A Model for Digital Content Management
Filippo Eros Pani, Simone Porru and Simona Ibba
Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, Cagliari, Italy
Keywords: Content Management, Digital Libraries, Taxonomy, Metadata Standards.
Abstract: Digital libraries work in complex and heterogeneous scenarios. The quantity and diversity of resources,
together with the plurality of agents involved in this context, and the continuous evolution of user-generated
content, require knowledge to be formally and flexibly organized. In our work, we propose a library
management system - which specifically addresses the Italian context - based on the creation of a metadata
taxonomy that analyses the existing management standards, connects them, and associates them with the
multimedia content, through a comparison with popular metadata standard employed for User-Generated
Content. The approach is based on the conviction that cultural heritage should be managed in the most flexible
way through the use of open data and open standards that promote knowledge interoperability and exchange.
Our management model for the proposed metadata aims to be a useful instrument for the greater sharing of
knowledge in a logic of reuse.
1 INTRODUCTION
Digital libraries are complex systems that connect
institutional resources and capabilities, but also offer
unparalleled opportunities for new and improved user
services (Schwartz, 2000). These systems have to
guarantee ease of access, sharing, storage and
retrieval of resources that are produced by different
organizations, as well as manage their heterogeneity.
The degree of complexity and richness of information
requires actions in a logic of strong cooperation and
interoperability.
The National Library Service (SBN) is the Italian
libraries network. The ISBN network is composed by
state libraries, council libraries, universities, schools,
academies, and public and private institutions which
operate in different areas. The main goal of this
network is to remove the fragmentation of library and
effectively manage the information that originates
from different types of digital content (books,
audiobooks, ebooks, audio, databases, music,
websites, documents).
As asserted in (Bellahsene et al., 2011), requiring
heterogeneous information systems to cooperate and
communicate has now become crucial, as such
cooperating systems have to match, exchange,
transform and integrate large data sets from different
sources and of different structure in order to enable
seamless data exchange and transformation. This is
also true for a national libraries network.
The purpose of this work is to formalize
knowledge through the creation of a metadata
taxonomy through the analysis and the integration of
existing metadata schemas and the study of the main
digital libraries. In the digital libraries context there
are different resources: some of these are unstructured
or described with different metadata schemas.
Resources integration is a complex activity, since the
quantity of existing metadata schemas is so large as
to make the realization of a single access to the
service difficult.
Our work aims to find a relationship between the
main metadata schemas through their comparison.
The final result is a taxonomy, which provides
innovative metadata with respect to resource
classification, especially ebooks, which nowadays
play a fundamental role in the context digital libraries.
Through the use of the proposed taxonomy, it is also
possible to effectively manage metadata related to
rights management, with the final goal of making it
easier to find the information truly regarded as
relevant by the final user.
The paper is structured as follows. In Section 2 we
propose an overview about the state of the art. In
section 3 we discuss our approach for multimedia
content management, based on the explanation of
each of the three phases on which it is built. In section
240
Pani F., Porru S. and Ibba S..
A Model for Digital Content Management.
DOI: 10.5220/0005512202400247
In Proceedings of 4th International Conference on Data Management Technologies and Applications (DATA-2015), pages 240-247
ISBN: 978-989-758-103-8
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
4 we describe the case study and the structure of the
resulting taxonomy; finally, in section 5 the
conclusions are presented, together with some
reasoning about the future evolution of the work.
2 RELATED WORK
Metadata are used as a means to retrieve digital
objects in a punctual and precise way through a single
access point. Metadata describe structure, features,
conditional use and management information related
to the associated resources. In the digital libraries
context, the metadata have the following features:
identify and find the resources (descriptive metadata),
manage resources and ensure acquisition,
management and use on the basis of existing rights
and licenses (management metadata). Metadata also
describe existing relationships between resource
components, to make the information easily
accessible with a higher granularity level (structural
metadata) (Hill et al., 1999).
Dublin Core (DC) is the most common standard.
Its core consists of 15 elements that are part of a larger
set of metadata vocabularies and technical
specifications maintained by the Dublin Core
Metadata Initiative (DCMI). The essential function of
the Dublin Core is maintained by the DCMI and is
represented by the basic the so-called simple DC (i.e.,
without 'qualifiers'). The DC is also used for the
exchange of metadata according to the Open Archive
Initiative Protocol for Metadata Harvesting (OAI –
MHP) (Lagoze and Van de Sompel, 2003). The need
to express certain values with higher granularity led
to the definition of qualifiers. The full set of
vocabularies (i.e., the DCMI Metadata Terms, also
includes a set of resource classes including the DCMI
Type Vocabulary, vocabulary encoding schemas, and
syntax encoding schemas. The schema can be
extended by defining additional elements
appropriately identified by a prefix that indicates the
schema they belong to. Additional metadata can be
inserted through application profiles, specifically
tailored for the context and not covered by the basic
schema. As the DC is a descriptive metadata schema,
additional technical and management metadata can be
useful for the management of the described resources.
