simplified according to a given production profile,
through content processing and structure repurpos-
ing concerns. A multimedia repository is available
to both concerns as a way to enhance the book’s con-
tent. Afterwards, the target reproduction platform is
chosen, by specifying the required output format con-
cern. Finally, to increase the flexibility of the produc-
tion architecture, a set of behavioral dimensions can
be filled by interaction and presentation concerns, or
left to be dealt by the target RDBs player. At the end
of the architecture, an RDB is available for the se-
lected reproduction platform and user profile. Having
modular concerns as mechanisms to handle the differ-
ent aspects found along the production process, meets
the production requirements gathered previously.
Each production time user’s specific issues are
supported by the production architecture. At a lower
level, developers define processing tasks for each con-
cern. On top of it, these tasks are aggregated into
production profiles, regarding specific requirements
(e.g., user profile, publisher’s presentation specifici-
ties). Finally, top level users control batch production
of books, selecting appropriate concerns or produc-
tion profiles.
3.1 Content Processing
The increase on production and use of rich contents
requires an efficient, and reliable multimedia content
management. However, this presents unique ques-
tions, such as the wide variety of complex formats, or
the need to associate these with the proper application
information. To handle these issues, the processing
architecture’s first concern deals with different tasks
centred on book content processing. As a wide range
of data formats is potentially available as input (e.g.,
DTB, HTML, PDF, timed text, etc.), an initial con-
tent format normalization task is required. This nor-
malization uses a book content format rich enough
to cover the complex tasks to be applied later, along
the lines of hypermedia reference models (Hardman
et al., 1994).
After this step, content reasoning tasks are per-
formed. These can be classified as manual, semi-
automatic, or even fully automated, depending on the
content’s complexity. For instance, a semantic analy-
sis of a book excerpt is difficult to be performed au-
tomatically, while a syntactic analysis requires little
to none user intervention. Therefore, a multimedia
repository was created, to sustain these tasks on RDB
production, mainly through its multimedia content in-
dexing and retrieval facilities. This eases RDB’s rich
content access and distribution.
Integrating such a repository of semantically in-
dexed media will assist the production of media en-
riched books. Figure 2 illustrates how the content
reasoning tasks were designed. This repository needs
to be able to store both raw (e.g., acquired from the
Web) and processed items (e.g., obtained from clas-
sification and composition components). Moreover, a
multimedia content manager component needs to pro-
vide repository indexing and retrieval facilities.
Composition
Media
Composer
Media Classifier
Multimedia Content
Manager
Indexing Retrieval
Feature
Extraction
Multimedia
Ontologies
Text Classifier
Feature
Extraction
Multimedia
Ontologies
n-Media Classifier
Multimedia Items
Multimedia
Repository
Figure 2: Classifying and storing multimedia items.
The media classifier component aggregates a wide
variety of dedicated classifiers, each one accountable
for a specific media type (e.g., text, image, video,
etc.). Each classifier performs two tasks: extract con-
tent features and create or reuse existing multimedia
ontologies, providing a semantic description for the
media item. The first task is performed by a feature
extraction component, responsible for content reason-
ing at different levels. This task is geared towards text
categorization, understanding portions of an image,
analysing an audio item, or establishing relationships
between different elements.
The multimedia interpretation and annotation ca-
pabilities must be supported either by manual, semi-
automatic, or automated tools. Hence, concerns deal-
ing with semantic multimedia analysis and annotation
must be followed. For instance, in the case of image
annotations, pattern features extraction for edge de-
tection, regions or texture analysis must be employed.
Afterwards, decisions have to be made on how to rep-
resent the extracted features, and describe methods
for their representation. To do this, a multimedia on-
tologies component provides an adequate way for rep-
resenting the generated annotations.
To support these media annotations, this compo-
nent is expected to follow a compliant format and al-
low authoring of semantically annotated documents.
In this context, knowledge is represented with RDF
and ontologies. A set of ontology derived seman-
tic tags must be created to describe annotated media
content features. This component may use new on-
tologies for media-specific domains, but it can also
import and extend already existing ontologies (such
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