FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation

• URL: http://arxiv.org/abs/2507.06622v1
• Date: Wed, 09 Jul 2025 07:49:55 GMT
• Title: FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation
• Authors: Boshko Koloski, Senja Pollak, Roberto Navigli, Blaž Škrlj,
• Abstract summary: We introduce FuDoBa, a Bayesian optimisation-based method that integrates LLM-based embeddings with domain-specific structured knowledge.<n>This fusion produces low-dimensional, task-relevant representations while reducing training complexity and yielding interpretable early-fusion weights.<n>We demonstrate the effectiveness of our approach on six datasets in two domains, showing that our proposed representation learning approach performs on par with, or surpasses, those produced solely by the proprietary LLM-based embedding baselines.
• Score: 43.56253799373878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building on the success of Large Language Models (LLMs), LLM-based representations have dominated the document representation landscape, achieving great performance on the document embedding benchmarks. However, the high-dimensional, computationally expensive embeddings from LLMs tend to be either too generic or inefficient for domain-specific applications. To address these limitations, we introduce FuDoBa a Bayesian optimisation-based method that integrates LLM-based embeddings with domain-specific structured knowledge, sourced both locally and from external repositories like WikiData. This fusion produces low-dimensional, task-relevant representations while reducing training complexity and yielding interpretable early-fusion weights for enhanced classification performance. We demonstrate the effectiveness of our approach on six datasets in two domains, showing that when paired with robust AutoML-based classifiers, our proposed representation learning approach performs on par with, or surpasses, those produced solely by the proprietary LLM-based embedding baselines.

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