EviOntoKG: Paper-Specific Ontology Induction for Evidence-Grounded Multimodal Scientific Knowledge Graph Construction

Constructing scientific knowledge graphs from academic papers is challenging because critical evidence is distributed across sections, tables, and formulas, and the meaning of individual claims often depends on the overall logic of the paper. Existing methods typically rely on schemas shared across papers or organize semantic structure only after extraction, making it difficult to adapt knowledge extraction to the specific context of each paper. We propose EviOntoKG, a two-scale framework for paper-level scientific knowledge graph construction. Inspired by the way researchers read scientific papers, EviOntoKG combines fast reading to reconstruct a paper-wide research chain with slow reading to analyze local multimodal evidence from text, tables, and formulas, thereby inducing a paper-specific ontology that is finalized before extraction to guide modality-specific extractors. On a dataset of 720 scientific papers, EviOntoKG achieves the best performance across all nine graph-quality metrics and all five downstream QA metrics, including 0.968 claim reliability, 0.741 source coverage, and 0.458 supported QA, the latter representing a 20.6% relative improvement over the best-performing baseline; it also achieves the highest cross-modal correspondence among all evaluated methods. Ablation studies further demonstrate the effectiveness of the major design choices in EviOntoKG.