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Semantic Search Tools Based on Ontological Representations of Documentary Information

  • Information analysis
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Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract

Ontological means of identification and representation of text documents semantics in relation to the problems of interactive information retrieval are considered. The implementations of ontologies operations are presented, which allow forming images of new meanings in the subject area. Taxonomies of relations (paradigmatic and syntagmatic) and entities (polythematic thesaurus of concepts) are used to perform operations, as well as to identify fuzzy relationships (in the case when entities and relationships are specified at different levels of generality and/or expressed by different linguistic constructions). Interactive tools are proposed that use the operation of constructing aspect projections for graph representations of ontologies, which make it possible to reduce the dimension of the graph to a level acceptable from the point of view of display and perception. The possibilities of context-based use of entities and relationships for the development of the search process are considered. This paper discuses the use of ontology graphs of organizational processes as a “navigation map” that provides new entry points in the graphical interface, that is, objects that will be contextually defined patterns of search queries in similarity selection tasks or expert analysis tasks. This allows one not only to increase the completeness and accuracy of information retrieval, but also to make the scheme of search navigation more natural, bringing it closer to the schemes of understanding and synthesis of knowledge.

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Notes

  1. The main provisions and literature reviews on ontologies, the methodology for building and using ontologies, and the approaches to typifying relationships are presented in [1, 2], and [3], respectively.

  2. The xIRBIS Information and Analytical System is used by a number of leading information centers and organizations to create industrial document databases.

  3. The complexity of constructing graph representations of the processes of synthesis of new knowledge is also due to the fact that there are no predefined typical solution schemes.

  4. The conditions of user interaction with the system also obey the Miller principle [17], which reflects quantitative restrictions on the user’s ability to perceive and identify information.

  5. It follows from here that the system must have complete and deep knowledge represented by well-formalized objects. Today, these are thesauri, classifications, taxonomies, and domain ontologies.

  6. An ontology is defined as a set of three interconnected systems: O = ❬Sf, Sc, St❭. Sf is the functional system (objects and relationships of reality), which is defined as Sf = ❬Mf, Af, Rf, Zf❭, where Mf is a set of objects (entities), Af is a set of characteristic properties, and Rf is a set of functional relationships represented by typified situational connections that are typical for the SA, Zf is the law of composition, i.e., the rules and schemes for ordering the objects (for example, the taxonomy of the SA). Sc is the conceptual system, which is defined as Sc = ❬Mc, Ac, Rc, Zc❭, where Mc is a set of concepts of the SA, Ac is a set of signs of systematization of the concepts (the meronomy of SA), Rc is a set of relationships (primarily paradigmatic relationships) that define classes/subclasses, and Zc is the law of composition (representation scheme that defines what concepts will be included in classes as well as in what relationship and in what order they will be included in these classes). St is the terminological system, which is defined as St = ❬Mt, At, Rt, Zx❭, where Mt is a set of terms, At is a set of properties, Rt is a set of equivalence and inclusion relationships as well as linguistic relationships, and Zt is the law of composition (grammar); ≡ – is the operation of comparing the elements of different systems at the level of signs, which ensures their identity in the functional, conceptual and terminological systems.

  7. This definition also reflects the relativity of knowledge. The presence of the ontology of the law of composition in the definition means the possibility of the existence of multi-ontology, i.e., more than one ontology (holistic self-sufficient “conceptualizations” of the SA), which are defined based on one set of entities and relationships.

  8. Examples of ontology operations are also given in [20].

  9. We consider the graph G (V, E) = ❬V, E❭, where V is the set of vertices of the graph, and E is the set of its edges. The graph G has the property of a multi-graph if: \(\exists {{G}_{i}}{\kern 1pt} \left( {{{V}_{i}},{{E}_{i}}} \right) \subset G,{{G}_{j}}{\kern 1pt} \left( {{{V}_{j}},{{E}_{j}}} \right) \subset \)\(G{\kern 1pt} :\,\,{\kern 1pt} \left( {\left\{ {{{V}_{i}}} \right\}\, = \,\left\{ {{{V}_{j}}} \right\}} \right) \wedge \left( {\left\{ {{{E}_{i}}} \right\} \ne \left\{ {{{E}_{j}}} \right\}} \right).\) The graph G has the property of a meta-graph if: \(\exists {{V}_{j}} \in V:\left( {\left\{ {{{V}_{j}}} \right\} \subset \left\{ {V\backslash {{V}_{j}}} \right\}} \right)\).

  10. First of all, this refers to the functional ontology.

  11. It is also a managed context: through the specification of an aspect and/or a parameter of conceptual depth and/or width.

  12. Aspect ideas are one of the methodological foundations of the synthesis of new knowledge [22]. This model of knowledge synthesis as a self-organizing process is based on the structural feature of the system: a complex system can be described using a set of relatively independent aspect representations. The process of decomposition not only identifies and binds components, but also forms the decomposition scheme, i.e., a system of characteristic features, in accordance with which the decomposition is carried out.

  13. At this point, the ambiguity of representation (and, accordingly, the identification of the vertices) becomes obvious: the vertices must essentially have the same name (name of the concept itself), but the content will be different, since the meaning of the concept is also determined by the context formed by related concepts and type of relationships.

  14. The correspondence of relationships and linguistic constructions is presented in [3].

  15. The practical material was prepared by the graduate student of the National Research Nuclear University “MEPhI” T.G. Ismailov.

  16. The ontology is built automatically; however, Figure 3 shows the manually processed image of the graph, where meta-vertices are distinguished manually (tools for display and operations on meta-graphs as part of the xIRBIS AIS are under development).

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Authors and Affiliations

  1. National Research Nuclear University MEPhI, 115409, Moscow, Russia

    N. V. Maksimov, O. L. Golitsina, K. V. Monankov, A. A. Lebedev, N. A. Bal & S. G. Kyurcheva

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  1. N. V. Maksimov
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  2. O. L. Golitsina
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  3. K. V. Monankov
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Correspondence to N. V. Maksimov, O. L. Golitsina, K. V. Monankov, A. A. Lebedev, N. A. Bal or S. G. Kyurcheva.

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Translated by L. A. Solovyova

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Maksimov, N.V., Golitsina, O.L., Monankov, K.V. et al. Semantic Search Tools Based on Ontological Representations of Documentary Information. Autom. Doc. Math. Linguist. 53, 167–178 (2019). https://doi.org/10.3103/S0005105519040046

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