DITA and Metadata on an Enterprise Scale

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DITA and Met adat a on
an Ent erprise Scale
CMS/DITA North America | 2019
Kristen James Eberlein

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Kristen James Eberlein
● Chair, OASIS DITA Technical Committee
● Principle content engineer, [A]; owner, Eberlein
Consulting LLC
● 17+ years working with DITA
[A] is the Content Intelligence Service. We help organizations reduce costs and increase the
business impact of content assets.
ke@simplea.com
kris@eberleinconsulting.com

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Agenda
1. Audience poll
2. What is metadata on an enterprise scale?
3. [A] Content Intelligence Framework
4. DITA metadata mechanisms & their strengths and weaknesses
5. Looking beyond the DITA silo
6. Emergent practices for DITA metadata on an enterprise scale
7. Resources

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Audience poll
1. Your DITA content has metadata (not including index terms, filtering
attributes, or @outputclass).
2. You are struggling with how to best implement metadata-enriched DITA.
3. You have successfully implemented a metadata strategy that enables you to
do smart things with your DITA content.
4. You are engaged with other parts of the enterprise on aligning metadata and
semantics for a unified customer experience.

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Metadata on an
enterprise scale

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● Most generally, “information about information”
● In DITA circles, metadata usually means “information that classifies,
describes, and identifies content”
● Primary purposes for metadata
○ Describe the content asset
○ Facilitate content retrieval and dissemination
○ Assist in preservation, retention, and archiving
○ Control access to content
○ Identify ownership of content
What is metadata?

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What is “enterprise scale”?
● Multiple divisions or business units, each with their own tools, processes, people, and
terminology
● Content inevitably siloed within the divisions or business units
● Content authored in multiple formats, using multiple authoring tools
● Content stored in multiple repositories: DAM, CEM, CRM, Web CMS, document CMS,
DITA CCMS, etc.
● Content distributed through multiple channels
● New emergent channels: Voice, chatbots, augmented reality ...
Viable for use in companies with …

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What is enterprise scale? (continued)
● New business drivers, such as plans to commercialize content through syndication,
which requires:
○ Finer-grained access to content (sub-document level)
○ Ability to license content based on particular subjects, rather than the entire
content repository
○ General current state: Metadata disparate, disjointed, inadequate to the
challenges

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What is “metadata on an enterprise scale”?
● Let’s start with assumptions:
○ There will be NO master silo in which all content is stored …
○ We need to work with people “where they are”; that means accepting
that people will use multiple authoring formats, storage and
management applications, delivery platforms, terminology, and
taxonomies.
● And then move to the key questions:
○ How can we bring some order to the chaos?
○ How can make it easier for content to move within the enterprise?
○ How can we respond quickly (but intelligently) to demands for new
channels?

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Metadata on an enterprise scale
○ Metadata that is designed, planned, and implemented to facilitate
movement of content throughout the enterprise
■ Across silos
■ Across different authoring formats
■ Into multiple output formats
○ Metadata that is designed, planned, and implemented to enable a rich,
pleasurable, and faceted experience for content consumers

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[A] Content
Intelligence
Framework

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[A] Content Intelligence Framework
• Separates structure and semantics.
• Structure is handled by the Master Content Model.
• Semantics are handled by the Master Semantic Model.

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What is a Master Content Model?
● A map of how content is created, managed, published, translated, and
archived across the enterprise
● Includes authoring and delivery formats (the different representations
that content takes through the content lifecycle)
● Suggests a lowest-common-denominator content model, which includes
metadata
● Corollary to the IT concept of a master data model
● Not “one model to rule them all”!
Master Content Model

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How does DITA fit into the Master Content Model?
● DITA provides the baseline XML representation. This is the equivalent of a
pivot language.
● In localization, a pivot language:
○ Is an intermediary language for translation. For example, Korean source
is translated to English before it is translated to German.
○ Reduces the number of source language/target language pairs.

