REDELEX: A Framework for Relational Deep Learning Exploration

• URL: http://arxiv.org/abs/2506.22199v1
• Date: Fri, 27 Jun 2025 13:05:15 GMT
• Title: REDELEX: A Framework for Relational Deep Learning Exploration
• Authors: Jakub Peleška, Gustav Šír,
• Abstract summary: Recently, Deep Deep Learning has emerged as a novel paradigm wherein RDBs are conceptualized as graph structures.<n>There is a lack of analysis into the relationships between various RDL models and the characteristics of the underlying RDBs.<n>We present REDELEX$-$a comprehensive exploration framework for evaluating RDL models of varying complexity on the most diverse collection of over 70 RDBs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational databases (RDBs) are widely regarded as the gold standard for storing structured information. Consequently, predictive tasks leveraging this data format hold significant application promise. Recently, Relational Deep Learning (RDL) has emerged as a novel paradigm wherein RDBs are conceptualized as graph structures, enabling the application of various graph neural architectures to effectively address these tasks. However, given its novelty, there is a lack of analysis into the relationships between the performance of various RDL models and the characteristics of the underlying RDBs. In this study, we present REDELEX$-$a comprehensive exploration framework for evaluating RDL models of varying complexity on the most diverse collection of over 70 RDBs, which we make available to the community. Benchmarked alongside key representatives of classic methods, we confirm the generally superior performance of RDL while providing insights into the main factors shaping performance, including model complexity, database sizes and their structural properties.

Related papers

• Task-Agnostic Contrastive Pretraining for Relational Deep Learning [0.0]
  We propose a novel task-agnostic contrastive pretraining approach for RDL that enables database-wide representation learning.<n>We implement the respective pretraining approach through a modular RDL architecture.<n>Our preliminary results demonstrate that finetuning the pretrained models measurably outperforms training from scratch.
  arXiv  Detail & Related papers  (2025-06-27T13:18:13Z)
• Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures [50.46688111973999]
  Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data.<n>We present a new blueprint that enables end-to-end representation of'relational entity graphs' without traditional engineering feature.<n>We discuss key challenges including large-scale multi-table integration and the complexities of modeling temporal dynamics and heterogeneous data.
  arXiv  Detail & Related papers  (2025-06-19T23:51:38Z)
• RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases [23.836665904554426]
  RDB-to-graph modeling helps capture cross-table dependencies, leading to enhanced performance across diverse tasks.<n>Applying a common rule for graph modeling leads to a 10% drop in performance compared to the best-performing graph model.<n>We introduce RDB2G, the first benchmark framework for evaluating such methods.
  arXiv  Detail & Related papers  (2025-06-02T06:34:10Z)
• Retrieval-Augmented Generation of Ontologies from Relational Databases [13.160850863758]
  We present RIGOR, Retrieval-augmented Iterative Generation of RDB Ontologies.<n>An approach that turns relationals into rich schemas with minimal human effort is presented.
  arXiv  Detail & Related papers  (2025-06-02T01:10:05Z)
• Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains [92.36624674516553]
  Reinforcement learning with verifiable rewards (RLVR) has demonstrated significant success in enhancing mathematical reasoning and coding performance of large language models (LLMs)<n>We investigate the effectiveness and scalability of RLVR across diverse real-world domains including medicine, chemistry, psychology, economics, and education.<n>We utilize a generative scoring technique that yields soft, model-based reward signals to overcome limitations posed by binary verifications.
  arXiv  Detail & Related papers  (2025-03-31T08:22:49Z)
• RelGNN: Composite Message Passing for Relational Deep Learning [56.48834369525997]
  We introduce RelGNN, a novel GNN framework specifically designed to leverage the unique structural characteristics of the graphs built from relational databases.<n>RelGNN is evaluated on 30 diverse real-world tasks from Relbench (Fey et al., 2024), and achieves state-of-the-art performance on the vast majority tasks, with improvements of up to 25%.
  arXiv  Detail & Related papers  (2025-02-10T18:58:40Z)
• RelBench: A Benchmark for Deep Learning on Relational Databases [78.52438155603781]
  We present RelBench, a public benchmark for solving tasks over databases with graph neural networks. We use RelBench to conduct the first comprehensive study of Deep Learning infrastructure. RDL learns better whilst reducing human work needed by more than an order of magnitude.
  arXiv  Detail & Related papers  (2024-07-29T14:46:13Z)
• Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks [0.0]
  Relation Extraction (RE) is crucial for converting unstructured data into structured formats like Knowledge Graphs (KGs) Recent studies leveraging pre-trained language models (PLMs) have shown significant success in this area. This work explores the performance of fine-tuned LLMs and their integration into the Retrieval Augmented-based (RAG) RE approach.
  arXiv  Detail & Related papers  (2024-06-20T21:27:57Z)
• Serving Deep Learning Model in Relational Databases [70.53282490832189]
  Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
  arXiv  Detail & Related papers  (2023-10-07T06:01:35Z)
• Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study [51.33182775762785]
  This paper presents an empirical study to build relation extraction systems in low-resource settings. We investigate three schemes to evaluate the performance in low-resource settings: (i) different types of prompt-based methods with few-shot labeled data; (ii) diverse balancing methods to address the long-tailed distribution issue; and (iii) data augmentation technologies and self-training to generate more labeled in-domain data.
  arXiv  Detail & Related papers  (2022-10-19T15:46:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.