📄 “FuDoBa: Fusing Document and Knowledge Graph Based Representations with Bayesian Optimisation” with Boshko Koloski, Senja Pollak, Roberto Navigli and Blaž Škrlj, has been published by Springer,

This framework improves off-the-shelf LLM embeddings (such as OpenAI text-embedding) for classification tasks by incorporating global knowledge from Wikidata KGs and local knowledge learned from task-specific knowledge graphs. High-dimensional fusion can be matched by searching for low-dimensional weighted modality contributions via Bayesian Optimization. Furthermore, tabular foundation models serve as effective surrogates, achieving results comparable to more expensive AutoML runs at a fraction of the computational cost.

📄 Link: https://lnkd.in/dUmENV-e