Abstract
The ability to freely publish any information content is causing rapid growth of unstructured, duplicated and unreliable information volumes with irregular dynamics. This significantly complicates timely access to actual reliable information especially in the tasks of the specific scientific topics monitoring or when it is necessary to get quick insight of adjacent scientific fields of interest. The paper contains the description of the technology of text representation as a semantic graph. The algorithmic implementation of proposed technology in the tasks of fuzzy and exploratory information search is developed. The problems of current search technologies are considered. The proposed ontology-associative graph matching approach to post-full-text search system development is capable of solving the problem of document search under conditions of insufficient initial data for correct query formation.
The proposed graph representation of texts allows restricting usable ontology, which in turn gives the benefit of thematic localization of the search region in the field of knowledge.
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Acknowledgements
The research is partly supported by the RFBR, project NÂ 16-29-12965\18 and by the budget 0073-2019-0005.
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Kuleshov, S., Zaytseva, A., Aksenov, A. (2019). Natural Language Search and Associative-Ontology Matching Algorithms Based on Graph Representation of Texts. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Intelligent Systems Applications in Software Engineering. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1046. Springer, Cham. https://doi.org/10.1007/978-3-030-30329-7_26
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DOI: https://doi.org/10.1007/978-3-030-30329-7_26
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