Enterprise data and semantic modelling: conceptual model of information technology incident management

ЗаглавиеEnterprise data and semantic modelling: conceptual model of information technology incident management
Вид публикацияJournal Article
Година на публикуване2023
АвториArnaoudova K, Nisheva-Pavlova M
СписаниеAnnuaire de l’Université de Sofia “St. Kliment Ohridski”. Faculté de Mathématiques et Informatique
Том110
Start Page25
Pagination25-36
ISSN1313-9215 (Print) 2603-5529 (Online)
ключови думиenterprise knowledge graph, enterprise semantic layer, hybrid classification model, incident management, ITIL, knowledge management, knowledge representation, ontology, problem management, semantic model
Резюме

Knowledge management methods and their efficient implementation across the organization determine sound and resilient management of processes. This paper studies the semantic integration of enterprise data sources essential to service management processes. Implementing a semantic layer within the enterprise architecture uses various tools, methods, and techniques. The semantic conceptual model unifies and implements intelligent integration of multiple data sources across the enterprise, achieving consistency and more accessible interpretation. Specifically, we draw our attention to incident and problem management within enterprises. We propose an ontology - a conceptual model for the incident management process. The incident ontology presented as an intelligent data integration layer component aims to achieve operational excellence. Besides, this ontology is a fundamental part of the proactive process in problem management. An ontology as a logic-based system supports integrity validation. It infers new, no explicitly modeled facts in the problem domain, thus helping experts better analyze and understand the problem. We discuss the conducted experiment results with the proposed in this article conceptual model using the enterprise knowledge graph platform. It can be perceived as a framework for a query-answering system with components, including ontology schema, data mapping, and classification methods for data graph enrichment.

DOI10.60063/GSU.FMI.110.25-36
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