Neuro-Conceptual Artificial Intelligence: Integrating OPM with Deep Learning to Enhance Question Answering Quality (2025)

Abstract: 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.

## introduction integrating neural and symbolic approaches in ai seeks to combine the learning capabilities of neural networks with the interpretability of symbolic reasoning @xcite . however, traditional knowledge representations, such as triplet-based knowledge graphs, are limited in capturing complex processes, state changes, and hierarchical relationships inherent in dynamic systems @xcite heinzerling and inui, 2021; @xcite . additionally, neural networks are often viewed as a black box due to their opaque decision-making processes, which poses significant challenges in domains requiring transparent reasoning, such as healthcare and finance @xcite @xcite . recent advancements have focused on enhancing ai reasoning capabilities by integrating language models with external knowledge sources. for example, combining language models with knowledge graphs has been applied to improve question answering systems @xcite @xcite . despite these efforts, fully capturing dynamic behaviors and providing transparent reasoning paths remains a challenge. to address these limitations, we introduce neuro-conceptual artificial intelligence (ncai), a specialization of the neuro-symbolic ai approach, in which the symbolic component is an object-process methodology (opm iso 19450:2024) conceptual model. opm is a conceptual modeling language and methodology that unifies the system's structural and behavioral aspects within a single model @xcite . it represents objects (things that exist) and processes (things that transform objects) in both graphical and textual modalities. by combining opm with the large language model (llm), ncai enhances reasoning transparency and answer accuracy in qa tasks. an overview of the ncai framework is illustrated in figure 1 . the framework begins by converting natural language text into structured opm knowledge using in-context learning with an llm. this structured knowledge is then used in an opm-qa, which leverages the expressive power of opm to represent complex processes and state changes that traditional triplet-based knowledge graphs cannot easily capture. by integrating conceptual modeling with deep learning, ncai creates a pipeline that transforms unstructured text into a rich knowledge representation, enabling more effective ai reasoning and interpretability. our contributions in this work are threefold: @xcite we propose ncai, which integrates opm with deep learning to enhance reasoning transparency and answer accuracy. ## related work neuro-symbolic ai approaches neurosymbolic ai integrates neural networks with symbolic reasoning to harness the strengths of both paradigms @xcite . challenges in achieving reasoning transparency and interpretability persist, with approaches such as symbolic knowledge distillation @xcite and factual knowledge editing @xcite addressing these issues. frameworks like transfernet @xcite and interpretable reasoning models for dialogue generation @xcite aim to provide clear reasoning paths. in sentiment analysis and mental health, neuro-symbolic frameworks like tam-senticnet @xcite and causal inference models @xcite enhance explainability and logical inference. specifically, in aspect-based sentiment analysis (absa), models such as the multi-agent collaboration (mac) @xcite and approaches to improve ai transparency using generative agents @xcite demonstrate the potential of neuro-symbolic ai in providing transparent and rational sentiment analysis. interpretability and transparency in language models ensuring transparency and interpretability in ai decision-making is critical, particularly in complex systems @xcite . various methods have been developed to enhance the interpretability of language models, including representation dissimilarity measures @xcite , shap-based explanation techniques @xcite , and prompt-based explainers like promptexplainer @xcite . evaluation benchmarks for interpretability @xcite and approaches to improve faithfulness and robustness @xcite @xcite further contribute to making language models more transparent. despite these advancements, achieving full transparency remains challenging, especially in applications requiring a clear understanding of the reasoning process. language models and knowledge graphs for question answering integrating language models with knowledge graphs has been a significant focus to enhance qa capabilities. approaches like qa-gnn @xcite , drlk @xcite , and unik-qa @xcite combine language models with graph neural networks and dynamic interactions to improve reasoning in qa tasks. frameworks such as cikqa @xcite and triple-r @xcite emphasize the integration of external knowledge sources for more accurate and interpretable reasoning. additionally, methods like tapera @xcite enhance faithfulness and interpretability in long-form table qa through content planning and execution-based reasoning. these integrations, while improving performance, often involve complex architectures and still face challenges in achieving complete reasoning transparency. ## conclusion we propose neuro-conceptual artificial intelligence (ncai), a neuro-symbolic approach that integrates opm conceptual modeling with deep learning to overcome limitations in traditional knowledge representation and reasoning. by embedding opm-based conceptual logic into a qa system, ncai captures complex processes and state changes that conventional triplet-based representations and black box neural models struggle to address. through this structured, bimodal opm representation, ncai provides not only improved answer accuracy but also a demonstrably transparent and interpretable reasoning pathway. the introduction of transparency metrics (p t , r t , f1 t ) offers quantitative support for the alignment with opm-defined conceptual structures, moving beyond purely qualitative assessments of interpretability. our experimental results demonstrate that ncai substantially outperforms traditional methods on both standard accuracy-based measures and 2 , demonstrating a coherent and transparent reasoning path.