AI 中文总结
该研究提出知识构造方法，通过详细的概念、数学和工程操作创建可扩展框架，利用WikiArt情感数据集构建界面，将语义和语用元数据建模为压力区，用梯度向量场量化人类探究“漂移”，可用于创造性人类行为领域。
AI 中文摘要
新兴知识可被设想为突出的岩浆。本文描述了如何将这种隐喻观点转化为一个模型，以捕捉认知岩石圈中概念的“大陆漂移”。我们将这种新方法称为知识构造。我们详细阐述了概念、数学和工程操作，以创建这样一个可扩展框架，从而能够在语义网环境中解释和管理知识演化。我们使用包含跨越600年的4105幅绘画信息的WikiArt情感数据集构建了一个概念验证界面，以实现对艺术品在特定特征景观中位置的可视化分析。我们展示了如何将融合的语义和语用元数据建模为不断演变的压力区。通过这种方式，我们使创造力产物演变背后的“力量”可见。我们新工作流程的核心元素是从应用于动态知识图的泊松势面导出的梯度向量场，最终将风格和情感转变捕获为定向强度流。通过将创造性过程的产物视为动态流形，我们提供了一种量化人类探究“漂移”的新方法。我们认为这种方法也适用于创造性人类行为的其他领域，包括科学知识生产。
英文摘要
Emerging knowledge can be envisioned as protruding magma. This paper describes how such a metaphoric view can be turned into a model to capture the 'continental drift' of concepts in an epistemic lithosphere. We call this new approach Knowledge Tectonics. We detail conceptual, mathematical and engineering operations to create such a scalable framework which allows us to interpret and manage knowledge evolution within Semantic Web environments. We use the WikiArt Emotions dataset which contains information on 4,105 paintings spanning 600 years to construct a proof--of--concept interface which enables visual analytics of where artworks are situated in a specific landscape of features. We demonstrate how fused semantic and pragmatic metadata can be modelled as evolving pressure zones. This way we are making 'forces' behind the evolution of artifacts of creativity visible. Core elements of our new workflow are gradient vector fields derived from Poisson potential surfaces applied onto dynamic knowledge graphs, eventually capturing stylistic and emotional shifts as directed intensity flows. By treating artefacts of creative processes as a dynamic manifold, we provide a novel methodology for quantifying the 'drift' of human inquiry. We argue that this approach is applicable also to other areas of creative human actions, including scientific knowledge production.
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