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@article{184649,
        author = {Srinivasa sridhar Kavikondala},
        title = {Knowledge Graph-Driven Product Hierarchy Management in Multi-Tenant Retail Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {2823-2829},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184649},
        abstract = {Managing intricate product hierarchies in multi-tenant retail settings can be very difficult, especially given the growing amount and variety of product data. Knowledge graphs present a viable way to deal with these issues because of their capacity to represent relationships and semantic data structures. With an emphasis on data integration, real-time updates, and personalization, this paper investigates how knowledge graphs can improve product hierarchy management across multi-tenant platforms. According to experimental results, the knowledge graph-based model performs better than conventional techniques in terms of personalized recommendations, search efficiency, and categorization accuracy. This strategy is a workable answer for contemporary retail systems since it not only enhances user experience but also scales well across extensive product catalogues. Scalability and the requirement for ongoing learning in knowledge graph updates are still issues, though. Future directions for enhancing the system's resilience and adaptability in practical situations are discussed in the paper's conclusion.},
        keywords = {AI-based Categorizations, Product Hierarchy Management, Multi-Tenant Retail, Data Integration, Knowledge Graph, E-commerce, and Real-Time Updates},
        month = {September},
        }