With the Adobe Extensible Metadata Platform
(XMP) is possible to embed metadata into files during
the content creation process. XMP allows for
meaningful content information to be captured (such
as titles and descriptions, searchable keywords, and
up-to-date author and copyright information). It is
freely available because it is an open source standard
since early 2012. XMP is also an ISO standard
(16684-1), and supports many image formats,
dynamic media formats, video package formats,
adobe applications formats, markup formats and
document formats.
Exif standard (Exchangeable image file format) is
an international open-standard used for tagging image
files with metadata, or adding information about the
image. It is supported by both the TIFF and JPEG
formats. When a picture is taken with a digital
camera, Exif data are automatically embedded into
the image. This typically includes the exposure time
(shutter speed), f-number, ISO setting, flash (on/off),
date and time, brightness, white balance setting,
metering mode, sensing method, and information
about copyright and GPS, which is used for
"geotagging" photos.
Different standards are usually not designed for a
combined use. Such problems arise especially with
the dissemination of user generated content found on
social media websites such as Flickr, YouTube, or
Facebook (Suárez-Figueroa et al., 2013). Many
efforts to build ontologies that can bridge this
semantic gap have been done for various applications
(annotation areas, multimedia retrieval, etc.),
sometimes involving different national or
international initiatives.
Many solutions have been proposed to provide a
formal classification that could take into account the
relationships between different multimedia metadata
(Stadhofer et al., 2013). An example for a complex
standard is MPEG-7. MPEG-7 provides a rich set of
complex descriptors that mainly focus on expressing
low-level features of images, audio, and video.
Several approaches have been published
providing a formalization of MPEG-7 as an ontology
(Dasiopoulou et al., 2009); (Hunter, 2003), or the
Core Ontology on Multimedia (Arndt et al., 2007).
Although these ontologies provide clear semantics for
the multimedia annotations, they still focus on
MPEG-7 as the underlying metadata standard. More
importantly, these ontologies basically provide a
formalization of MPEG-7, but do not focus on the
integration of different standards. Ontologies based
on the MPEG-7 standard, like the one proposed in
(García and Celma, 2005), the one proposed in
(Tsinaraki et al., 2004), and the MPEG-7 Upper MDS
(Hunter, 2001) developed within the Harmony
Project, which are all represented in OWL, are not
suitable for an immediate use in the Italian digital
library scenario, both for the higher emphasis placed
on audio and video content than on other multimedia
objects, and for the interoperability issues connected
with the exploitation of the OAI-PMH.
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The Multimedia Metadata Ontology (M3O) is a
possible solution to metadata standard integration
issues (Scherp et al., 2012). The M3O provides a
generic modeling framework for representing
sophisticated multimedia metadata. It allows for the
integration of the features provided by the existing
metadata models and metadata standards.
Another proposed solution is the Media Resource
Ontology, created by the W3C Media Annotation
Working Group. The Media Resource Ontology is an
ontology based on a mapping effort between many
different multimedia metadata standards, including
Exif 2.2, MPEG-7, METS (Gartner, 2002), NISO
(Davis, 2004), and XMP. It is mainly web-oriented,
and, being structured following other standards, does
not analyze the specific elements of the context at
hand.
PICO AP is a DC application profile used by
Cultura Italia (Buonazia et al., 2007). PICO AP is an
XML metadata schema oriented to the exploitation of
OAI-PMH. PICO AP aims at providing metadata
harvesting functionalities also in the presence of
different schemas, so addressing the interoperability
issues.
The MAG (Pierazzo, 2006) schema is an
application profile that interacts with other standards,
namely the Dublin Core, and the NISO (Davis, 2004).
MAG aims to provide formal specifications for the
collection, transfer and dissemination of metadata and
digital data in their archives. MAG schema defines a
metadata taxonomy that can achieve a higher degree
of independence, both from the specific application
context, and from software and hardware. MAG
metadata are defined through the XML format, in
order to be compliant with the OAI-PMH standard.
As an extensible standard, MAG is a suitable
candidate as a starting point for the construction of a
metadata taxonomy.
With respect to mapping, the work by (Euzenat
and Shvaiko, 2013) is certainly worth of
consideration, as we decided to map entities taken
from different classifications. On the other hand, the
FRBR (Functional Requirements for Bibliographic
Resources) (IFLA, 1998) model serve as a guide for
understanding the relationships between metadata
taken from diverse classifications.