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And content authored directly in DITA is most efficient ...
DITA reduces the number of transformations
Source formats
(6)
Representations/Output
formats (7)
Infrastructure Formula
Number of
transformations
● Word
● Google Doc
● Custom
application
● HTML
● Markdown
● Framemaker
● HTML
● HTML5 + Schema.org
● PDF
● Legacy application #1
● Legacy application #2
● In Design
● Chatbot
Without DITA
# source formats
times
# output formats
6 ✕ 7
42
With DITA
# source formats
plus
# channels
6 + 7
13

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Metadata +
structure
Master Content
Model
Metadata-
enriched,
structured
content
Thesaurus
Taxonomy
Ontology
Discoverable
Personalized
Predictive
Master semantic
model
Structured
content
Content
experience
High-level approach ...

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Master semantic model
● Contains the semantic model for the
enterprise
● Includes taxonomies, thesauri, and
ontologies
● Best developed and managed in a taxonomy
management application:
○ Examples: PoolParty, Topbraid,
Semaphore SmartLogic, etc.
Thesaurus
Taxonomy
Ontology

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Metadata component of the MCM
● Provides the connection between the master semantic
model and the Master Content Model
● Defines how metadata will be instantiated in the
structured content
● Outlines the elements and attributes that will be used, as
well as the specific architectural mechanisms used for
metadata
● Outlines strategy for implementing metadata in the
content
○ Inserted at authoring time?
○ Applied by semantic specialists?
○ Automatically-inserted by a semantic application?
Metadata
__________
Content

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Metadata-enriched, structured content
● Maximizes your investment in DITA
● Staged for delivery to a wide variety of
platforms, including some currently unknownMetadata-
enriched,
structured
content

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Content experience
● Highly personalized
● Easy to find “the right information, at
the right time”
● Uses predictive power of natural
language processing
Rich experience for the content consumer
Discoverable
Personalized
Predictive

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DITA metadata &
semantic
mechanisms

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● Elements in topics
● Elements in maps
● <data> elements
● Subject scheme maps
● Classification maps
DITA metadata and semantic mechanisms

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● Element designed to hold subject and lifecyle management
information.
● Most elements reflect IBM needs circa 2000.
● Can be difficult to define and reuse these elements
○ Not all elements can appear multiple times in a topic
○ Lack of wrapper elements
● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps
For more information, see Eberlein,
DITA Metadata, 2013.

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● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps
<prolog>
<copyright>
<copyryear year="2019"/>
<copyrholder>Simple A LLC</copyrholder>
</copyright>
<metadata>
<audience type="executive infoDev IT"/>
<category>CCMS selection</category>
<category>DITA tools</category>
</metadata>
</prolog>
Example

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● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps
Advantages
○ Located in topic and travels with it
○ No dependency on map
○ Can be used for searching within the DITA source
Disadvantages
○ Located in topic and thus difficult to maintain
○ Likely to be inaccurate due to authoring errors and
omissions
○ Labor intensive for content developers

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● The same elements available in <prolog> are available in
<topicmeta>.
● Metadata applied in a map cascades:
○ If applied at the root of a map, it cascades throughout the
entire map
○ If applied at a lower level in the map, to cascades to the
children of the element on which it is applied
● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps

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● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps

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● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps
Advantages
○ Metadata is located in map and cascades to topics during
processing.
○ Easier to maintain
○ Less labor-intensive for content authors
○ Provides a layer of abstraction
Disadvantages
○ Metadata is not located in the topics.
○ Requires a processing step

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● Available everyone
● Can be nested
● Can point to external resource by ID
● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps

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Referenced element
<data id="100"
href="www.taxonomyTool.subject"
scope="external"
format="rdf">
Label for subject
</data>
Referencing
element
● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps

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● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps
Advantages
○ Can be used everywhere
○ Robust specialization base
○ Nestable
Disadvantages
○ Requires special processing

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● A specialized map introduced with DITA 1.2 (2010)
● Can be used to:
○ Define subjects
○ Define taxonomies (hierarchies of subjects
○ Develop controlled values for an attribute or an
attribute and element pair
○ Associate metadata with subjects
○ Define relationships between subjects
● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps

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Advantages
○ Easy to create a list of controlled values
○ Easy to bind a list of controlled values to an attribute or
attribute + element pair
○ Useful for prototyping semantic models
○ Can be extended by using <schemeref> elements
Disadvantages
○ Not suitable for a robust semantic model
○ Controlled values are not usable for attributes that take a
space separated list of values
● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps

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● A classification map is any DITA map that includes the
classification domain.
● The classification domain provides elements for:
○ Referencing subjects defined in a subject scheme
○ Defining relationships between topics and subjects
● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps For more information, see Eberlein,
DITA and Metadata, 2013.

