Knowledge Discovery
Knowledge Extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) 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 (NLP) and ETL (Data Warehouse), the main criteria 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 (reusing identifiers or ontologies) or the generation of a schema based on the source data.
The RDB2RDF W3C group is currently standardizing a language for extraction of RDF from relational databases. Another popular example for Knowledge Extraction is the transformation of Wikipedia into structured data and also the mapping to existing knowledge (see DBpedia, Freebase and ).
Read more about Knowledge Discovery: Overview, Extraction From Natural Language Sources, Knowledge Discovery, Ontology Learning
Famous quotes containing the words knowledge and/or discovery:
“As the Spanish proverb says, “He who would bring home the wealth of the Indies, must carry the wealth of the Indies with him.” So it is in travelling; a man must carry knowledge with him, if he would bring home knowledge.”
—Samuel Johnson (1709–1784)
“It was one of those evenings when men feel that truth, goodness and beauty are one. In the morning, when they commit their discovery to paper, when others read it written there, it looks wholly ridiculous.”
—Aldous Huxley (1894–1963)