Explainable Models for Justifying Search Result Rankings in Ambiguous Knowledge Base Queries

Abstract

This paper explores the design and justification of explainable models for ranking search results in situations where knowledge base queries are ambiguous. Ambiguity often arises when user queries contain limited context, leading to multiple plausible interpretations within large-scale knowledge repositories. The objective is to develop strategies that systematically identify and rank relevant records while providing transparent rationales for why certain results appear in higher positions. Our approach merges algorithmic ranking methods, intuitive interpretability components, and structured knowledge base representations to enhance user trust and understanding of the retrieval process. We examine the interplay between latent semantic structures, data-driven features, and explicit logical constraints that can reconcile ambiguous query terms. The key elements we discuss include the integration of domain-agnostic feature extraction mechanisms, the incorporation of human-understandable rules for interpretability, and the formal modeling of query-to-result relationships. This research expands on foundational work in semantic search and explainable artificial intelligence by focusing on methods prevalent before and around 2019, highlighting the methodological gaps that remain in rendering accurate but justifiable rankings. We conduct an in-depth analysis of the interplay between uncertainty in query interpretation and the algorithmic processes that prioritize relevant knowledge base entries. Our findings aim to advance the creation of robust, interpretable ranking mechanisms that address both performance and user-oriented transparency in search systems.