In my last post, I described one of the most used applications of Dublin Core Metadata - RDF. In today's post, I will describe second most used applications of Dublin Core Metadata - Web Ontology Language (OWL).

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontology. Ontology is a formal way to describe taxonomy and classification networks, essentially defining the structure of knowledge for various domains: the nouns represent classes of objects and the verbs represent relations between the objects.

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The Dublin Core Schema is a small set of vocabulary terms that can be used to describe different resources.

Dublin Core Metadata may be used for multiple purposes, from simple resource description, to combining metadata vocabularies of different metadata standards, to providing inter-operability for metadata vocabularies in the Linked data cloud and Semantic web implementations.

Most used applications of Dublin Core Metadata are RDF and OWL. I will describe OWL in my next post.

RDF stands for Resource Description Framework. It is a standard model for data interchange on the Web. RDF has features that facilitate data merging even if the underlying schemas differ, and it specifically supports the evolution of schemas over time without requiring all the data consumers to be changed.

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Concept Searching Limited is a software company which specializes in information retrieval software. It has products for Enterprise search, Taxonomy Management and Statistical classification.

Concept Searching Technology Platform

The Concept Searching Technology Platform is based on our Smart Content Framework™ for information governance, and incorporates best practices for developing an enterprise framework to mitigate risk, automate processes, manage information, protect privacy, and address compliance issues. Underlying the framework is the technology to:

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Thesaurus is necessary for effective information retrieval. A major purpose of a thesaurus is to match the terms brought to the system by an enquirer with the terms used by the indexer.

Whenever there are alternative names for a type of item, we have to choose one to use for indexing, and provide an entry under each of the others saying what the preferred term is. The goal of the thesaurus, and the index which is built by allocating thesaurus terms to objects, is to provide useful access points by which that record can be retrieved.

For example, if we index all full-length ladies' garments as dresses, then someone who searches for frocks must be told that they should look for dresses instead.

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Drupal is a quite powerful content management system (CMS) that is similar to competitors like WordPress and Joomla. It is typically installed on a web server, unlike WYSIWYG (What You See Is What You Get) local programs like Adobe Dreamweaver (now part of Creative Cloud) and Microsoft FrontPage.

Drupal is an open source platform, meaning that publicly contributed extensions have been offered to extend functionality of the CMS. Part of the Drupal Core, taxonomy is integral to what web developers and programmers can or could do with the software. Taxonomy is a system of categorization, and Drupal can use taxonomy for a number of different purposes within its framework by using various techniques and tools available for the platform. Here, we will examine the basics of taxonomy in Drupal (what it means, how it’s used, etc.) and the various types of tasks that can be accomplished by taking advantage of taxonomy within the software.

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