Сравнение методов

	Построение графа знаний из текста	Связывание сущностей	Распознавание именованных сущностей (NER)	Извлечение отношений
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	—	2008	—	—
Автор метода	—	Milne & Witten	—	—
Тип	Structured knowledge representation pipeline	NLP knowledge-base grounding task	NLP sequence-labelling task	NLP information-extraction task
Основополагающий источник	Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37.	Milne, D. & Witten, I.H. (2008). Learning to Link with Wikipedia. CIKM (Proceedings of the 17th ACM Conference on Information and Knowledge Management).	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106.
Другие названия	knowledge graph, KG construction, Bilgi Grafiği Oluşturma (Knowledge Graph)	named entity disambiguation, entity disambiguation, entity resolution to knowledge base, Varlık Bağlama (Entity Linking)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	semantic relation extraction, İlişki Çıkarma (Relation Extraction)
Связанные	3	3	3	4
Сводка	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.	Entity linking is a natural-language-processing task that matches ambiguous entity mentions in text — people, places, organisations — to the correct record in a knowledge base such as Wikidata, DBpedia, or a domain dictionary. Surveyed and shaped by Milne and Witten (2008) and later neural approaches reviewed by Sevgili and colleagues (2022), it grounds free text into structured, unambiguous references used in knowledge-graph building and multi-source text analysis.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.	Relation extraction is a natural-language-processing task that detects and classifies the semantic relations that hold between entities mentioned in text. Building on early kernel-based methods (Zelenko and colleagues, 2003) and later neural matching approaches (Baldini Soares and colleagues, 2019), it turns free-form text into structured facts of the form entity–relation–entity.