Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of concepts. As a knowledge hierarchy evolves with these automated techniques, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. In this paper, we focus on measures that leverage structural properties of the knowledge hierarchy graph to assess the temporal changes. We propose a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing different versions of a knowledge hierarchy, modeled as a generic directed acyclic graph. We present theoretical analysis to depict that the proposed measure accurately captures the salient properties of taxonomic hierarchies, assesses changes in the ordering of nodes, along with the logical subsumption of relationships among concepts. We also present a linear time variant of the measure, and show that our measures, unlike previous approaches, are tunable to cater to diverse application needs. We further show that our measure provides interpretability, thereby identifying the key structural and logical difference in the hierarchies. Experiments on a real DBpedia and biological knowledge hierarchy showcase that our measures accurately capture structural similarity, while providing enhanced scalability and tunability. Also, we demonstrate that the temporal evolution of different subgraphs in this knowledge hierarchy, as captured purely by our structural measure, corresponds well with the known disruptions in the related subject areas.

中文翻译：

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知识层次演化的自动评估：比较有向无环图

近年来，为了解决手动提取和语义链接数百万个概念的不可行问题，从庞大的数据集自动构建知识层次结构正受到越来越多的关注。随着知识层次结构通过这些自动化技术的发展，需要采取措施来评估其时间演化，量化不同版本之间的相似性并确定知识层次结构中不同子图的相对增长。在本文中，我们关注于利用知识层次图的结构属性来评估时间变化的措施。我们基于Katz相似度提出了原则上可扩展的相似度度量在概念节点之间，用于比较知识层次结构的不同版本，建模为通用有向无环图。我们目前进行的理论分析表明，所提出的措施可以准确地捕获分类层次结构的显着特性，评估节点顺序的变化，以及概念间关系的逻辑归纳。我们还介绍了该度量的线性时间变体，并表明我们的度量与以前的方法不同，可以调整以满足各种应用程序需求。我们进一步证明，我们的措施具有可解释性，从而确定层次结构中的关键结构和逻辑差异。在真实的DBpedia和生物学知识层次结构上进行的实验表明，我们的方法可以准确地捕获结构相似性，同时提供增强的可伸缩性和可调性。同样，我们证明，纯粹由我们的结构度量所捕获的此知识层次结构中不同子图的时间演变与相关主题领域中的已知中断非常吻合。