Semantic Technology: Difference between revisions
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'''Semantic Technology''' is an encoding process whereby meaning is stored separately from data and content; this separation provides a fluidity to searches and systems operations that is not found in standard IT.<ref>[http://www.readwriteweb.com/archives/everything_to_know_about_semantic_technology_at_semtech_09.php Semantic Technology Definition]</ref> | '''Semantic Technology''' is an encoding process whereby meaning is stored separately from data and content; this separation provides a fluidity to searches and systems operations that is not found in standard IT.<ref>[http://www.readwriteweb.com/archives/everything_to_know_about_semantic_technology_at_semtech_09.php Semantic Technology Definition]</ref> | ||
Revision as of 00:50, 20 September 2012
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Semantic Technology is an encoding process whereby meaning is stored separately from data and content; this separation provides a fluidity to searches and systems operations that is not found in standard IT.[1]
Overview
Semantic Technologies can appear in a variety of formats, and the scope and application of these programs is still in flux. However, generally speaking, these technologies offer a system, and the tools to support it, which is "meaning centered"; that is, the meaning and implications of data is broken down and stored separately so that the nuance of human language and communication can be more accurately captured and understood by the computer system.[2]
The application for such technology is expanding, and it has widespread possibility. Semantic technologies could hypothetically divine what a company sells from its name alone, or would know "that the West Bank of Jordan is not a financial institution".[3]
Mechanisms
Basically, semantic technology aims to assess the probable intent of the user. In this way, this type of technology involves a set of tools used to analyze text with the purpose of dividing its meaning into formats and standards to enable the codification and integration of information based on the meanings discovered.
There are also standards used to describe the semantic meaning of an unstructured data set, which subsequently structure the data set, and which can then be used in relation to other data sets. Through such standards, like the Resource Description Framework (RDF) and Web Ontology Language (OWL), companies can codify unstructured data.[4]