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Descriptive metadata is typically used for discovery and identification, as information to search and locate an object, such as title, authors, subjects, keywords, and publisher. Structural metadata describes how the components of an object are organized. An example of structural metadata would be how pages are ordered to form chapters of a book.
A good example of metadata is the cataloging system found in libraries, which records for example the author, title, subject, and location on the shelf of a resource. Another is software system knowledge extraction of software objects such as data flows, control flows, call maps, architectures, business rules, business terms, and database schemas.
A type of structural and metadata encoding system using an XML Document Type Definition (DTD) was the result of these efforts. The MoAII DTD was limited in that it did not provide flexibility in which metadata terms could be used for the elements in the descriptive, administrative, and structural metadata portions of the object. [5]
Another aspect of email privacy is the privacy risk that arises from embedded file metadata in email attachments. Such metadata can divulge privacy compromising data, both to unauthorized parties that gain access to the email message, as well as to the intended recipient of the email message.
Metadata tags as described in this article should not be confused with the use of the word "tag" in some software to refer to an automatically generated cross-reference; examples of the latter are tags tables in Emacs [15] and smart tags in Microsoft Office. [16]
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The format of an email address is local-part@domain, where the local-part may be up to 64 octets long and the domain may have a maximum of 255 octets. [5] The formal definitions are in RFC 5322 (sections 3.2.3 and 3.4.1) and RFC 5321—with a more readable form given in the informational RFC 3696 (written by J. Klensin, the author of RFC 5321) and the associated errata.
The ISO/IEC 11179 model is a result of two principles of semantic theory, combined with basic principles of data modelling. The first principle from semantic theory is the thesaurus type relation between wider and more narrow (or specific) concepts, e.g. the wide concept "income" has a relation to the more narrow concept "net income".