Abstract
The paper considers the problem of data enrichment, which is understood as supplying the data with semantics with further introduction of structuring. It also considers the data collected from heterogeneous sources with their subsequent organization in the form of information graphs. A data model is used in the form of a network, the framework of which is objects and relations between them. What is more, as objects, in turn, the relations can be used, and this potentially leads to higher order structures. The data connections and data dependencies are also taken into account.
Providing semantic stability during the data enrichment requires solving a number of problems, among which the search for semantically unstable objects, their classification by types of instability and the identification of ways to overcome the instability. An approach is proposed to solve these problems on the basis of the homotopic type theory. Methods are considered for modifying unstable objects for types of different structures. The paper discusses the possibilities of using the results in information systems that allow to increase the “degree of cognitization” of the data and, in the long term, the transition to cognitive business.
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Acknowledgement
The paper is supported by the grant 18-07-01082 of the Russian Foundation for Basic Research.
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Wolfengagen, V.E., Kosikov, S.V., Ismailova, L.Y. (2019). Data Enrichment with Provision of Semantic Stability. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2018. BICA 2018. Advances in Intelligent Systems and Computing, vol 848. Springer, Cham. https://doi.org/10.1007/978-3-319-99316-4_45
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DOI: https://doi.org/10.1007/978-3-319-99316-4_45
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