Jennifer D'Souza

A Pathway to General-Purpose Scientific AI: Multimodal Comprehension of Scientific Images

Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve and interpret. The ALD/E-ImageMiner benchmark and ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching Scientific Figures provide 1,951...

Mined from Scientific Literature: Process Schemas for Atomic Layer Deposition and Etching in Materials Science

Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials science, hindering comparison and machine-actionable reuse. We present four domain-expert-reviewed JSON Schemas for ALD and ALE experimental and simulation processes. Cura...

Do General NLP Embeddings Capture Ontological Reasoning?

AVA is introduced, a systematic framework for evaluating whether embeddings distinguish logic-sensitive relational semantics in ontologies and knowledge graphs, and reveals a persistent gap between linguistic representation learning and ontology-level discrimination, challenging the assumption that strong NLP benchmark...

OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques

It is revealed that ensemble composition directly affects the precision-recall trade-off: heterogeneous cross-paradigm ensembles generally improve precision, whereas homogeneous LLM ensembles more often achieve higher overall F1-scores.

When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning

Findings show that model size alone is an insufficient selection criterion for OL and provide empirical guidance for reproducible LLM-assisted ontology engineering and indicate that architecture and model lineage can outweigh nominal parameter count.

The Unreasonable Benchmark