Metadata

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Posted by kaori 02/28/2009 @ 00:38

Tags : metadata, information science, sciences

News headlines
New primer introduces Web authors to using RDFa metadata in XHTML - Georgia Straight
By Stephen Hui Are you interested in putting your site on the Semantic Web but don't know how to start? Steven Pemberton of the World Wide Web Consortium and the Netherlands' Centrum Wiskunde & Informatica has put together a primer for you....
Is Your Paid Search Account Structure Optimal? - Search Engine Land
In this table, each keyword in the account has multiple columns of metadata, that when combined with our campaign data, lets us pivot through the information in a much more intuitive way. In essence, this helps us transcend the constraints of the...
Universal Type Server 2.0 targets more font security - MacNN
Universal Type Server stores all fonts and font metadata -- classifications, keywords, and regrouped families -- in a central location on the server, making them more accessible to users. This too helps users ensure their font licenses are all kept...
EveryZing relocates from Cambridge to Woburn - Boston/SF
The technology ensures that every piece of audio and video from a client's website is wrapped in a rich layer of metadata, including a full text output of the spoken word track, so it can be searched and accessed easily and precisely by consumers....
Henry Stewart DAM NY 2009 - Digital Asset Management (DAM ... - PR Web (press release)
Seth Earley, President of Earley & Associates, and a recognized expert in metadata and taxonomy states, "Organizations can benefit from learning about the tangible business benefits of metadata maturity and taxonomy strategy....
Entries tagged with “semantic web” from O'Reilly Radar - O'Reilly Radar
In fact, while Google is famous as a machine-learning company, their initial breakthrough with pagerank was based on the realization that there was hidden metadata in the link structure of the web that could be used to improve search results....
Two alternative approaches - FCW.com
By Alan Joch Like a library's card catalog, information indexes list metadata-based pointers to available information that might reside within and across several agencies. Indexes provide a comprehensive overview of available information,...
$910K Debt Deal for metacarta - Xconomy
Documents filed today with the Securities and Exchange Commission indicate that Cambridge, MA-based metacarta, whose software plots Web news items on digital maps by extracting metadata about locations mentioned in the items, has raised $910000 in...
Cloud providers answer the tough questions - SDTimes.com
Should a customer choose to leave, he or she may generate reports about application metadata as well as the complete relational structure of their data, Coffee added. Salesforce does not act punitively, but customers will still incur switching and...
Content I/O Suite Aims to Solve Common Enterprise CMS Problems - CMSWire
Their existing content migration product, Caliente! has been rebranded as Content I/O Migrate, and helps to move data and metadata between ECM repositories, automating much of the process. Finally, Ligero becomes Content I/O Deliver and has a delivery...

Learning object metadata

Learning Object Metadata is a data model, usually encoded in XML, used to describe a learning object and similar digital resources used to support learning. The purpose of learning object metadata is to support the reusability of learning objects, to aid discoverability, and to facilitate their interoperability, usually in the context of online learning management systems (LMS).

The IEEE 1484.12.1 – 2002 Standard for Learning Object Metadata is an internationally-recognised open standard (published by the Institute of Electrical and Electronics Engineers Standards Association, New York) for the description of “learning objects”. Relevant attributes of learning objects to be described include: type of object; author; owner; terms of distribution; format; and pedagogical attributes, such as teaching or interaction style.

The IEEE working group that developed the standard defined learning objects, for the purposes of the standard, as being “any entity, digital or non-digital, that may be used for learning, education or training." This definition has struck many commentators as being rather broad in its scope, but it should be noted that the definition was intended to provide a broad class of objects to which LOM metadata might usefully be associated rather than to give an instructional or pedagogic definition of a learning object. IEEE 1484.12.1 is the first part of a multipart standard, and describes the LOM data model. The LOM data model specifies which aspects of a learning object should be described and what vocabularies may be used for these descriptions; it also defines how this data model can be amended by additions or constraints. Other parts of the standard are being drafted to define bindings of the LOM data model, i.e. define how LOM records should be represented in XML and RDF (IEEE 1484.12.3 and IEEE 1484.12.4 respectively). This article focuses on the LOM data model rather than issues relating to XML or other bindings.

