Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data-mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data-mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data-mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge-based methods or big-data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data-mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

中文翻译：

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信息时代的语义数据挖掘：系统评价

数据挖掘是在数据中发现有意义的信息或未揭示的模式。传统的数据挖掘方法使用统计计算、机器学习、人工智能和数据库技术，无法在概念或语义层面解释数据，也无法揭示数据中的含义。这导致用户不被分析并确定其含义和含义。在过去的十年中已经提出了几种语义数据挖掘方法，它们通过使用领域本体作为背景知识来实现​​和增强数据挖掘性能来克服这些限制。这项文献调查的主要贡献包括以一种新的方式组织调查的文章，使感兴趣的研究人员易于理解，并提供对所调查文章的批判性分析和总结，确定这些论文对该领域的贡献，以及本语料库中讨论的分析方法和方法的局限性，目的是告知这一不断发展的领域中的研究人员新研究的创新方法。最后，我们确定了本研究轨道中未来研究人员会关注的未来趋势和挑战，例如基于动态知识的方法或大数据工具协作。这篇综述文章全面概述了用于各种语义数据挖掘任务（例如预处理、建模和后处理）的领域本体的文献。我们研究了语义数据挖掘在数据科学领域的作用以及将语义数据挖掘应用于数据资源描述框架的过程和方法。