This project builds a graph- and ontology-augmented QA system over AI papers from arXiv by combining GraphRAG for entity/relation-aware retrieval with OntoRAG for schema- and rule-driven reasoning. We construct a heterogeneous knowledge graph of papers, authors, methods, datasets, align it with AI ontologies (subfields, method and dataset taxonomies), and use ontological constraints to normalize terms, resolve synonyms, and support multi-hop inference.