方法对比

	文本知识图谱构建	命名实体识别 (NER)	关系抽取
领域	文本挖掘	文本挖掘	文本挖掘
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	—	—	—
提出者	—	—	—
类型	Structured knowledge representation pipeline	NLP sequence-labelling task	NLP information-extraction task
开创性文献	Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37.	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.
别名	knowledge graph, KG construction, Bilgi Grafiği Oluşturma (Knowledge Graph)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	semantic relation extraction, İlişki Çıkarma (Relation Extraction)
相关	3	3	4
摘要	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.	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.