手法を比較

	検索拡張生成（Retrieval-Augmented Generation, RAG）	テキストからの知識グラフ構築
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	2020	—
提唱者	Lewis, Patrick et al. (Meta AI / Facebook AI Research)	—
種類	Hybrid retrieval + generation pipeline	Structured knowledge representation pipeline
原典	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.	Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37.
別名	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)	knowledge graph, KG construction, Bilgi Grafiği Oluşturma (Knowledge Graph)
関連	7	3
概要	Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model.	Knowledge graph construction is a text-mining pipeline that turns unstructured text into a structured graph of entities and the relations between them. Drawing on the synthesis of Hogan et al. (2021) and the relational-machine-learning review of Nickel et al. (2016), it represents knowledge as nodes (entities such as people, places, organisations) connected by labelled edges (relations), and serves semantic search, recommendation systems, and reasoning.