KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data
arXiv:2606.10358v1 Announce Type: new Abstract: Learning Bayesian network (BN) structure from sparse discrete data is hard: when each instance records only a few variables, most variable pairs lack the joint observations needed for reliable scoring, and data-only methods recover little structure. Imperfect domain knowledge, expressible as a weighted directed knowledge graph (KG), is often available. We propose KG-SoftMAP, which encodes such a KG as a soft, confidence-weighted, data-overridable edge prior and maximizes a MAP objective combining the BDeu score with a logit-form prior; the KG may
The increasing complexity and sparsity of real-world datasets necessitates more sophisticated methods for AI to learn effective models, especially in data-limited environments.
This research enhances the ability to build robust AI models from incomplete or sparse data by integrating imperfect domain knowledge, which is crucial for real-world applications where perfect datasets are rare.
The explicit inclusion of 'soft' knowledge graph priors allows AI systems to more accurately infer relationships in data that would otherwise be too sparse for effective learning, making domain knowledge a more pliable asset.
- · AI/ML researchers
- · Industries with sparse data (e.g., healthcare, specialized manufacturing)
- · AI model developers
- · Companies relying solely on massive, dense datasets for AI development
Improved accuracy and robustness of Bayesian networks in scenarios with limited data.
Faster development and deployment of AI systems in domains previously hindered by data scarcity.
Potential for new AI applications to emerge in highly specialized or sensitive areas where data collection is difficult.
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Read at arXiv cs.LG