Improving LLM-based Ontology Matching with fine-tuning on synthetic data

• URL: http://arxiv.org/abs/2511.22612v1
• Date: Thu, 27 Nov 2025 16:46:45 GMT
• Title: Improving LLM-based Ontology Matching with fine-tuning on synthetic data
• Authors: Guilherme Sousa, Rinaldo Lima, Cassia Trojahn,
• Abstract summary: Large Language Models (LLMs) are increasingly being integrated into various components of Ontology Matching pipelines.<n>This paper investigates the capability of LLMs to perform ontology matching directly on ontology modules and generate the corresponding alignments.<n>A dedicated fine-tuning strategy can enhance the model's matching performance in a zero-shot setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly being integrated into various components of Ontology Matching pipelines. This paper investigates the capability of LLMs to perform ontology matching directly on ontology modules and generate the corresponding alignments. Furthermore, it is explored how a dedicated fine-tuning strategy can enhance the model's matching performance in a zero-shot setting. The proposed method incorporates a search space reduction technique to select relevant subsets from both source and target ontologies, which are then used to automatically construct prompts. Recognizing the scarcity of reference alignments for training, a novel LLM-based approach is introduced for generating a synthetic dataset. This process creates a corpus of ontology submodule pairs and their corresponding reference alignments, specifically designed to fine-tune an LLM for the ontology matching task. The proposed approach was evaluated on the Conference, Geolink, Enslaved, Taxon, and Hydrography datasets from the OAEI complex track. The results demonstrate that the LLM fine-tuned on the synthetically generated data exhibits superior performance compared to the non-fine-tuned base model. The key contribution is a strategy that combines automatic dataset generation with fine-tuning to effectively adapt LLMs for ontology matching tasks.

Related papers

• Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models [48.83701310501069]
  Large Language Models (LLMs) offer a transformative approach to Neural Architecture Search (NAS)<n>We formulate the search as a sequence of conditional code generation tasks, where an LLM refines architectural specifications based on performance telemetry.<n>We generate a vast corpus of valid, shape-consistent architectures via Abstract Syntax Tree (AST) mutations.<n> Experimental results on CIFAR-100 validate the efficacy of this approach, demonstrating that the model yields statistically significant improvements in accuracy.
  arXiv  Detail & Related papers  (2026-01-13T13:00:30Z)
• Evaluating Embedding Generalization: How LLMs, LoRA, and SLERP Shape Representational Geometry [0.0]
  We study the extent to which spherical linear (SLERP) model-merging mitigates over-specialization introduced by task-specific adaptation.<n>We compare four families of models: non-LLM encoders trained from scratch or fine-tuned for embeddings, LLM-based encoders adapted with parameter-efficient methods (LoRA), LLM-based encoders with LoRA followed by model souping merging into the base weights, and the same LoRA-adapted LLMs merged using SLERP across checkpoints or stages.
  arXiv  Detail & Related papers  (2025-11-16T17:28:06Z)
• SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations [25.18297372152296]
  SOCRATES is a novel two-stage procedure that automates the design of tailored SO algorithms.<n>An ensemble of digital replicas of the real system is used as a testbed to evaluate a set of baseline SO algorithms.<n>An LLM acts as a meta-optimizer, analyzing the performance trajectories of these algorithms to iteratively revise and compose a final, hybrid optimization schedule.
  arXiv  Detail & Related papers  (2025-11-01T19:57:38Z)
• LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding [55.5535016040221]
  LM-Searcher is a novel framework for cross-domain neural architecture optimization.<n>Central to our approach is NCode, a universal numerical string representation for neural architectures.<n>Our dataset, encompassing a wide range of architecture-performance pairs, encourages robust and transferable learning.
  arXiv  Detail & Related papers  (2025-09-06T09:26:39Z)
• Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings [25.565372681837697]
  We introduce a Multi-Granularity Hard-negative (MGH) synthesis framework to generate diverse negative samples with varying levels of similarity with the query.<n>We also propose an Anchor Token Aware (ATA) pooling method to improve text embedding accuracy without increasing model complexity.
  arXiv  Detail & Related papers  (2025-08-31T13:24:48Z)
• SyGra: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data [0.0]
  We present a comprehensive synthetic data generation framework for large language models (LLMs)<n>Our approach employs a modular and configuration-based pipeline capable of modeling complex dialogue flows with minimal manual intervention.<n>The resulting datasets are structured under a flexible schema supporting both SFT and DPO use cases, enabling seamless integration into diverse training.
  arXiv  Detail & Related papers  (2025-08-21T10:35:41Z)
• Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques [14.892995952768352]
  Language Models (LMs) have excelled at tasks like text generation, summarization, and question answering.<n>Their inference remains computationally expensive and energy intensive in settings with limited hardware, power, or bandwidth.<n>Recent approaches have introduced multi LLM intelligent model selection strategies that dynamically allocate computational resources based on query complexity.
  arXiv  Detail & Related papers  (2025-06-06T23:13:08Z)
• Large Language Models are Good Relational Learners [55.40941576497973]
  We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
  arXiv  Detail & Related papers  (2025-06-06T04:07:55Z)
• LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models [20.767947974005168]
  LLM Synthor is a macro-aware simulator that generates realistic micro-records consistent with target macro-statistics.<n>It iteratively builds synthetic datasets to minimize discrepancies between synthetic and target aggregates.<n>It achieves strong realism, statistical fidelity, and practical utility, making it broadly applicable to economics, social science, and urban studies.
  arXiv  Detail & Related papers  (2025-05-20T13:35:38Z)
• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild [84.57103623507082]
  This paper introduces Model-GLUE, a holistic Large Language Models scaling guideline.<n>We benchmark existing scaling techniques, especially selective merging, and variants of mixture.<n>We then formulate an optimal strategy for the selection and aggregation of a heterogeneous model zoo.<n>Our methodology involves the clustering of mergeable models and optimal merging strategy selection, and the integration of clusters.
  arXiv  Detail & Related papers  (2024-10-07T15:55:55Z)
• Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation [83.87767101732351]
  Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. We propose DARec, a sequential recommendation model built on top of coarse-grained adaption for capturing inter-item relations.
  arXiv  Detail & Related papers  (2024-08-14T10:03:40Z)

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.