Semantic Knowledge Retrieval: A Framework for Enterprise Information System Search

Abstract

In today’s competitive global market, knowledge is a vital asset for 
organizational success, making its effective retrieval crucial. However, 
traditional information systems often struggle to meet user search demands. 
Semantic knowledge retrieval techniques address this by capturing the 
meaning behind user queries and knowledge resources, improving search result 
relevance. This paper proposes a novel semantic knowledge retrieval framework 
that processes natural language queries. It employs pre-processing techniques 
like stop-word removal, stemming, and tokenization to refine input data. Query 
expansion incorporates synonyms to ensure comprehensive results, while a 
semantic similarity approach compares the original and expanded queries to 
prioritize relevance and reduce information overload. Domain ontologies are 
utilized to accurately interpret and execute user requests. The framework is 
designed to enhance enterprise information system searches by delivering 
more accurate and context-aware results. By integrating advanced semantic 
techniques, it aims to improve knowledge retrieval, supporting better decision
making and innovation in a competitive landscape.