对比学界与产业在创新性上的差异，发现学界更擅长产出新颖成果。
Exploring Novelty Differences between Industry and Academia: A Knowledge Entity-centric Perspective
- 以方法、工具、数据集、度量标准四类知识实体为单位，统一量化新颖性。
- 学界在论文和专利中的新颖性更高，尤其在专利领域优势明显。
- 产业在数据集方面有独特优势，合作对专利新颖性提升更有效。
学界与产业在推动技术进步中各有优势：学界强调成果公开与学科发展，产业重视知识可占有性与核心竞争力，同时参与学术会议与平台共享，形成知识策略悖论。高新颖性且公开的知识是技术进步的核心驱动力，但学界与产业谁更能产生新颖成果尚不明确。既有研究受限于数据来源与新颖性衡量方式不一致。本研究基于四种细粒度知识实体（方法、工具、数据集、度量标准），在统一语义空间中计算实体间语义距离以量化新颖性，实现不同文献类型间的可比性，并构建回归模型分析学界与产业在发表新颖性上的差异。结果表明，学界整体新颖性更高，尤其在专利中表现突出。在实体层面，两者均以方法驱动论文进展，而产业在数据集方面具有独特优势。此外，产学研合作对论文新颖性提升有限，但有助于提升专利的新颖性。数据与代码已开源：https://github.com/tinierZhao/entity_novelty。
原文摘要 · Abstract (English)
Academia and industry each possess distinct advantages in advancing technological progress. Academia's core mission is to promote open dissemination of research results and drive disciplinary progress. The industry values knowledge appropriability and core competitiveness, yet actively engages in open practices like academic conferences and platform sharing, creating a knowledge strategy paradox. Highly novel and publicly accessible knowledge serves as the driving force behind technological advancement. However, it remains unclear whether industry or academia can produce more novel research outcomes. Some studies argue that academia tends to generate more novel ideas, while others suggest that industry researchers are more likely to drive breakthroughs. Previous studies have been limited by data sources and inconsistent measures of novelty. To address these gaps, this study conducts an analysis using four types of fine-grained knowledge entities (Method, Tool, Dataset, Metric), calculates semantic distances between entities within a unified semantic space to quantify novelty, and achieves comparability of novelty across different types of literature. Then, a regression model is constructed to analyze the differences in publication novelty between industry and academia. The results indicate that academia demonstrates higher novelty outputs, which is particularly evident in patents. At the entity level, both academia and industry emphasize method-driven advancements in papers, while industry holds a unique advantage in datasets. Additionally, academia-industry collaboration has a limited effect on enhancing the novelty of research papers, but it helps to enhance the novelty of patents. We release our data and associated codes at https://github.com/tinierZhao/entity_novelty.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。