From TEI to a CIDOC-CRM Conforming Model: Towards a Better Integration Between Text Collections and Other Sources of Cultural Historical Documentation

Introduction
In the last couple of years, there has been a growing interest
towards including into TEI documents information about
the world rather than information concerning the text of the
document to be encoded only. We see examples of this through
recent additions to the TEI standard, e.g. the person element
(TEI P5, sec. 20.4.2), as well as through the work in the
Ontologies SIG since it was established in 2004 (TEI Ontology
SIG WIKI). In the SIG, the topic of discussion is how to
organise this kind of information about the world according to
specific ontologies.
One particularly promising ontology in this context is the CRM
(CIDOC 2003). It has been used together with Topic Maps to
organize information from TEI documents (Tuohy 2006) and
as an attempt to find a solution to the so-called exhibition
problem (Eide 2006). Further, attempts have been made to
formalise a way to connect TEI and CRM documents (Ore
2006).
In this paper, we propose a method for automatic generation
of CRM conforming models based on TEI documents. We will
discuss limitations to this approach, as well as ways these may
be overcome. Our proposed method
The method we propose will include two important steps
that should be possible to implement to any given TEI
document: Mapping and model building.
Mapping
A mapping from the TEI document into a model conforming
with CRM should be created. It will be based on a general
mapping of TEI elements to CRM we are currently developing.
But in TEI, many elements are defined quite loose, and
depending on the way they are used, they may be modelled
differently in CRM. According to the TEI guidelines, tag usage
may be described in the TEI header. Such descriptions may
help to decide which type of modelling is the most appropriate.
Ideally, such a specific mapping should be created based on an
automatic reading of the TEI header. But an element description
in a tagUsage element in the TEI header is in prose and will
generally not be stringent enough to be understood by an
automatic reading (TEI P5, sec. 5.3.4). Human interaction will
be needed. It may be the case that use of the equiv element will
make automatic creation of mappings possible, as a reference
to a certain CRM class may be included as an external link
(ibid, sec. 6.3.4).
Model building
A CRM conforming model based on the TEI document and
populated with all instances of mapped elements should then
be created. This model may be used as a query or a data mining
system where the user looks for interesting structures in the
CRM conforming model alone, as well as in combination with
textual information collected from the TEI source document.
But this model may also be used in connection with other CRM
conforming models, such as museum databases. The
connections will be based on regional or global object
identification, such as authority lists of names and classification
schema. The resulting "super model" may then be used as a
data mining tool based on semantic integration between
heterogeneous resources.
An example of a TEI input document
We are currently developing the building blocks for a
system based on the method described above. The
example text used in our work is taken from a manuscript
describing examinations about geographical matters performed
as court interviews in 1740s, printed in the 1960s (Schnitler
1962). The printed edition was digitised and marked up in a
typical TEI way, with names of places and people as well as
dates tagged. A short paragraph of this tagged version, based
on page 73 in the printed book, is translated to English and
included below.
<p xml:id="s1_24449"> Answ: Named <name type="person"
xml:id="s1_24454"> Ole Nilsen</name>, is born in <name
type="place" xml:id="s1_24457"> Tydals </name> mountains,
Which is in
<name type="place" xml:id="s1_24460"> Norway </name>,
of Sami parents, is 50 years old, married, and having one child;
has mostly dwelled in <name type="place" xml:id="s1_24466"
> Tydals </name> mountains, and now dwelling in the
Norwegian <name type="place" xml:id="s1_24469">
Mærragers </name> mountains. </p>
What is tagged — and what is not
Names and dates are tagged in our document. This means that
many references to persons, places and times are included. But
there are a lot of other references to similar real world entities
that are not tagged, e.g. when words other than proper names
are used to refer to them. In the example above, "one child" is
not tagged, whereas other references to the same historical
person in terms of his or her name are tagged as person names.
Furthermore, events are not tagged in this text. Thus, whereas
the name of a boy who is born and the place of his birth is
tagged, the event connecting these together, i.e. the birth, is not
marked up.
This is common to most TEI documents, and is based on the
text centric tradition of the TEI community. There were good
reasons why this tradition was established, but it may be the
case that for some types of documents, textual references to
e.g. events should be marked up.
A simple CRM model of parts of the paragraph above is
included as Figure 1. The solid lines represent what can be
directly read from XML elements in the TEI document, whereas
the dotted lines shows the parts not based on the TEI markup.
This shows that the information needed to model the connection
between the person and the mountain - that he was born there
- is not tagged in the TEI document. How to find the events
As the models created through the use of our proposed method
is based on the XML tags alone, important information in the
text is not considered when the model is created. Several
possible ways to improve this exist. One is automatic event
detection, as used e.g. in the Perseus project (Smith 2002). The
use of this method too causes some problems. One problem is
that fact that even if the system works quite good for English
material, considerable work remains to be done to create a
similar tool for 18th century Danish, even though the method
is implemented also for smaller languages such as Finnish
(Makkonen 2003). Named entity recognition systems are
developed for modern Danish (Johannessen 2004), but this is
of no use in the common situation where names are already
tagged in the documents. Another problem is the fact that these
kinds of methods will always be imperfect, resulting in either
missed events, false positives, or both of these.
Another way to solve the problem would be to reread the text,
identifying events and tagging them. This would be a reliable
method, but time-consuming. Even a combination of these
strategies, a semi automatic method, would mean quite a lot of
work.
A possible way to solve the problem of event identification is
to use the model. Any person or place in the CRM model has
a link to the name of the person or place, an from the name in
the CRM model to the TEI name element. This means that the
textual distance to other person name elements, place name
elements and date elements can be calculated. Further, it is
possible to locate all words within a certain distance, and all
words between two names.
This is similar to the first approach above. But in addition, we
propose to connect the CRM model to external databases. An
example of this would be if we have a CRM version of a
database based on church book records. In this church book
based CRM model a person may be found with a name similar
to Ole Nilsen, a birth date in a possible range for being a witness
in 1742, and a birth place with a name similar to a place name
mentioned in the text in connection to the person's name. This
external source may then help us to include the E67 Birth event
in our model. This may turn out to be impossible without
manual work, but we hope at least to make the manual work
more effective.
Conclusion
Ageneral observation from our work is that the more
relevant information types is marked up in a TEI
document, the easier it is to use automatic methods to generate
CRM conforming models. But even a limited tagging with only
names and dates marked up do help. We will continue our work
on the implementation of a system based on the method
described in this abstract. We believe this will improve the
usability of TEI documents as information sources as well as
simplifying the process of adding more information, such as
event elements, into such documents.
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