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:
“I seek in books only to give myself pleasure by honest amusement; or if I study, I seek only the learning that treats of the knowledge of myself and instructs me in how to die well and live well.”
—Michel de Montaigne (1533–1592)
“He is not a true man of science who does not bring some sympathy to his studies, and expect to learn something by behavior as well as by application. It is childish to rest in the discovery of mere coincidences, or of partial and extraneous laws. The study of geometry is a petty and idle exercise of the mind, if it is applied to no larger system than the starry one.”
—Henry David Thoreau (1817–1862)