Overcoming the Generalization Limits of SLM Finetuning for Shape-Based Extraction of Datatype and Object Properties

TL;DR

This paper explores methods to improve small language models' ability to extract complete RDF graphs, especially addressing challenges with rare properties due to data imbalance, and offers practical training strategies.

Contribution

It identifies the long-tail distribution as a key challenge and proposes effective data balancing strategies, including dataset scaling and synthetic data augmentation, for shape-based relation extraction.

Findings

Balanced training sets improve extraction of rare properties

Synthetic data augmentation enhances model performance

Reproducible datasets and code are provided

Abstract

Small language models (SLMs) have shown promises for relation extraction (RE) when extracting RDF triples guided by SHACL shapes focused on common datatype properties. This paper investigates how SLMs handle both datatype and object properties for a complete RDF graph extraction. We show that the key bottleneck is related to long-tail distribution of rare properties. To solve this issue, we evaluate several strategies: stratified sampling, weighted loss, dataset scaling, and template-based synthetic data augmentation. We show that the best strategy to perform equally well over unbalanced target properties is to build a training set where the number of occurrences of each property exceeds a given threshold. To enable reproducibility, we publicly released our datasets, experimental results and code. Our findings offer practical guidance for training shape-aware SLMs and highlight promising directions for future work in semantic RE.