用大模型和异常检测，量化科研提案的新颖性。
Novelty-focused R&D landscaping using transformer and local outlier factor
- 用微调的Transformer提取提案语义，构建研发地图
- 通过局部离群因子（LOF）计算提案新颖度得分
- 适合需要评估科研创意价值的机构决策者
尽管已有大量研究聚焦研发（R&D）布局的预测分析，多基于专利与学术文献，但对研究提案本身及其新颖性分析的关注仍不足。本文提出一种系统化方法，通过融合基于Transformer的语言模型与局部离群因子（LOF），实现研发布局的构建与导航。利用微调后的Transformer捕捉研究提案的语义信息，构建全面的研发地图；再通过LOF量化每年新增提案相对于以往及同年内其他提案的差异程度，从而在数值上衡量其新颖性。以韩国能源与资源领域研究提案为例，验证了该方法的可重复性与时效性。结果表明，该流程与量化输出可作为研发规划与路线图制定的决策支持工具。
原文摘要 · Abstract (English)
While numerous studies have explored the field of research and development (R&D) landscaping, the preponderance of these investigations has emphasized predictive analysis based on R&D outcomes, specifically patents, and academic literature. However, the value of research proposals and novelty analysis has seldom been addressed. This study proposes a systematic approach to constructing and navigating the R&D landscape that can be utilized to guide organizations to respond in a reproducible and timely manner to the challenges presented by increasing number of research proposals. At the heart of the proposed approach is the composite use of the transformer-based language model and the local outlier factor (LOF). The semantic meaning of the research proposals is captured with our further-trained transformers, thereby constructing a comprehensive R&D landscape. Subsequently, the novelty of the newly selected research proposals within the annual landscape is quantified on a numerical scale utilizing the LOF by assessing the dissimilarity of each proposal to others preceding and within the same year. A case study examining research proposals in the energy and resource sector in South Korea is presented. The systematic process and quantitative outcomes are expected to be useful decision-support tools, providing future insights regarding R&D planning and roadmapping.
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