Query Expansion and Refinement Techniques for Enhancing Search Relevance in Large Knowledge Bases

Abstract

Query expansion and refinement strategies have emerged as important techniques for enhancing the relevance of search results, particularly in large-scale knowledge bases where the scope and complexity of data can lead to incomplete or ambiguous query interpretations. This paper explores structured methodologies that employ lexical, semantic, and context-based approaches to bridge the gap between a user’s initial query and the extensive set of possible relevant documents. Central to this exploration are methods that leverage term co-occurrences, entity relationships, and hierarchical concept taxonomies to systematically alter and refine queries in ways that capture the user’s underlying intent more effectively. We also examine methods designed to alleviate the adverse effects of synonymy and polysemy, offering mechanisms to expand terms in a query while simultaneously constraining expansions that might introduce noise. The aim is to delineate a set of robust techniques that adapt to the dynamic nature of large knowledge bases, ensuring consistent search precision and recall. The findings presented here are motivated by the goal of creating reusable pipelines for query processing that can operate in real-time or near-real-time settings, thus enabling dynamic interaction and iterative feedback from the user. Such strategies open the door for more accurate search and data exploration, especially when dealing with massive, multifaceted repositories.