3 THE PROPOSED APPROACH
Our aim is to effectively use the reference knowledge
(ontology, taxonomy, metadata schema) to start
classifying the information related to the context of
modern digital libraries.
We propose a model that starts from the
comparison of different classifications of the same
domain. In the second phase, the knowledge is
analysed by pinpointing, among the available
information, what is needed, in order to define a
reference glossary to describe the data.
Thus pinpoint, for each single metadata we found,
where the information can be found. This information
represent the context in which the object is inserted.
Thus, we consider the semantic concept taking the
bias of the context into account.
Starting from this knowledge base (KB), further
refining can be made by re-analysing the information
in different phases: with a first phase, checking if the
information that is not represented by the chosen
formalization can be formalized; with a second phase,
analysing if some information found on the Web sites
can be connected to formalized items; finally, we try
to reconcile these concepts through the refining
phase, presented in section 4.
This is obviously needed only for the information
to be represented. The knowledge that we want to
represent is the one considered of interest by the
users: for this reason, the most important pieces of
information are chosen. The final outcome of the
proposed work is a metadata taxonomy, aimed at
effectively representing the knowledge of interest in
the domain of the digital libraries.
4 CASE STUDY
According to an industrial project concerning the
implementation of Web-based platform for both
library cataloguing and reference services, we
decided to define a taxonomy intended for the
optimization of multimedia object metadata
classification. A metadata taxonomy must support
different organizations that manage the digital
contents in various ways. This taxonomy aims to
create a shared language that helps to lower the
existing barriers between systems and people, so
increasing knowledge retrievability and usability.
Information are often application-centric;
departments and processes are often fragmented. We
want to identify these differences and leverage them
through a cross-mapping between different
vocabularies.
The basic starting concept is the definition of a
KB: in our study, the knowledge base is composed by
all kinds of multimedia objects that a digital library
must manage: ebooks, audiobooks, music, websites,
magazines, images.
We have first analyzed the metadata standards
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used in multimedia content management, and then
defined a taxonomy to represent the semantics of the
multimedia content, finally giving an unambiguous
meaning to each metadata.
4.1 The First Phase: Selected
Metadata, UGC and Direct
Mapping
We used the metadata standards that have been
described Section 2 to have a complete modelling of
the domain of multimedia content properties. Then
we compare these metadata standards with metadata
schemas used for user-generated content. We use this
approach because such standards allow for
cataloguing different aspects of multimedia content.
4.1.1 Selected Metadata
Metadata belonging to the Dublin Core standard are
entirely adopted, since they can represent any type of
digital resource, due to the generality of the elements
semantics. The adoption of the DC standard allows
for the system to be OAI compliant, so that the OAI-
PMH protocol could be used. The XMP standard is
vast, and requires a selection, not only of its schemas,
but also of the metadata included in them. Unlike DC,
XMP represents very specific information, which are
not entirely of interest for the digital library context.
The metadata that are considered are thus the ones
belonging to the following schemas: XMP basic
schema, XMP rights management schema, XMP
paged-text schema, XMP Dynamic Media Schema
and Exif schema. Among those, only metadata
belonging to XMP rights management schema were
taken entirely, as they represent information about the
rights associated to the resource. It was also decided
to include metadata taken from MAG 2.0, an
application profile specifically designed for the
description of digital resources (derived or born
digital). MAG includes structural and administrative
metadata, but does not include a vast set of
descriptive metadata (it only includes the 15 core
elements of DC). This section must be in one column.
4.1.2 User-Generated Content
The cultural information also exists outside of the
institutions that manage the collection of books. One
of our activities involved studying the representation
of User-Generated Content (UGC) (Pani et al., 2014).
YouTube for instance was studied in order to gather
the metadata used for multimedia content, especially
video content; we noticed how it makes use of
different standards (Atom Publishing Protocol,
GeoRSS) as well as proprietary ones (YouTube XML
Schema). YouTube uses feeds, based on XML files,
each of which has its own metadata containing
objects and a web link to the source of the content.
XML schemas used by Youtube are many (Atom
Syndication, Format Open Search, Media RSS
Schema, YouTube XML, Google Data Schema,
Schema GeoRSS, Geography, Markup Language,
Atom Publishing, Protocol Google Data API, Batch
Processing). This large amount determines the use of
a very high number of metadata. Once the metadata
coming from YouTube had been grouped, the
semantics of each and every one of them was
evaluated, and, similarly to what was done for DC and
XMP, only the most representative and interesting
metadata for a digital library were selected.
