Overview

Integrating Knowledge Engineering (KE) with Machine Learning (ML) offers a promising approach to building trustworthy AI systems. By combining the strengths of data-driven learning with structured knowledge—such as electronic health records in healthcare, scientific axioms, or legal guidelines—AI systems gain the ability to perform commonsense reasoning, enhancing their reliability and making them more knowledge-aware. Although using knowledge representation and reasoning methods can sometimes limit scalability, their ability to provide verifiable, human-understandable explanations makes them especially valuable in mission-critical applications.

The workshop hosted by HHAI 2026 seeks to bridge the gap between KE and ML by exploring the synergies between these fields. A key focus is on developing hybrid human-AI systems that utilize multimodal approaches, incorporating various forms of data including text, speech, images, and video. This collaborative forum will bring together researchers and practitioners from academia and industry to discuss cutting-edge research and innovative strategies for integrating KE and ML. Ultimately, the goal is to advance the development of AI systems that are not only robust and efficient but also transparent and human-centric, addressing both the challenges and benefits of merging knowledge representation and reasoning with data-driven techniques.

Check out our previous year proceedings here HHAI-KEML 2025 Workshop Proceedings

Important Dates

• Paper Submission: May 16, 2026 (23:59 AoE)
• Acceptance Notification: May 23
• Camera-ready Version: May 26, 2026
• Workshop: July 6 or 7, 2026
• Conference: July 8–10, 2026

Organizing & Program Committees

Organizing Committee

• Shreya Banerjee, University of New Orleans
• Atriya Sen, Oklahoma State University
• Yuan Yuan Lin, Kristiania University College

Program Committee

• Shreya Banerjee, University of New Orleans
• Atriya Sen, Oklahoma State University
• Yuan Yuan Lin, Kristiania University College
• Anthony Marchiafava, Oklahoma State University
• Henry Fordjour Ansah, University of New Orleans
• Bithi Banik, Kristiania University College

Topics of Interest

• Neuro-Symbolic Knowledge Representation
• KRR and Commonsense Reasoning in ML
• Artificial General Intelligence
• KE with Generative AI and Large Language Models
• KE in Multi-Agent AI Systems
• KRR in Hybrid AI Systems for Health and Other Application Domains
• Human-Centric Multimodal AI
• Human-AI Interaction and Human-in-the-loop
• Knowledge Repressentation in World Model

Paper Submission & Review Process

Authors should submit papers electronically in PDF format to Easychair. Please use CEURART style formatting to write single column papers to be published with CEUR-WS.

Paper Types:

• Regular Paper: 10-12 pages (excluding references)
• Short Paper: 5–9 pages (excluding references)
• Abstract/Extended Abstract: Title, author, and abstract only
• Poster Paper:A title, author and a very short text (less than 5 “standard” CEUR-WS pages). Poster papers will mostly be handled like an abstract.

All submissions will be peer-reviewed through a double-blind process. Papers should be anonymized and written in English. Submissions of regular and short papers should be original work without substantial overlap with pre-published papers. For further details, please see the submission guidelines.

Accepted submissions shall be submitted to CEUR-WS.org for online publication in CEUR Workshop Proceedings (Scopus indexed). Contributions will be presented either as oral presentations (lightning talks) or posters.