Knowledge representation and reasoning are critical challenges in Artificial Intelligence (AI), particularly in integrating neural and symbolic approaches to achieve explainable and transparent AI systems. Traditional knowledge representation methods often fall short of capturing complex processes and state changes. We introduce Neuro-Conceptual Artificial Intelligence (NCAI), a specialization of the neuro-symbolic AI approach that integrates conceptual modeling using Object-Process Methodology (OPM) ISO 19450:2024 with deep learning to enhance question-answering (QA) quality. By converting natural language text into OPM models using in-context learning, NCAI leverages the expressive power of OPM to represent complex OPM elements—processes, objects, and states—beyond what traditional triplet-based knowledge graphs can easily capture. This rich structured knowledge representation improves reasoning transparency and answer accuracy in an OPM-QA system. We further propose transparency evaluation metrics to quantitatively measure how faithfully the predicted reasoning aligns with OPM-based conceptual logic. Our experiments demonstrate that NCAI outperforms traditional methods, highlighting its potential for advancing neuro-symbolic AI by providing rich knowledge representations, measurable transparency, and improved reasoning.

The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.