Proximity Measure of Information Object Features for Solving the Problem of Their Identification in Information Systems

Computer Science > Artificial Intelligence [Submitted on 17 Feb 2026] Proximity Measure of Information Object Features for Solving the Problem of Their Identification in Information Systems Volodymyr Yuzefovych The paper considers a new quantitative-qualitative proximity measure for the features of information objects, where data enters a common information resource from several sources independently. The goal is to determine the possibility of their relation to the same physical object (observation object). The proposed measure accounts for the possibility of differences in individual feature values - both quantitative and qualitative - caused by existing determination errors. To analyze the proximity of quantitative feature values, the author employs a probabilistic measure; for qualitative features, a measure of possibility is used. The paper demonstrates the feasibility of the proposed measure by checking its compliance with the axioms required of any measure. Unlike many known measures, the proposed approach does not require feature value transformation to ensure comparability. The work also proposes several variants of measures to determine the proximity of information objects (IO) based on a group of diverse features. Comments: 14 pages, 12 figures Subjects: Artificial Intelligence (cs.AI) MSC classes: 62H30, 62P20, 68T10 ACM classes: H.3.3; I.5.3 Cite as: arXiv:2604.04939 [cs.AI]   (or arXiv:2604.04939v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.04939 Focus to learn more Submission history From: Volodymyr Yuzefovych [view email] [v1] Tue, 17 Feb 2026 19:10:18 UTC (1,097 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)