Overview

Integrating Knowledge Engineering (KE) with Machine Learning (ML) offers a promising approach to building trustworthy AI systems. This integration combines the strengths of data-driven learning with formal, structured reasoning, enabling AI models to be both highly accurate and explainable. By leveraging 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 symbolic methods for knowledge representation and reasoning can sometimes limit scalability, their ability to provide verifiable, human-understandable explanations makes them especially valuable in mission-critical applications.

The workshop proposed for HHAI 2025 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 symbolic reasoning with data-driven techniques.

Important Dates

• Paper Submission: April 10, 2025 (23:59 AoE)
• Acceptance Notification: May 2, 2025
• Camera-ready Version: May 10, 2025
• Conference: June 9–13, 2025

Organizing & Program Committees

Organizing Committee

• Shreya Banerjee, University of New Orleans
• Atriya Sen, Oklahoma State University

Program Committee

• Shreya Banerjee, University of New Orleans
• Atriya Sen, Oklahoma State University
• Anthony Marchiafava, Oklahoma State University
• Henry Fordjour Ansah, University of New Orleans
• TBD

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
• Human-Centric Multimodal AI
• Human-AI Interaction and Human-in-the-loop

Paper Submission & Review Process

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

Paper Types:

• Regular Paper: 10 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.