FuDoBa fuses low-dimensional LLM, global knowledge graph, and locally extracted knowledge graph embeddings via Bayesian optimisation, matching or exceeding LLM-only baselines on several benchmarks.
Enriching BERT with Knowledge Graph Embeddings for Document Classification
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abstract
In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classification task. For a more coarse-grained classification using eight labels we achieve an F1- score of 87.20, while a detailed classification using 343 labels yields an F1-score of 64.70. We make the source code and trained models of our experiments publicly available
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FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation
FuDoBa fuses low-dimensional LLM, global knowledge graph, and locally extracted knowledge graph embeddings via Bayesian optimisation, matching or exceeding LLM-only baselines on several benchmarks.