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Onto-Graphic Mechanisms for Deep Semantic Search

  • INFORMATION SEARCH
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Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract

In human-machine document retrieval frameworks focused on information support for main activity cognitive processes, onto-graph-based mechanisms for deep semantic search are discussed. The mechanisms of the application of examples corresponding to users’ cognitive states are given on graphs constructed from full texts. The paper gives a comparative evaluation of graph search mechanisms effectiveness in retrieval tasks, as applied to text reading processes.

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Notes

  1. Below, “graph.”

  2. The vertex type characterizes the origin of the corresponding name: from the text, from the thesaurus, from the taxonomy of properties and units of measurement, etc.

  3. The role of the entity name is determined based on the extended functional model [8].

  4. The vertex weight is calculated based on the frequency of occurrence, role, belonging to significant text fragments.

  5. The relationship class is defined in accordance with the taxonomy of functional relationship classes [9].

  6. In this case, visualization tools can be considered as search tools that perform array ranking. This makes it possible to lower search efforts by reducing the space of perception, as well as ordering and formatting graph elements in accordance with the semantics of the document and the pragmatics of the task.

  7. Note that automatic role recognition is in the stage of formation at the moment and therefore is not always correct, but even this result allows the user to form an initial idea of the processes described in the text.

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Funding

The study was supported by the Ministry of Science and Higher Education of the Russian Federation (draft state assignment no. 0723-2020-0036).

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Authors and Affiliations

  1. National Research Nuclear University MEPhI, Moscow, Russia

    A. A. Lebedev, A. S. Gavrilkina, N. V. Maksimov, O. L. Golitsina & K. V. Monankov

Authors
  1. A. A. Lebedev
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  2. A. S. Gavrilkina
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  3. N. V. Maksimov
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  4. O. L. Golitsina
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  5. K. V. Monankov
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Corresponding authors

Correspondence to A. A. Lebedev, A. S. Gavrilkina, N. V. Maksimov, O. L. Golitsina or K. V. Monankov.

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The authors declare that they have no conflict of interest.

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Translated by L. Solovyova

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Lebedev, A.A., Gavrilkina, A.S., Maksimov, N.V. et al. Onto-Graphic Mechanisms for Deep Semantic Search. Autom. Doc. Math. Linguist. 56, 163–178 (2022). https://doi.org/10.3103/S0005105522040057

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