IMS Global Learning Consortium is an international consortium that contributed to the drafting of the IEEE Learning Object Metadata and endorsed early drafts of the data model as part of the IMS Learning Resource Meta-data specification (IMS LRM, versions 1.0 – 1.2.2). Feedback and suggestions from the implementers of IMS LRM fed into the further development of the LOM, resulting in some drift between version 1.2 of the IMS LRM specification and what was finally published at the LOM standard. Version 1.3 of the IMS LRM specification realigns the IMS LRM data model with the IEEE LOM data model and specifies that the IEEE XML binding should be used. Thus, we can now use the term 'LOM' in referring to both the IEEE standard and version 1.3 of the IMS specification. The IMS LRM specification also provides an extensive Best Practice and Implementation Guide, and an XSL transform that can be used to migrate metadata instances from the older versions of the IMS LRM XML binding to the IEEE LOM XML binding.

The LOM comprises a hierarchy of elements. At the first level, there are nine categories, each of which contains sub-elements; these sub-elements may be simple elements that hold data, or may themselves be aggregate elements, which contain further sub-elements. The semantics of an element are determined by its context: they are affected by the parent or container element in the hierarchy and by other elements in the same container. For example, the various Description elements (1.4, 5.10, 6.3, 7.2.2, 8.3 and 9.3) each derive their context from their parent element. In addition, description element 9.3 also takes its context from the value of element 9.1 Purpose in the same instance of Classification.

The data model specifies that some elements may be repeated either individually or as a group; for example, although the elements 9.3 (Description) and 9.1 (Purpose) can only occur once within each instance of the Classification container element, the Classification element may be repeated - thus allowing many descriptions for different purposes.

When implementing the LOM as a data or service provider, it is not necessary to support all the elements in the data model, nor need the LOM data model limit the information which may be provided. The creation of an application profile allows a community of users to specify which elements and vocabularies they will use. Elements from the LOM may be dropped and elements from other metadata schemas may be brought in; likewise, the vocabularies in the LOM may be supplemented with values appropriate to that community.

There are many metadata specifications; of particular interest is the Dublin Core Metadata Element Set (commonly known as Simple Dublin Core, standardised as ANSI/NISO Z39.85 – 2001), which provides a simpler, more loosely-defined set of elements with some overlap with the LOM, and which is useful for sharing metadata across a wide range of disparate services. The Dublin Core Metadata Initiative is also working on a set of terms which allow the Dublin Core Element Set to be used with greater semantic precision (Qualified Dublin Core). The Dublin Education Working Group aims to provide refinements of Dublin Core for the specific needs of the education community. Details of Dublin Core can be found at the Dublin Core website .

Many other education-related specifications allow for LO metadata to be embedded within XML instances, such as: describing the resources in an IMS Content Package or Resource List; describing the vocabularies and terms in an IMS VDEX (Vocabulary Definition and Exchange) file; and describing the question items in an IMS QTI (Question and Test Interoperability) file. Details of these can be found at the IMS Global website .

The IMS Vocabulary Definition and Exchange (VDEX) specification has a double relation with the LOM, since not only can the LOM provide metadata on the vocabularies in a VDEX instance, but VDEX can be used to describe the controlled vocabularies which are the value space for many LOM elements.

LOM records can be transported between systems using a variety of protocols, perhaps the most widely used being OAI-PMH.

For UK Further and Higher Education, the most relevant family of application profiles are those based around the UK LOM Core . The UK LOM Core is currently a draft schema researched by a community of practitioners to identify common UK practice in learning object content, by comparing 12 metadata schemas.

CanCore provides detailed guidance for the interpretation and implementation of each data element in the LOM standard. These guidelines constitute a 250-page document, and have been developed over three years under the leadership of Norm Friesen, and through consultation with experts across Canada and throughout the world. These guidelines are also available at no charge from the CanCore Website.

ANZ-LOM is a metadata profile developed for the education sector in Australia and New Zealand. The profile provides interpretations of metadata structures and illustrates how to apply controlled vocabularies, especially using the "classification" element. It is supported by detailed examples of learning resource metadata, including regional vocabularies. The ANZ-LOM profile was first published by The Le@rning Federation (TLF) in January, 2008.

