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Visual Contextualisation of Digital Content
Introduction VICODI (www.vicodi.org) is a
collaborative RTD project carried out by seven
partners from six European countries under the
5FP IST programme of the European Union. The main
aim of this project is to develop a novel visual
contextualisation environment for digital content
on the Internet. The VICODI system will
demonstrate the benefits of semantics by
improving searching and navigation in historical
databases. The development of a visualisation
and contextualisation environment for digital
content addresses the management of searching
and retrieval as well the management of
information presentation in two ways. First by
creating an open knowledge space that can
be enhanced by its users and, second, by
providing an innovative interface that employs
Scalable Vector Graphics (SVG) for the
presentation of information. In order to achieve
this the creation of a history ontology was
required.

• Historical Time To deal with the complexity of
  time we have interval times and an event centric
  ontology. This means that instances with a
  time-dependent relation are connected using an
  event with an existence time which represents the
  validity of that connection. For the VICODI
  prototype the intervals are precisely defined,
  although a novel fuzzy temporal model has now
  been devised (reference) and its use is being
  explored for future follow-on projects.
• Historical sources To get round the lack of
  general repositories we decided to build our own
  ontology of history based upon our empirical
  deductive analysis of a 2000 document corpus.

The second authoring tool is the expert
annotation tool (see Fig. 4) which is used to
insert and adjust the context settings of the
VICODI historical resources. This tool also
allows for every resource document to be
described by ontology instances. The
contextualisation engine will provide a starting
set of relevant ontology instances which the
expert historical editors can correct and/or add
new instances if required.


Fig. 4 The expert annotation tool interface
Management System of Knowledge Space The
Management System of Knowledge Space (MSKS)
component is the core of the VICODI architecture.
It provides for the continuous storage and
management of both the ontology and the
contextualized historical documents (repository).
The MSKS is based on KAON for the ontology
storage and management.
Fig. 2 The six basic concepts (flavours) of
our shallow concept hierarchy.
Fig. 1 VICODI portal enhanced with a visual
contextualisation system devoted to European
history
Context Engine, Transformation Engine and
Multilinguality The context engine uses text
categorization to build correlation scores
between documents and the notions in the VICODI
ontology. This allows the system to enhance the
documents' visualization and linkage to give the
users a faster and more intuitive understanding
of a document's position among the notions
represented in the ontology. The transformation
engine processes the data of the relevant
contextual information from the context engine
and outputs it by either transforming it into SVG
instances (dynamic maps) or by generating
hyperlinked (contextualised) documents for
display in the user interface. The transformation
engine will support both graphics-based
visualisation and text-based presentation of the
contexts. Another novel aspect of VICODI is the
multilinguality offered by Systran.B The KAON
framework provides a lexical layer on top of a
language-independent ontology core for each
language. Using that feature it is possible to
translate the language-dependent part of the
ontology without disturbing its logical
structure. Historical information will be
translated via an automatic translation tool into
English, German, French and Latvian.
SVG enabled GUI and Knowledge Portal VICODI has
created a web portal with a graphical
contextualisation interface, which uses Scalable
Vector Graphics (SVG) to visualise digital
content with the help of historical (period)
maps. The portal's web application connects and
integrates all the various system components.
This interface is also the basis of a new
knowledge portal entitled eurohistory.net (see
Fig. 1). The portal contains a number of
innovative elements stimulating user interaction
with its contents. Users can paste their
history-related texts in a special
contextualisation box and have this information
automatically processed and classified on the
basis of LATCH (Location, Time, Category) (Wurman
et al 2000). Moreover, textual information is
visualised on a map of Europe from the
corresponding historical period. European history
terms (listed in VICODI ontology) are
automatically highlighted and their contextual
relevance is marked. The ontology search and
browsing may be carried out either by Yahoo-type
browsing or by Location (SVG maps), Time (decades
from 1000-2000AD) and/or Subject (historical
topics). The portal also provides web-based tools
for the uploading and authoring context of new
historical content.
History ontology building The main purpose of the
history ontology for the VICODI project is to
help machine algorithms in the automatic
contextualisation task by storing relevant
historical knowledge in machine processable form.
In order to achieve this goal an ontology with a
well-defined formal semantics is needed. The task
of devising an ontology of history is very
daunting. On the one hand, it is always
challenging to build an ontology covering a broad
and very complex area of knowledge. On the other
hand, history has several unique features which
are problematic from an ontological point of view.

