Semantic Cartography: Using RDF/OWL to Build Adaptable Tools for Text Exploration

Texts encoded using the Text Encoding
Initiative Guidelines (TEI) are ideally suited
to close examination using a variety of digital
methodologies and tools. However, because
the TEI is a broad set of guidelines rather
than a single schema, and because encoding
practices and standards vary widely between
collections, programmatic interchange among
projects can be difficult. Software that operates
effectively across TEI collections requires a
mechanism for normalizing data - a mechanism
that, ideally, preserves the essence of the
source encoding. Brown University’s Center
for Digital Scholarship will address this
issue as one part of a project, funded by
a National Endowment for the Humanities
Digital Humanities Start-Up Grant, to develop
tools for analyzing TEI-encoded texts using
the SEASR environment (National Center for
Supercomputing Applications).
SEASR is a scholarly software framework
that allows developers to create small
software modules that perform discrete, often
quite simple, analytical processing of data.
These modules can be chained together and
rearranged to create complex and nuanced
analyses. Both the individual modules (or
components, in SEASR’s parlance) and the
chains (or flows) running within a SEASR
server environment can be made available
as web services, providing for a seamless
link between text collections and the many
visualizations, analysis tools, and APIs available
on the web. For literary scholars using TEI,
this approach offers innumerable possibilities
for harnessing the semantic richness encoded
in the digital text, provided that there is a
technological mechanism for negotiating the
variety of encoding standards and practices
typical of TEI–based projects.
The central thrust of Brown’s effort is to develop
a set of SEASR components that exploit the
semantic detail available in TEI-encoded texts.
These components will allow users to submit
texts to a SEASR service and get back a result
derived from the analytical flow that they have
constructed. Results could include a data set
describing morphosyntactic features of a text,
a visualization of personal relationships or
geographic references, a simple breakdown of
textual features as they change over time within
a specific genre, etc. SEASR flows can also
be used to transform parts of TEI documents
into more generic formats in order to use
data from digital texts with web APIs, such
as Google Maps. Because these components
deal with semantic concepts rather than raw,
predictable data, they require a mechanism
to map concepts to their representations in
the encoded texts. Furthermore, in order for
these modules to be applicable to a variety
of research collections, they must be able to
adapt to different manifestations of semantically
similar information. Users need the freedom to
tell the software about the ways in which data
with semantic meaning, such as relationships or
sentiment, are encoded in their texts. To do this,
we will build a semantic map – a document that
describes a number of relationships between 1)
software (in this case, SEASR components), 2)
ontologies, and 3) encoded texts (see Figure 1).

2
Figure 1
The semantic map is modeled on the approaches
of previous projects working with semantically
rich TEI collections, most notably the work
of the Henry III Fine Rolls Project, based
at King’s College London. The creators of
that project developed a method for using
RDF/OWL to model the complex interpersonal
and spatial relationships represented in their
TEI documents (Vieira and Ciula). They use
semantic-web ontologies, such as CIDOC-CRM
and SKOS, to define relationships between
TEI nodes. Vieira and Ciula offer examples in
which TEI nodes that describe individuals are
related to other nodes that describe professions,
using the CIDOC-CRM ontology to codify the
relationship. (Vieira and Ciula, 5) This approach
serves well as a model for mapping some of
the more nebulous concepts of interest to the
SEASR components to the variable encoding of
those concepts in TEI.
The links between the encoded texts and
the SEASR components are forged using
two techniques that exploit the Linked Data
concepts around which SEASR - and many other
emerging software tools - are designed (Berners-
Lee). The first is a RDF/OWL dictionary that
defines the ontology of the semantic concepts
at work in the TEI component suite. RDF/OWL
allows us to make assertions about the concepts
associated with any resource identified by a URI
(Uniform Resource Identifier). Within SEASR,
any component or flow is addressable via a
URI, making it the potential subject of a RDF/
OWL expression. For example, a component
that extracts personal name references from a
text can be defined in an RDF/OWL expression
as having an association with the concept of a
personal name, as defined by any number of
ontologies. The second part of the semantic map
is an editable configuration document that uses
the XPointer syntax to identify the fragments of
a particular TEI collection that correspond to
the semantic definitions expressed in the RDF/
OWL dictionary. In this example, collections
that encode names in several different ways can
specify, via XPointer, the expected locations of
relevant data. When the SEASR server begins
an analysis, data is retrieved from the TEI
collection using the parameters defined in the
semantic map, and is then passed along to other
components for further analysis and output.
In addition to the semantic map and the set of
related analytical components, the planned TEI
suite for SEASR includes components to ease
the retrieval and validation of locally defined
data as they are pulled into analytical flows. One
such component examines which of the TEI-
specific components in a flow have definitions in
the RDF/OWL dictionary. The result is passed
to another component, which uses Schematron
to verify that the locations and relationships
expressed in the semantic map are indeed
present in the data being received for analysis.
A successful response signals SEASR to proceed
with the analysis, while an unsuccessful one
returns information to the user about which
parts of the text failed to validate.
Our approach is different from that of other
projects, such as the MONK Project, which
have also wrestled with the inevitable variability
in encoded collections (MONK Project). For
MONK, this issue was especially prominent as
the goal of the project was to build tools that
could combine data from diverse collections
and analyze them as if they were a uniform
corpus. This meant handling not only TEI of
various flavors, but other types of XML and
SGML documents as well. To solve this problem,
MONK investigators developed TEI Analytics, a
generalized subset of TEI P5 features designed
"to exploit common denominators in these
texts while at the same time adding new
markup for data structures useful in common
analytical tasks" (Pytlik-Zillig, 2009). TEI-A