# Explaining Weather Bulletins via ILP

arXiv: https://arxiv.org/abs/2607.21184 · HTML: https://mlsanity.com/paper/2607.21184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data](https://mlsanity.com/paper/2202.06913/) (2022)
- [GPU-based parallelism for ASP-solving](https://mlsanity.com/paper/1909.01786/) (2019)
- [Parallel Logic Programming: A Sequel](https://mlsanity.com/paper/2111.11218/) (2021)
- [NeuralFastLAS: Fast Logic-Based Learning from Raw Data](https://mlsanity.com/paper/2310.05145/) (2023)
- [Hierarchical AI-Meteorologist: LLM-Agent System for Multi-Scale and Explainable Weather Forecast Reporting](https://mlsanity.com/paper/2511.23387/) (2025)
- [Data2Concept2Text: An Explainable Multilingual Framework for Data Analysis Narration](https://mlsanity.com/paper/2502.09218/) (2025)

## Abstract (arXiv)

Inductive Logic Programming (ILP) originated within the Logic Programming community in the Nineties as a framework for combining symbolic learning with declarative knowledge representation. Nowadays, mature ILP frameworks exist and they are capable of learning complex, non-monotonic hypotheses, thus broadening both the modeling capabilities and the scope of real-world applications of ILP. This work is primarily based on the FastLAS2 framework and aims to generate simple, interpretable hypotheses to help clarify the weather bulletins issued by OSMER FVG, the Regional Meteorological Observatory of the Italian region of Friuli Venezia-Giulia. In this paper we present a pipeline that, starting from simulated meteorological raw data and from OSMERs' bulletins (used as ground truth), extracts data as ASP facts and generates ILP examples. From such examples an explanatory hypothesis is then inferred via FastLAS2. Such a hypothesis (translated into natural language) explains the weather forecast issued by human experts, and in particular the rationale behind experts' choices of specific symbols in the bulletin pictogram (the symbol-annotated meteorological map of the forecast). The proposed approach is general, not specific to any particular region and it can equally be applied to bulletins from other sources and to different regions.