• Problems
• The complexity of history is immense and
  requires an almost unlimited number of instances
  and property relations. To complicate matters
  historians do not focus only on what questions
  but also on when, where, who, how and
  most importantly on why questions.
• Historical time is uncertain and often debated.
  It includes many unknown dates, imprecise
  intervals (ca., approximately from to etc.), and
  overlapping time (historical periods and events
  extending into each other without clear start and
  end dates). Moreover, many ontology relations are
  time-dependent.
• Historical Sources there are no comprehensive
  and large-scale thesauri of history. During the
  evaluation of related works in the area we
  realised that existing approaches to historical
  ontologies were not suitable for our purposes.
  Some used non-formal, "intuitive" taxonomies,
  which mixed various, semantically different
  hierarchical relationships ("is-a", "part-of",
  "member-of") which made them unsuitable for
  machine processing (like Hassett or the UNESCO
  thesaurus). Others covered only a tiny area of
  history (like the Getty location names) which was
  too limited for our goals. Finally, the CIDOC CRM
  ontology standard has a formal conceptual
  hierarchy. However it is too complex and
  inflexible for our domain experts to fill it with
  the necessary domain knowledge (instances) which
  it does not presently contain.A
• Our solutions
• Complexity We use a shallow concept hierarchy
  starting from only six basic concepts (called
  flavours), which are meaningful for
  domain/history experts person, artefact, group,
  event, abstract notion and location. The
  hierarchy below these concepts is shallow (2-3
  levels), stops at an abstraction level which is
  already
• meaningful for historians, but is still general
  enough to make the place of new instances in the
  ontology easy to find, which speeds up the
  population of the ontology with new historical
  knowledge. The complexity of history is
  represented by connecting instances of these
  flavours by various property relations.

• Conclusions
• VICODI will provide an example of how a
  visualisation and contextualisation environment
  for humanities digital content can be build. Some
  of the most important results are
• The creation of a usable and extensible European
  history ontology.
• Complexity within the history ontology can most
  easily be achieved by a shallow concept hierarchy
  and property relations.
• The capability of KAON to provide programmatic
  access to the ontology makes it possible to mass
  upload instances with the aid of textual
  glossaries or Excel sheets.

Authoring tools VICODI has two authoring tools.
The first is a revised ontology editor based on
the KAON ontology. This is an extension of the
W3C RDFS standard and developed by FZI, one of
the project partners (Maedche et al, 2003). The
tool allows scalability for editing ontologies,
as well as incorporating some usability issues
related to ontology management. The editor
provides several windows for representing the
ontology and tools for editing and adding
concepts, instances and property relations (see
Fig. 3).
References Maedche, A. et al, 2002. A Conceptual
Modeling Approach for Semantics-Driven Enterprise
Applications, Proceedings of the First
International Conference on Ontologies, Databases
and Application of Semantics (ODBASE-2002),
Springer, LNAI. Motik, B. et al, 2003. A Fuzzy
Model for Representing Uncertain, Subjective and
Vague Temporal Knowledge in Ontologies,
Proceedings of the Second International
Conference on Ontologies, Databases and
Application of Semantics (ODBASE-2003), Springer,
LNAI. Wurman, R.S. et al, 2000, Information
Anxiety 2, Que, Indianapolis. Ahttp//cidoc.ics.f
orth.gr/official_release_cidoc.html BSYSTRAN,
White Papers, MT Summit 2001, 2002, 2003,
http//www.systransoft.com/Technology/WhitePapers.
html
Fig. 3 The user interface of the ontology
editor.
Thanks to KAONs capability to provide
programmatic access to the ontology it is also
possible to add a huge number of instances and
concepts to the ontology by processing textual
glossaries or Excel sheets. This - together with
our simple and intuitive concept hierarchy -
significantly sped up the ontology populating
process, as the history domain experts could use
their preferred software tools while codifying
their knowledge. The manual functions of the
ontology editor are therefore only needed to
carry out some advanced operations, like
relocating existing concepts and instances,
adding new connections and visualising the
existing ontology structure.