Ontological Approach: Knowledge Representation and Knowledge Extraction
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 fundamenta...
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| Published in | Lobachevskii journal of mathematics Vol. 41; no. 10; pp. 1938 - 1948 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Moscow
Pleiades Publishing
01.10.2020
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| 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. |
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| AbstractList | 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. |
| Author | Serebryakov, V. A. Tuchkova, N. P. Ataeva, O. M. |
| Author_xml | – sequence: 1 givenname: O. M. surname: Ataeva fullname: Ataeva, O. M. email: oli@ultimeta.ru organization: Federal Research Center ’’Computer Sciences and Control’’, Russian Academy of Sciences – sequence: 2 givenname: V. A. surname: Serebryakov fullname: Serebryakov, V. A. email: serebr@ultimeta.ru organization: Federal Research Center ’’Computer Sciences and Control’’, Russian Academy of Sciences – sequence: 3 givenname: N. P. surname: Tuchkova fullname: Tuchkova, N. P. email: natalia_tuchkova@mail.ru organization: Federal Research Center ’’Computer Sciences and Control’’, Russian Academy of Sciences |
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| Cites_doi | 10.1007/s11192-017-2304-3 10.1016/S0004-3702(98)00055-1 10.1093/ije/dyl189 10.1016/j.artint.2019.02.003 10.26615/978-954-452-056-4_031 10.1134/S1054661819040114 10.2200/S00602ED1V01Y201410ICR035 10.20948/abrau-2017-39 10.1134/S1064562416020174 10.1007/BF02478112 10.1108/00242530910961792 10.1134/S1995080219070047 10.1007/978-3-642-31374-5_36 10.1073/pnas.0507655102 |
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| Keywords | knowledge base knowledge space knowledge extraction knowledge metrics ontology of subject domain artificial intelligence methods |
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