Studying Evolution of a Branch of Knowledge by Constructing and Analyzing Its Ontology
We propose a method for semi-automatic construction of an ontology of a given branch of science for measuring its evolution in time. The method relies on a collection of documents in the given thematic domain. We observe that the words of different levels of abstraction are located within different parts of a document: say, the title or abstract contains more general words than the body of the paper. What is more, the hierarchical structure of the documents allows us to determine the parent-child relation between words: e.g., a word that appears in the title of a paper is a candidate for a parent of the words appearing in the body of this paper; if such a relation is repeated several times, we register such a parent-child pair in our ontology. Using the papers corresponding to different years, we construct such an ontology for each year independently. Comparing such ontologies (using tree edit distance measure) for different years reveals the trends of evolution of the given branch of science.
KeywordsThematic Domain Paper Title Grey Rectangle Ontology Construction Vector Space Representation
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- 1.Kuhn, T.S.: The Structure of Scientific Revolutions, 2nd edn. University of Chicago Press (1970)Google Scholar
- 2.References Book of Proceedings of Free Economic Society of Russia. (1983–2000), Vol.4. p. 756, Moscow, Russia (2000)Google Scholar
- 3.Makagonov, P., Alexandrov, P.M., Sboychakov, K.: A toolkit for development of the domain-oriented dictionaries for structuring document flows. In: Kiers, H.A., et al. (eds.) Data Analysis, Classification, and Related Methods, Studies in classification, data analysis, and knowledge organization, pp. 83–88. Springer, Heidelberg (2000)CrossRefGoogle Scholar
- 4.IRBIS Automated Library System, Russian National Public Library for Science and Technology, http://www.gpntb.ru
- 7.Makagonov, P., Ruiz Figueroa, A., Sboychakov, K., Gelbukh, A.: Learning a Domain Ontology from Hierarchically Structured Texts. In: Proc. of Workshop Learning and Extending Lexical Ontologies by using Machine Learning Methods at 22nd International Conference on Machine Learning, ICML 2005, Bonn, Germany (2005)Google Scholar