Evo-DKD: פיתוח אונטולוגיות באמצעות מודלים גדולים

תקציר מקורי באנגליתarXiv:2507.21438v2 Announce Type: replace Abstract: Ontologies and knowledge graphs require continuous evolution to remain comprehensive and accurate, but manual curation is labor intensive. Large Language Models (LLMs) possess vast unstructured knowledge but struggle with maintaining structured consistency. We propose Evo-DKD, a novel dual-decoder framework for autonomous ontology evolution that combines structured ontology traversal with unstructured text reasoning. Evo-DKD introduces two parallel decoding streams within an LLM: one decoder generates candidate ontology edits (e.g., new concepts or relations) while the other produces natural-language justifications. A dynamic attention-based gating mechanism coordinates the two streams, deciding at each step how to blend structured and un