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.

翻译：学术界与产业界在推动技术进步方面各具独特优势。学术界的核心使命是促进研究成果的公开传播并推动学科发展，而产业界虽重视知识的可占有性与核心竞争力，却仍积极参与学术会议和平台共享等开放实践，由此形成了知识策略悖论。高度新颖且可公开获取的知识是技术进步的核心驱动力，然而，关于产业界与学术界孰能产出更具新颖性的研究成果这一问题尚不明确。部分研究认为学术界更易产生新颖观点，另一些研究则指出产业界研究者更可能推动突破性进展。受限于数据来源及新颖性度量标准的不一致，先前研究存在局限性。为弥补上述不足，本研究采用四类细粒度知识实体（方法、工具、数据集、度量指标）进行分析，通过计算统一语义空间中实体间的语义距离来量化新颖性，并实现了跨文献类型的新颖性可比性。进而构建回归模型，分析产业界与学术界出版物新颖性的差异。结果表明，学术界展现出更高的新颖性产出，这一现象在专利中尤为显著。在实体层面，学术界与产业界在论文中均强调方法驱动的进展，但产业界在数据集方面具有独特优势。此外，学界-产业界合作对提升研究论文新颖性的作用有限，却有助于增强专利的新颖性。我们已在 https://github.com/tinierZhao/entity_novelty 公开相关数据与代码。