4.1.3 Direct Mapping
Our next step was the direct mapping between
metadata: same meaning, same format, and same data
type. We represented their correspondences in a table,
so that we could have a clear view of both the
metadata we considered in this first phase as a whole,
and of the way in which the semantics of the elements
overlap. We then chose, where semantics overlapped,
the most suited for our purposes. In the table, direct
semantic correspondence is represented by placing
metadata in the same row, whereas isolated metadata
represent a single semantics. The XMP standard was
not compared in the table because none of its
elements have the same semantics as any of the
metadata shown above.
4.2 The Second Phase: Data Collection,
Grouping, Selection
From the raw data we went up to assign them to more
general categories up to the root node. We analyzed
the specific objects of digital libraries context,
choosing the tags that we considered as the most
suitable for the realization of the taxonomy. The tags
were then identified as labels that constitute the set of
descriptive metadata of a resource. We then searched
for the necessary information to retrieve objects in the
domain. This analysis is divided into 3 steps: data
collection, grouping, and selection. Data collection
has the sole aim to search for multimedia objects (in
reference sites) constituting the reference domain,
analyzing and writing down the characteristics (i.e.,
tags) they possess. Grouping involves assigning the
labels collected in the first phase to different
categories. Lastly, selection consists in choosing tags
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that are considered to be the most suitable candidates
for representation. The frequency with which the
characteristics are shown in reference sites, and the
possible interest that a digital library might have in
considering them, are some of the factors taken into
account when making the choice.
The websites that were used as reference are:
Europeana, Internet Culturale, Cultura Italia, Internet
Archive, Open Library, and Project Gutenberg. These
websites offer an overview of the objects that a digital
library is interested in representing, making it
possible to examine and compare the classification of
those same objects found in portals. The first step was
to list the different types of analysed objects, based
on the name assigned to them by the website. Each
type of element is associated to one of the following
macro-categories: “Image”, “Text”, “Audio”,
“Video”, “Ebook”, “Other”. The macro-category
“Other” groups together metadata belonging to
elements that do not belong to the other labels (such
as metadata belonging to the legal documents group
from the previous sections). Once the nature of the
elements was defined, each group of metadata
describing an element becomes part of the group of
metadata belonging to the nature of that same
element. The importance of this phase is in
understanding how objects are classified and which
information were chosen to represent them. A list of
tags, divided by macro-category, is indeed
appropriate, but after that it is useful to create a list of
tags that uses their semantics to distinguish them,
regardless of their name. In order to avoid duplicates,
a name that reminds of the semantics of that tag is
assigned, while the choice of the most suitable name
is postponed to a later phase. With a list of metadata
by macro-categories, all we had to do was to decide
which tags to keep and which ones to reject,
considering the frequency of their use on the chosen
websites and the importance of each piece of
information for a digital library.
4.3 Refining Phase
This phase involved comparing metadata taken from
the standards analysed during the first phase with the
data collected during the second phase. The purpose
of the comparison was to verify whether all the
characteristics studied during the second phase were
represented by the metadata retrieved during the first
phase. If they were not, new metadata would be
created, either as an extension of the chosen metadata
(DC allows semantics extensions by adding
qualifiers) or as entirely new metadata, creating a new
namespace to include them. The process began with
a mapping phase, followed by the creation of new
metadata. Once the refining phase was completed,
and all available metadata were selected, we started
to design the taxonomy schema.
The first step was to compare the list of tags with
the metadata selected during the previous two phases,
based on their semantics. Thus, tags whose semantics
was not covered by any metadata were identified,
with the aim of creating new metadata specifically
designed for them. Tags with semantics similar to DC
elements, but more precise, were described via new
qualifiers, while tags that could not be encompassed
by the DC standard would be included in a new
namespace called “multimediatype”. For example,
the following new qualifiers were created for the DC
element “dc.identifier”: “isbn”, “LoC”, “dewey”,
“iccd”, where each of them represent a specific code
associated to the digital resource. It is not required to
create one metadata for each code type, but it was
considered wiser to create four qualifiers of
“dc.identifier” for the most relevant codes: ISBN,
LoC, dewey, iccd. For the other codes, the general
“dc.identifier” can be used, and the type of code has
to be specified during insertion. The namespace
“multimediatype”, instead, includes metadata
describing federal documents, publishing
information, institutions (for example, museums and
libraries), and User-Generated Content. After
creating the metadata derived from the second phase,
the capability of any metadata to represent
fundamental concepts needed to be investigated. The
fundamental concepts are, for example, the ebooks
categorization, the definition of “grey literature”
documents, UGCs, and rights management. The
results of our research showed that there were not any
metadata suitable for suggesting the optimal software
or hardware device for the exploitation of a resource,
e.g. an ebook. To overcome this, two new DC
qualifiers were created: “dc.format.testedSoftware”
and “dc.format.testedDevice”. These metadata define
the most suitable software and device through which
the resource can be exploited. Grey literature can be
defined by the level of education of their target users
(thus defining the suggested group of users that
typically use a specific kind of resources), and the
type of document, selected from a list of types
belonging to that category (for example, papers,
theses and scientific research documents). The
metadata are: “dc.audience.instructionLevel” and
“multimediatype.documentCategory”.
