Zero-shot Named Entity Recognition is critical for low-resource domains, yet existing approaches rely on opaque prompting of large language models or dense representations that suffer from polysemanticity. We propose an alternative approach that leverages monosemantic features of Sparse Autoencoders. We introduce SAE-NER, a training-free framework that maps monosemantic SAE feature activations to entity types through direct precision estimation, requiring no supervision or prompting. Experiments across general and biomedical domains show that SAE-NER consistently outperforms trained probing classifiers, with especially a large margin in the biomedical domain (up to +20 F1). Finally, we evaluate the utility of SAE-NER predictions as silver training data for downstream NER models. Using controlled perturbations of gold annotations to simulate realistic annotation noise, we show that false negatives are the primary bottleneck for silver-data quality, outweighing the impact of boundary imprecision and false positives.

Information Extraction (IE) in specialized domains often suffers from a severe cold-start problem due to the high cost of expert annotation. Recent Reverse-IE approaches leverage knowledge graphs to generate synthetic training corpora, but typically assume the availability of an existing knowledge base. In this work, we propose an ontology-driven pipeline for synthetic supervision that removes this requirement. Starting from a formal domain ontology, we introduce a stochastic motif sampling strategy that constructs schema-consistent Knowledge Graph structures with controllable topology, which are then verbalized into natural language. This ontology-first formulation also allows direct control over the data generation process, enabling oversampling of underrepresented entity types or relation patterns. Applied to the IT Heritage domain, our approach produces a fully labeled NER/RE corpus without large-scale manual annotation. Evaluation in a low-resource setting shows that while the synthetic corpus lacks the linguistic diversity of gold data, its scalability produces training sets large enough to alleviate the cold-start problem, making ontology-guided motif generation a practical strategy for domains where gold annotation is limited.

In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. This paper is an initial attempt to automatically generate such data for Information Extraction tasks. We accomplished this by developing a novel synthetic data generation framework called KGAST, which leverages Knowledge Graphs and Large Language Models. In our preliminary study, we conducted simple experiments to generate synthetic versions of two datasets—a French security defense dataset and an English general domain dataset, after which we evaluated them both intrinsically and extrinsically. The results indicated that synthetic data can effectively complement original data, improving the performance of models on classes with limited training samples. This highlights KGAST’s potential as a tool for generating synthetic data for Information Extraction tasks.