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● Elements in
topics
● Elements in
maps
● <data> element
● Subject scheme
maps
● Classification
maps
Advantages
○ Provides abstraction layer
○ Enables topics to have different metadata applied
depending on the context defined by the map
Disadvantages
○ Makes DITA maps difficult for content authors to work in
○ Requires a new set of DITA map knowledge for content
authors -- or a new role for an information architect to
add metadata to maps
○ No out-of-the-box DITA-OT processing for classification
maps

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Scenarios:
Looking beyond
the DITA silo

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The company wants to deliver marketing,
technical documentation, and service content from
a single portal.
Customers viewing content in the portal should
receive relevant recommendations from all three
streams.
Each content stream has their own taxonomy.
What’s the best approach?
Bridging silos

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Map taxonomies
● Requires the three groups -- Marketing, Service, and TechDoc to become
familiar with each others’ terminology and taxonomies
○ What’s the same?
○ What’s different?
● Requires creating manual mappings between taxonomies
● Example: If a customer is reading marketing content about the super-duper
deluxe widget, the portal should recommend:
○ TechDoc: System overview
○ Service: Replacement parts

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Develop unified taxonomies
● Marketing, Service, and TechDoc decide to standardize their disparate
taxonomies
● Unified taxonomies = Taxonomies that manage relationships and differences
intentionally and explicitly

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Emergent practices:
DITA metadata on
enterprise scale

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Emergent practices
● Develop a Master Content Model (MCM):
○ A map of how content is created, managed, published, and translated
○ Includes authoring and delivery formats (the different representations that content takes
through the content lifecycle)
○ DITA is at the center of the MCM; it serves the equivalent purpose as a pivot language in
the translation and localization environment
● Develop a master semantic model (MSM)
○ A comprehensive model that describes concepts and their relationships
■ Example: Concepts might be “User task,” “Installing”, and “Widget A”
■ Example: “Installing” is part of the larger concept “User tasks”
○ Includes terminology used in different contexts -- for example, marketing, product
development, technical documentation, and service
○ Informs the metadata component of the MCM

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Emergent practices
Use a enterprise-wide application to develop and maintain the master semantic
model
○ Examples:
■ PoolParty
■ Topbraid
■ SmartLogic Semaphore
○ Should be accessible through API
○ Explore auto-classification …

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Emergent practices
Build transformations so that each application in the environment can ingest
applicable taxonomies
○ CRM
○ DITA authoring environments
○ Other authoring environments
○ Web CMS
○ CEM systems
Extend applications to be able to implement appropriate metadata based on the
enterprise classification

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Markup solution
● <data> element with attribute values controlled by an associated
subjectScheme map
● <data> elements stored in dedicated topics and conref’d into DITA source
● Solutions can be more or less elaborate
● Build a solution that fits with the authoring environment and provides most
support for authors

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● Stan Doherty, Getting to First Base: Managing Cross-Organizational Content
with Basic Metadata, CMS/DITA NA 2016.
● Kristen James Eberlein, DITA Metadata, session at CMS/DITA North America
2013.
● Joe Pairman, Create a Smooth & Satisfying Reader Experience using
Metadata-Based Links & Suggestions, tcworld 2017.
● Joe Pairman, Taxonomy Now! Building a stress-resistant knowledge
architecture in your current tools, session at CMS/DITA North America 2017.
● Amber Swope, Taxonomy: When you need to move beyond standard
metadata, easyDITA Ask the Expert Series, July 2012.
Resources

DITA and Metadata on an Enterprise Scale

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