TRIZ-RAGNER: 一种用于基于专利矛盾挖掘的TRIZ-aware命名实体识别的检索增强型大语言模型
TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining
AI总结：
TRIZ-RAGNER通过整合TRIZ知识和检索增强技术，提升专利矛盾挖掘中命名实体识别的准确性和一致性。
摘要：
基于TRIZ的矛盾挖掘是专利分析和系统创新中的基础任务，因为它能够识别驱动创新问题解决的改进和恶化的技术参数。然而，现有方法大多依赖于基于规则的系统或传统机器学习模型，在处理复杂专利语言时面临语义模糊、领域依赖性和有限泛化能力的挑战。最近，大型语言模型（LLMs）显示出强大的语义理解能力，但将其直接应用于TRIZ参数提取仍然具有挑战性，因为存在幻觉和在结构化的TRIZ知识中缺乏足够的基础。为了解决这些限制，本文提出了TRIZ-RAGNER，一种用于基于专利矛盾挖掘的TRIZ-aware命名实体识别的检索增强型大语言模型框架。TRIZ-RAGNER将矛盾挖掘重新表述为语义层面的命名实体识别任务，并整合了对TRIZ知识库的密集检索、交叉编码器重排序用于上下文细化以及结构化LLM提示以从专利句子中提取改进和恶化的参数。通过将领域特定的TRIZ知识注入LLM的推理过程中，所提出的框架有效减少了语义噪声并提高了提取的一致性。在PaTRIZ数据集上的实验表明，TRIZ-RAGNER在TRIZ矛盾对识别中的一致性优于传统序列标记模型和LLM基线模型。所提出的框架在TRIZ矛盾对识别中实现了85.6%的精确率、82.9%的召回率和84.2%的F1分数。与使用提示增强GPT的最强基线相比，TRIZ-RAGNER在F1分数上实现了7.3个百分点的绝对提升，证实了检索增强型TRIZ知识基础对于稳健和准确的专利矛盾挖掘的有效性。
英文摘要：
TRIZ-based contradiction mining is a fundamental task in patent analysis and systematic innovation, as it enables the identification of improving and worsening technical parameters that drive inventive problem solving. However, existing approaches largely rely on rule-based systems or traditional machine learning models, which struggle with semantic ambiguity, domain dependency, and limited generalization when processing complex patent language. Recently, large language models (LLMs) have shown strong semantic understanding capabilities, yet their direct application to TRIZ parameter extraction remains challenging due to hallucination and insufficient grounding in structured TRIZ knowledge. To address these limitations, this paper proposes TRIZ-RAGNER, a retrieval-augmented large language model framework for TRIZ-aware named entity recognition in patent-based contradiction mining. TRIZ-RAGNER reformulates contradiction mining as a semantic-level NER task and integrates dense retrieval over a TRIZ knowledge base, cross-encoder reranking for context refinement, and structured LLM prompting to extract improving and worsening parameters from patent sentences. By injecting domain-specific TRIZ knowledge into the LLM reasoning process, the proposed framework effectively reduces semantic noise and improves extraction consistency. Experiments on the PaTRIZ dataset demonstrate that TRIZ-RAGNER consistently outperforms traditional sequence labeling models and LLM-based baselines. The proposed framework achieves a precision of 85.6%, a recall of 82.9%, and an F1-score of 84.2% in TRIZ contradiction pair identification. Compared with the strongest baseline using prompt-enhanced GPT, TRIZ-RAGNER yields an absolute F1-score improvement of 7.3 percentage points, confirming the effectiveness of retrieval-augmented TRIZ knowledge grounding for robust and accurate patent-based contradiction mining.
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