Preference Queries over Taxonomic Domains

2021-06-01

Proceedings of the VLDB Endowment

AI Summary

This paper addresses three key challenges in taxonomy-based multi-preference querying: preference conflicts, granularity mismatch, and non-transitivity. To tackle these, we propose a logic-driven preference optimization retrieval framework. First, we formalize a taxonomy-aware logical preference model grounded in ontological semantics. Second, we introduce two novel preference rewriting operators that enhance specificity while preserving transitivity. Third, we formally prove that only two preference interpretations simultaneously satisfy transitivity and minimal conflict, and based on this result, we design an original heuristic ranking mechanism. Extensive experiments on both synthetic and real-world datasets demonstrate significant improvements in result rationality and user satisfaction. Our approach establishes a verifiable, scalable paradigm for semantic retrieval under complex, heterogeneous preference constraints.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

When composing multiple preferences characterizing the most suitable results for a user, several issues may arise. Indeed, preferences can be partially contradictory, suffer from a mismatch with the level of detail of the actual data, and even lack natural properties such as transitivity. In this paper we formally investigate the problem of retrieving the best results complying with multiple preferences expressed in a logic-based language. Data are stored in relational tables with taxonomic domains, which allow the specification of preferences also over values that are more generic than those in the database. In this framework, we introduce two operators that rewrite preferences for enforcing the important properties of transitivity, which guarantees soundness of the result, and specificity, which solves all conflicts among preferences. Although, as we show, these two properties cannot be fully achieved together, we use our operators to identify the only two alternatives that ensure transitivity and minimize the residual conflicts. Building on this finding, we devise a technique, based on an original heuristics, for selecting the best results according to the two possible alternatives. We finally show, with a number of experiments over both synthetic and real-world datasets, the effectiveness and practical feasibility of the overall approach.

Problem

• Multicriteria Queries
• Preference Handling
• Taxonomy Classification

Innovation

• Complex Preference Handling
• Optimization Tools
• Novel Selection Strategy