The Australian Vocational Training and Education (VET) sector has developed an application profile of the IEEE LOM called Vetadata . The profile contains five mandatory elements and makes use of a number of vocabularies specific to the Australian VET sector. It is aligned with the broader ANZ-LOM profile.

ISRACORE is the Israeli LOM profile. The Israel Internet Association (ISOC-IL) and Inter University Computational Center (IUCC) have teamed up to manage and establish an e-learning objects database.

Other important application profiles are those developed by the Celebrate project and the metadata profile that is part of the SCORM reference model .

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Preservation metadata

Preservation metadata is an essential component of most digital preservation strategies. As an increasing proportion of the world’s information output shifts from analog to digital form, it is necessary to develop new strategies to preserve this information for the long-term. Preservation metadata is information that supports and documents the digital preservation process. Preservation metadata is sometimes considered a subset of technical or administrative metadata.

Preservation metadata stores technical details on the format, structure and use of the digital content, the history of all actions performed on the resource including changes and decisions, the authenticity information such as technical features or custody history, and the responsibilities and rights information applicable to preservation actions.

Preservation metadata often includes the following information: Provenance: Who has had custody/ownership of the digital object? Authenticity: Is the digital object what it purports to be? Preservation Activity: What has been done to preserve the digital object? Technical Environment: What is needed to render and use the digital object? Rights Management: What intellectual property rights must be observed?

Preservation metadata is a new and developing field. The Reference Model for an Open Archival Information System (OAIS) is a broad conceptual model which many organizations have followed in developing new preservation metadata element sets. Early projects in preservation metadata in the library community include CEDARS, NEDLIB, The National Library of Australia and the OCLC/RLG Working Group on Preservation Metadata. Following on from this work, the Preservation Metadata: Implementation Strategies (PREMIS) Working Group created the "Data Dictionary for Preservation Metadata: Final Report of the PREMIS Working Group" which was released in May 2005. The ongoing work of maintaining, supporting, and coordinating future revisions to the PREMIS Data Dictionary is undertaken through the PREMIS Maintenance Activity, hosted by the Library of Congress.

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Metadata discovery

In metadata, metadata discovery is the process of using automated tools to discover the semantics of a data element in data sets. This process usually ends with a set of mappings between the data source elements and a centralized metadata registry.

Metadata discovery is also known as metadata scanning.

Semantic matching attempts to use semantics to associate target data with registered data elements.

Statistical matching uses statistics about data sources data itself to derive similarities with registered data elements.

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Code (metadata)

In metadata, the representation term code refers to, and is used in the name of, data elements whose allowable values can be represented as enumerated lists. Each enumerated value is a string that for brevity represents a specific meaning. For example, for a PersonGenderCode the allowable code valid values might be "male", "female" or "unknown". To be compliant with ISO standards a value meaning or definition must also be associated with each code.

The ISO/IEC 11179 metadata registry standard defines code as a system of valid symbols that substitute for longer values. In general, if the number of codes is small the list of valid codes and their definitions can be transmitted with a document that validates the data. Codes usually have a symbolic meaning that can be understood by a person.

One example code is a set of two letter state codes used in a US postal address. The code MN represents the state of Minnesota. Its equivalent ID using FIPS standards is the number 27. The number 27 would be classified as having a representation term of type Identifier and have the representation suffix of ID.

Another example is the three-letter international airport codes such as 'MSP' for the Minneapolis-St. Paul Airport. Although people use these codes to identify an airport, they would be classified as having a representation term of Code because they contain mnemonic information.

Sometimes identification systems are also called "codes" in common every-day language. For example people frequently refer to a location's "zip code". Because of the lack of symbolic meaning in a numeric value, zip codes would technically be classified under ISO guidelines as an identifier. For example in the NIEM and GJXDM standards a zip code is called LocationPostalCodeID. The concept is Location, the property is PostalCode and the representation term is ID.

It is sometimes difficult to determine if a data element is a code or an identifier. In general identifiers are sequential numbers used to identify a specific item in an identification scheme. If a data element has mnemonic information it is generally classified as a code.

If only two code values are needed, an indicator (Boolean true/false) representation term can be used.

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Source : Wikipedia