Knowledge Retrieval Systems for Enterprise Service Environments

Authors

  • Siva Hemanth Kolla

Keywords:

Enterprise Knowledge Retrieval Systems, Knowledge Retrieval Architectures, Enterprise Service Environments, Information Retrieval in Enterprises, Enterprise Data Silos, Semantic Heterogeneity, Service-Oriented Architectures, Data Ingestion Pipelines, Enterprise Indexing Strategies, Search-Friendly Data Organization, Knowledge Freshness and Index Quality, Semantic Inference Layers, Enterprise Search Systems, Knowledge-Based Decision Support, Architecture Design Best Practices, Deployment-Oriented Retrieval Design, Enterprise Analytics Enablement, Service Contract–Driven Integration, Expert-Validated Retrieval Architectures, Enterprise Information Systems.

Abstract

Knowledge Retrieval Systems for Enterprise Service Environments: An objective, evidence-based examination aligning with formal structure and academic rigor. Organizations seek to monetize data by applying analytics and using the acquired knowledge to enhance operations and remain competitive. Knowledge Retrieval is a subfield of Information Retrieval that addresses the need to find relevant information across enterprise data silos when a user makes a request for information. Enterprise service environments are characterized by collections of semantically heterogeneous, interoperable application services that communicate via defined service contracts. Enterprise Knowledge Retrieval Systems are designed to support user requests in these contexts. Such systems ingest data from numerous, diverse enterprise data sources and organize it in a search-friendly way. Effectiveness depends on both the quality and freshness of the resulting index.

Research into enterprise Knowledge Retrieval System architecture centers on deployment specifics and design best practices. The method focuses on a design-oriented perspective through formal Knowledge Retrieval System building blocks—analyzing the architecture via data ingestion and indexing pipelines, as well as the semantic inference layer—while reiterating the need for systems that address enterprise deployment scenarios. Analysis of the complete Knowledge Retrieval System architecture leads to the identification of practical best practices. These are validated by expert interviews probing proven Knowledge Retrieval System architectures with a focus on enterprise-specific requirements.

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Published

27.12.2022

How to Cite

Siva Hemanth Kolla. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 495–506. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8037

Issue

Vol. 10 No. 3s (2022)

Section

Research Article

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