Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data

2026-02-02

AI Summary

This work addresses the challenge of extracting high-quality features from relational data, where informative attributes are often scattered across multiple tables and existing methods struggle to balance effectiveness and efficiency under complex schemas. The authors propose a novel framework that integrates semantic reasoning from large language models with lightweight statistical signals to efficiently identify high-potential join paths through semantic guidance. By combining an optimized multi-way join algorithm with a joint semantic-statistical feature selection mechanism, the approach achieves high predictive accuracy while significantly improving computational efficiency. Experimental results on public benchmarks demonstrate that the method outperforms state-of-the-art baselines by up to 26.8% in feature augmentation accuracy, while maintaining superior runtime performance.

Technology Category

• Machine Learning: Statistical Relational/Logic Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Machine learning models depend critically on feature quality, yet useful features are often scattered across multiple relational tables. Feature augmentation enriches a base table by discovering and integrating features from related tables through join operations. However, scaling this process to complex schemas with many tables and multi-hop paths remains challenging. Feature augmentation must address three core tasks: identify promising join paths that connect the base table to candidate tables, execute these joins to materialize augmented data, and select the most informative features from the results. Existing approaches face a fundamental tradeoff between effectiveness and efficiency: achieving high accuracy requires exploring many candidate paths, but exhaustive exploration is computationally prohibitive. Some methods compromise by considering only immediate neighbors, limiting their effectiveness, while others employ neural models that require expensive training data and introduce scalability limitations. We present Hippasus, a modular framework that achieves both goals through three key contributions. First, we combine lightweight statistical signals with semantic reasoning from Large Language Models to prune unpromising join paths before execution, focusing computational resources on high-quality candidates. Second, we employ optimized multi-way join algorithms and consolidate features from multiple paths, substantially reducing execution time. Third, we integrate LLM-based semantic understanding with statistical measures to select features that are both semantically meaningful and empirically predictive. Our experimental evaluation on publicly available datasets shows that Hippasus substantially improves feature augmentation accuracy by up to 26.8% over state-of-the-art baselines while also offering high runtime performance.

Problem

• feature augmentation
• relational data
• join paths
• machine learning
• efficiency-effectiveness tradeoff

Innovation

• feature augmentation
• relational data
• large language models
• join path pruning
• multi-way join optimization