on LLM-driven Knowledge Graph and Ontology Engineering (llms4kgoe) invites submissions at the rapidly evolving intersection of Large Language Models (LLMs), semantic technologies, and ontology engineering. It addresses challenges and opportunities in leveraging LLMs for ontology creation, refinement, and validation, covering both theoretical and practical aspects such as automated ontology generation pipelines, evaluation methodologies, hallucination mitigation, and ensuring semantic consistency. The workshop provides a venue to discuss methods, evaluation techniques, and best practices for this emerging paradigm. Submission Guidelines All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome: * Full papers should describe a well-motivated problem, position it in existing work, present the proposed method or system with enough detail to understand and reasonably reproduce it, and support its claims with a thorough set of experiments or analyses plus a short reflection on limitations and implications. Length of full papers should be between 10-12 pages (CEUR Style). * Short Papers should describe one focused idea or preliminary result, explain why it is interesting, outline the core approach or insight without exhaustive technical detail, and provide enough empirical or analytical evidence to make the contribution credible. Length of Short papers should be 6 pages (CEUR style). * For all manuscript submissions, at least one author must agree to review another paper List of Topics * LLM-to-KG with schema/pattern constraints * Education & UX for CQ/axiom authoring * Evidence-linked triple extraction and provenance * Hallucination benchmarking: metrics and datasets * Post-hoc KG repair with reasoners or learned validators * Calibration and abstention for link prediction and typing * Robustness and red-teaming for KG extraction * Domain-specific KG applications (biomedical, climate, materials science, etc.) * Lifecycle & Maintenance of ontologies with LLMs * Modular Evaluation & Benchmarks * Provenance & Governance in ontology workflows * Neuro-symbolic control (SHACL/OWL-guided generation, reasoner-in-the-loop) * Human-in-the-loop protocols * Domain adaptation & continual learning * Multilingual & multimodal ontology engineering * Operational efficiency metrics * Standards & community alignment (FAIR, OBO, ODP, OAEI, LLMs4OL) Organizing committee * Aryan Singh Dalal, Kansas State University * Kathleen Jagodnik, Kansas State University * Hande McGinty, Kansas State University * Cogan Shimizu, Wright State University * Maria Maleshkova, Helmut Schmidt University Publication llms4kgoe 2026 proceedings will be published by CEUR. Authors are advised to Follow the CEUR Venue In-person, co-located with ESWC 2026 Important Dates * Submission Deadline: Feb 20, 2026 [AOE] * Notification Deadline: Mar 15, 2026 Contact All questions about submissions should be emailed to aryand@ksu.edu
Received on Friday, 23 January 2026 15:36:00 UTC