The integration of UGC metadata was performed
by focusing on those that featured a single semantics
during the first phase, and selecting the most suited
metadata for the context.
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Under “multimediatype.ugc”, metadata “mail”,
“mediarestriction”, “private”, “error”, “statistics”
were created, for representing information about the
user who provided the resource (“mail”), information
about viewing restrictions (some resources can only
be viewed in some countries), and to qualify the
resource status (e.g., if it is private, only users allowed
by the owner can view it), and also information about
errors and statistics (such as the average rating or the
number of views).
The resulting metadata were used to create the
taxonomy structure. The structure has three branches
departing from the parent node, related to the main
groups of metadata: MAG, DC, multimediatype.
MAG is an application profile with its own structure,
so it does not need to be changed and it could be
entirely included in the taxonomy. The DC, being
composed of simple elements and qualifiers, suggests
a further distinction in two levels: the first is reserved
to simple elements that come directly from the
namespace “dc”, the second to the qualifiers of the
aforementioned elements, among which, the class
“Ebook”, that comprises “testedSoftware” and
“testedDevice” metadata. Those metadata, in fact,
refer only to that type of resource. Multimediatype
metadata can be associated to different types of
resource with no distinction (those in the “general”
category), or to a specific resource. Among those, we
include XMP, that consists of the subclasses Audio,
Text and Video, and Exif, which includes the Image
subclass. This hierarchy makes it possible to quickly
point out the nature of a resource and the position of
the related metadata in the taxonomy, at the moment
in which a resource has to be catalogued, thus
allowing for easily selecting the level of detail, or
which standard to use.
As previously discussed, we found it necessary to
introduce two fundamental metadata that final users
should consider in accessing the resource. These two
metadata are dc.format.testedSoftware and
dc.format.testedDevice: the former suggests an
application that might be used to easily access the
resource, along with some additional information
about the operating systems that are compatible with
the suggested application; the latter gives some
information about the devices which might be used to
successfully access the resource (e.g. a specific tablet,
or smartphone).
Taking advantage of the metadata provided in
(Pani et al., 2014), we also selected a set of metadata,
Figure 1: Taxonomy of metadata defined.
AModelforDigitalContentManagement
245
with the purpose of effectively qualifying UGCs in
the context of digital libraries. The selection is a
concise one, since our objective is to provide a core
metadata set for UGCs. They could easily adapt to the
specific needs of a given library.
5 CONCLUSIONS
We studied a process to identify existing
formalizations and knowledge sources within the
domain of digital libraries, focusing our attention to
multimedia objects. Valuable knowledge was
represented in explicit form through proper
information formalization and codification, with the
aim of increasing knowledge availability through
enhancing interoperability. Our real goal is to make
interesting knowledge available for sharing and reuse.
In order to do this, we focused on interesting
information in domain-specific knowledge, thus
allowing for the formalization of metadata associated
with multimedia objects.
The resulting taxonomy, created on the basis of an
accurate analysis and the exploitation of widespread
standards, provides a descriptive model for the
content management in the context of Italian digital
libraries. In particular, resources such as ebooks,
which have recently become more popular, need not
only an exhaustive description (i.e, proper descriptive
metadata), but also metadata that make them easy to
use. The classification structure proposed in this
paper is thus able to provide information that is
currently essential, because it is impossible to have a
full understanding of the knowledge level of each and
every final user. Metadata that describe the most
suitable software for the effective use of a certain
resource, or that provide information on the most
suitable device for offering the best user experience
for that resource, were introduced, as we considered
those information to be of primary relevance for the
modern digital libraries.
ACKNOWLEDGEMENTS
Simone Porru gratefully acknowledges Sardinia
Regional Government for the financial support of his
PhD scholarship (P.O.R. Sardegna F.S.E. Operational
Programme of the Autonomous Region of Sardinia,
European Social Fund 2007-2013 Axis IV Human
Resources, Objective l.3, Line of Activity l.3.1).
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