用概念建模提升问答准确率与可解释性
Neuro-Conceptual Artificial Intelligence: Integrating OPM with Deep Learning to Enhance Question Answering Quality
- 将自然语言转为OPM概念模型，构建更丰富的知识表示
- 在QA任务中实现更高准确率和推理透明度
- 适合需要可解释AI的医疗、金融等高风险领域
知识表示与推理是人工智能的核心挑战，尤其在融合神经与符号方法以实现可解释、透明AI系统方面。传统知识表示难以捕捉复杂过程与状态变化。本文提出神经-概念人工智能（NCAI），一种神经符号AI的特殊形式，通过将基于国际标准ISO 19450:2024的物体-过程方法论（OPM）与深度学习结合，提升问答质量。利用上下文学习将自然语言文本转化为OPM模型，充分表达过程、对象与状态等复杂元素，超越传统三元组知识图谱的表达能力。该结构化知识显著增强推理透明性与答案准确性。我们还提出透明度评估指标，定量衡量预测推理与OPM逻辑的一致性。实验表明，NCAI优于传统方法，展示了其在提供丰富知识表示、可度量透明性与改进推理方面的潜力。
原文摘要 · Abstract (English)
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.
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