Guest post by by Bryan Bell, VP of Enterprise Solutions,Expert System
As companies around the world strive to find better options to manage the inflow of unstructured information, they often turn to classification systems to organize the chaos.
Classification systems give structure to massive volumes of information, with the overriding goal of increasing discoverability. Organizations are working to manage large sets of data by classifying it into hierarchical trees (or taxonomies) based on the commonality of the content. You could say that classification is much like a multi-level sort, grouping similar information across multiple category classes.
Classification systems make it easier to understand and process large volumes of data by assigning sets of rules to a node within a classification tree. Various classification methods are being used to apply knowledge to the nodes via a set of specific rules. The challenge is building and organizing a system in a logical order that covers a multitude of user perspectives–building the proper categories, assigning the proper classification to those categories and describing what belongs in each category.
The development of classification systems and the management of data has quickly become a science. Generally speaking, a classification system will contain several parts: 1) The collection itself, 2) A classification hierarchy (tree) that categorizes documents by topic, 3) Sample documents describing the type of content to be classified within each category/node of the hierarchy and 4) An information platform that drives collection of content from the appropriate data sources and then places the content in the correct category.