Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Konstrukce grafu znalostí z textu× | Rozpoznávání pojmenovaných entit (NER)× | Extrakce vztahů× | |
|---|---|---|---|
| Obor | Dolování textu | Dolování textu | Dolování textu |
| Rodina | Process / pipeline | Process / pipeline | Process / pipeline |
| Rok vzniku | — | — | — |
| Tvůrce | — | — | — |
| Typ≠ | Structured knowledge representation pipeline | NLP sequence-labelling task | NLP information-extraction task |
| Původní zdroj≠ | Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37. DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ | Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106. link ↗ |
| Další názvy≠ | knowledge graph, KG construction, Bilgi Grafiği Oluşturma (Knowledge Graph) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) | semantic relation extraction, İlişki Çıkarma (Relation Extraction) |
| Příbuzné≠ | 3 | 3 | 4 |
| Shrnutí≠ | 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. |
| ScholarGateDatová sada ↗ |
|
|
|