Text Encoding and Ontology - Enlarging an Ontology by Semi- Automatic Generated Instances

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Text Encoding and
Ontology – Enlarging
an Ontology by Semi-
Automatic Generated
Instances
Amélie Zöllner-Weber
amelie.zoellnerweber@gmail.com
University of Bergen, Norway
In this contribution, we present an application
that supports users when working with
ontologies in literary studies. Thereby, semi-
automatic suggestions for including information
in an ontology are generated. This application
is meant for users, who are familiar with
annotation and markup and are interested in
topic of literary studies.
When reading literature we can identify literary
phenomena but we cannot prove them directly
in the text. Our ability is to puzzle sentences
together so that they form a meaning. But
this process happens in our mind not in texts.
However, these interpretations are individual
and can differ from reader to reader since
they are influenced by our cultural and social
background. It is therefore a challenge to create
a model of these interpretations to be able to
have a more general and formal description, e.g.
of a character.
In computer philology, one can detect several
applications when modeling texts: 1) by using
mark up languages like XML (meta) information
can be marked in texts (e.g. Jannidis et al.
2006, Meister 2003), 2) one can model theories
in literary studies that try to represent mental
representations (Jannidis 2004, Schneider
2000). However, text structures and mental
representations can differ from each other so
that we are not able to model them in the same
way.
In Zöllner-Weber 2007, mental representations
have been modelled by an ontology. It tried
to regard a character as a complex cognitive
entity in the reader’s mind. Here, the description
of literary characters has been realised as
an ontology. For manipulating this ontology,
users have to extract information manually
about characters from literary texts and add
them to the ontology. This process might
be time-consuming, and users who are not
familiar with the structure of an ontology
might need even more time to become familiar
with the application. We want to solve this
problem by combining text encoding and the
ontology. Therefore, an annotation system has
been developed, which takes the mark up
from the text and generates semi-automatically
suggestions of instances be included into the
ontology.
1. Methods
For the description of literary characters,
an ontology that models characters by their
mental representations was used (Zöllner-
Weber 2006). Briefly, an ontology is a hierarchy
of classes. In addition, the classes contain
instances that represent individuals. Properties,
which contain additional information, are
attached to the individuals (Noy et al. 2001).
By using this kind of structure, information
is described formally. We chose an ontology
because its structure corresponds to the mostly
hierarchical structures of proposed theories
to describe or analyse literary characters
(Jannidis 2004, Lotman 1977). Several theories
of literary characters are combined to create
a base of a formal description using an
OWL ontology (Grigoris and Harmelen 2003,
Jannidis 2004, Nieragden 1995). The frame
of mental representations is presented by the
main classes of the ontology, e.g. inner and
outer features, actions on other characters and
objects. The sub classes contain characteristics
of special characters (special features or groups
of characters). We decided to include single
pieces of information gained from literary
texts into instances of the ontology. In
addition, so-called instances of the classes
represent individual and explicit objects of
the domain of literary characters. Here, direct
information about a character given in a text
is assigned to an instance. Properties contain
additional information, e.g. type of narrator,
author, annotation information or reference
to literature. Together with the information
of the class hierarchy, instances and their
properties, a single mental representation of
a character is modelled (cf. Figure 1). In this

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approach, individual description, the pre-step of
interpretation, is focused. The main description
categories secure a general classification so that
it is also possible to compare two different
interpretations of one character, which might be
spread over different categories of the ontology.
In order to fill the aforementioned ontology of
literary characters in a more automated fashion
an encoding scheme has been developed. For
the annotation, we selected tags of the TEI-DTD
(Text Encoding Initiative 2003,
http://www.te
i-c.org/
), which were developed for marking
interpretation sections in texts. Thereby, the
encoding scheme had to be exploited and
rearranged so that it is usable for literary
studies. This means that the usage of elements
was enlarged. By using this special markup,
a user can directly add interpretive pieces of
information about a literary character to a text.
Here, the annotation scheme is based on four
main categories,
description
,
statement
,
action
,
and
speech
, which classify pieces of information.
All descriptions about a character that are stated
by a narrator are subsumed under
description
.
The category
statement
depicts commentaries of
a character about another character. To mark
non-verbal and verbal actions of a character,
the categories
action
and
speech
should be
used. In addition, a user should add e.g.
information about the type of narrator, the name
of a character and depending on the chosen
category additional information to complete the
annotation. After the process of annotation, a
user sends the marked texts via a web form to a
server where the annotations are evaluated by an
in-house developed programming algorithm (cf.
Fig 1). The pre-sorting of encoded information
about a character is based on the four categories,
which match the main classes of the ontology.
If further encoded information is given by
the markup, the algorithm tries to generate
a further sub-classification. Figure 1 depicts
an example of this process. After successful
processing, a user is presented with a list
for all processed annotations that probably
form instances. Additionally, for all of these
suggestions a class assignment is also given.
Figure 1
In addition, we present surrounding classes
by showing an extracted list of classes of the
ontology so that a user is able to inspect the
environment of the new instance and its class.
Whether a class that should include a new
instance does not exist yet, a user can also add
a new class. Afterwards, (s)he can include the
instance in the new class.
2. Results
The application has been tested by using
an extract of the novel “Melmoth the
Wanderer” (1820), written by Charles Robert
Maturin. We encoded the text with the
mentioned TEI-DTD and afterwards, by using
the programming algorithm, we obtained
suggestions for new instances. In figure 2, the
process of generating an instance from a text
passage is shown as an example. For the main
character Melmoth 72 instances were generated
and assigned to the ontology.
Figure 2

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3. Conclusion
In this contribution, a system has been
presented that includes information into an
ontology, which is generated from markup. We
tested this application by using an ontology for
literary characters. In comparison to the manual
manipulation of the ontology, the application
comprises a semi-automatic generation of
ontology instances and supports the user when
assigning this information about a character to
classes of the ontology. In addition, it is not
only possible to add information about a single
character to the ontology, but the application
can simultaneously process annotations of
several characters. Thereby, time and work can
be saved, as the whole text can be annotated
at once and will then be transferred to the
ontology. There is no need to go back and
forth between text and ontology as for the pure
manual insertion of character information into
an ontology.
Ontologies and their applications are often
linked to logical reasoning. However,
incorporating such techniques into the present
application might be difficult, especially for
untrained users, as shown elsewhere (Zöllner-
Weber 2009).
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