基于视觉信息的科技文献知识领域划分
Delineating Knowledge Domains in the Scientific Literature Using Visual Information
摘要：
数字是科学交流的重要渠道，用于以文字无法表达的方式表达复杂的想法，模型和数据。然而，在科学文献的分析中，这种视觉信息大多被忽视。在本文中，我们展示了使用科学数字作为科学知识领域的标记的实用性，它可用于分类，推荐系统和科学信息交换的研究。我们将一组图像编码成一个视觉签名，然后使用这些签名之间的距离来理解视觉交流模式与行话和引用结构模式的比较。我们发现，数字可以有效区分实践社区的文本或引用模式。然后，我们考虑这些指标不一致的地方，以了解不同的学科如何使用可视化来表达想法。最后，我们进一步考虑如何通过文学传播特定的数字类型，提出了一种新的机制，除了传统的文本和引用渠道的理解的思想流。我们的最终目标是更好地利用这些信息密集的对象，以改善跨学科界限的科学交流。
英文摘要：
Figures are an important channel for scientific communication, used to express complex ideas, models and data in ways that words cannot. However, this visual information is mostly ignored in analyses of the scientific literature. In this paper, we demonstrate the utility of using scientific figures as markers of knowledge domains in science, which can be used for classification, recommender systems, and studies of scientific information exchange. We encode sets of images into a visual signature, then use distances between these signatures to understand how patterns of visual communication compare with patterns of jargon and citation structures. We find that figures can be as effective for differentiating communities of practice as text or citation patterns. We then consider where these metrics disagree to understand how different disciplines use visualization to express ideas. Finally, we further consider how specific figure types propagate through the literature, suggesting a new mechanism for understanding the flow of ideas apart from conventional channels of text and citations. Our ultimate aim is to better leverage these information-dense objects to improve scientific communication across disciplinary boundaries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
