as distinct, from other text in the post. Knowledge tags A knowledge tag is a type of meta-information that describes or defines some aspect of a piece of information (such as a document, digital image, database table, or web page). Knowledge tags are more than traditional non-hierarchical keywords or terms; they are a type of metadata that captures knowledge in the form of descriptions, categorizations, classifications, semantics, comments, notes, annotations, hyperdata, hyperlinks, or references that are collected in tag profiles (a kind of ontology). These tag profiles reference an information resource that resides in a distributed, and often heterogeneous, storage repository. Knowledge tags are part of a knowledge management discipline that leverages Enterprise 2.0 methodologies for users to capture insights, expertise, attributes, dependencies, or relationships associated with a data resource. Different kinds of knowledge can be captured in knowledge tags, including factual knowledge (that found in books and data), conceptual knowledge (found in perspectives and concepts), expectational knowledge (needed to make judgments and hypothesis), and methodological knowledge (derived from reasoning and strategies). These forms of knowledge often exist outside the data itself and are derived from personal experience, insight, or expertise. Knowledge tags are considered an expansion of the information itself that adds additional value, context, and meaning to the information. Knowledge tags are valuable for preserving organizational intelligence that is often lost due to turnover, for sharing knowledge stored in the minds of individuals that is typically isolated and unharnessed by the organization, and for connecting knowledge that is often lost or disconnected from an information resource. Advantages and disadvantages In a typical tagging system, there is no explicit information about the meaning or semantics of each tag, and a user can apply new tags to an item as easily as applying older tags. Hierarchical classification systems can be slow to change, and are rooted in the culture and era that created them; in contrast, the flexibility of tagging allows users to classify their collections of items in the ways that they find useful, but the personalized variety of terms can present challenges when searching and browsing. When users can freely choose tags (creating a folksonomy, as opposed to selecting terms from a controlled vocabulary), the resulting metadata can include homonyms (the same tags used with different meanings) and synonyms (multiple tags for the same concept), which may lead to inappropriate connections between items and inefficient searches for information