Ontological Approach: Knowledge Representation and Knowledge Extraction

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.