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Everything about Knowledge extraction

Creation of knowledge from structured and unstructured sources

Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (IE) in natural language processing (NLP) and extract, transform, load (ETL), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Knowledge extractionA 5-minute orientation
  2. Skim a recent paper: Knowledge Graph Construction: Extraction, Learning, and EvaluationInfluential work since 2022
  3. Pick up a classic book: The nature and design of ChristianityThe most-reprinted book on the subject
  4. Read the landmark paper: Automatic knowledge extraction from documentsCited 77 times
  5. Go sideways: DBpediaA closely related topic
  6. Go sideways: Linguistic Linked Open DataA closely related topic

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🏆 Landmark papers (most cited)

🆕 Influential since 2022

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