Abstract

The application of artificial intelligence algorithms for data analysis, characteristics, and metrics of scientific information resources are considered. In this paper, we discuss how metrics are related to assessment of scientific publication components and whether metrics are related to fundamental knowledge. It was noted that the characteristics of professional scientific activity are evaluated on the basis of metrics that are not related to the knowledge characteristics. The problem of knowledge extraction was studied on the basis of data verification by means of logical evidence–based schemes specified in the knowledge ontology. Properties of the modern stage of development of the knowledge space as a resource of artificial intelligence were noted. The transformation of artificial intelligence tasks into a new digital age was also analyzed. The insufficient use of artificial intelligence and machine learning methods in scientific bibliographic databases was emphasized, where quantitative scientometric indicators prevailed. Examples of ontological presentation of data and knowledge extraction are discussed and the special role of ontological approach to data structuring and knowledge extraction is highlighted.

中文翻译：

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本体论方法：知识表示和知识提取

摘要

考虑了人工智能算法在科学信息资源的数据分析，特征和度量中的应用。在本文中，我们讨论了指标如何与科学出版物组成部分的评估相关以及指标是否与基础知识相关。有人指出，专业科学活动的特征是根据与知识特征无关的指标进行评估的。通过基于知识本体中指定的基于逻辑证据的方案，在数据验证的基础上研究了知识提取问题。指出了知识空间作为人工智能资源发展的现代阶段的特性。还分析了人工智能任务到新的数字时代的转变。有人强调指出，在科学的书目数据库中，人工智能和机器学习方法的使用不足，而占主导地位的是定量科学计量指标。讨论了数据的本体表示和知识提取的示例，并着重介绍了本体方法在数据结构和知识提取中的特殊作用。