MIHS Text Mining Historical Sources using Factoid

  1. 1. Sharon Webb

    Maynooth University (National University of Ireland, Maynooth)

  2. 2. John G. Keating

    Maynooth University (National University of Ireland, Maynooth)

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This paper provides an overview of methods used in
bespoke software, MIHS (Mining Interactive Historical
Sources), currently under development. This paper
considers associational culture and the development
of Irish nationalism through the anthropological idea of
Othering [1] and how customised software aids knowledge
construction and historical research.
A brief overview of the historical question reveals that
the concept of the Other is a changing manifestation of
socio-economic and political conditions. It constructs
group identity by labeling and categorising the core
group but more importantly those outside it. Irish identity
and nationalism, for example, during the eighteenth
century is immersed in ideas of Othering, as political and
social expressions, such as the Penal Laws1, demonstrate
how one group, Protestants, ascribe negative attributes
to label and define the Other group, Catholics, as second
class citizens. This historical research uses customised
software that aids knowledge construction by providing
an interface and central environment to digitise,
store and share sources. The software provides an application
to create
and extract information by developing
factoids, defined as the connection of “different kinds of
structured information” [2], from primary and secondary
sources. These factoids are dynamically generated
by the system in response to researcher-defined queries.
They are composed of factlets and additional XML encoded
source information; factlets themselves are observations
on sources, chosen and manually encoded by the
researcher. The environment also uses data mining, or
knowledge mining [3], techniques to generate organised
clusters of factoids, called data clusters.
MIHS provides a user friendly interface for historians to
create and generate an information environment driven
by their research question. It creates a database containing
relevant information about sources so, for example,
a bibliography and footnotes can be produced. However,
the database is “an intermediate act, not a fi nal one” [2].
The software supports the user in the generation of factoids,
the research-driven encodings of researcher-relevant
information, derived from uploaded images of primary
and secondary sources. It therefore allows the user
to refine and define information of interest and value in
order to solve, or inform, the research question. The generation
of factoids encapsulates the researchers’ thinking
and demonstrates links and relationships between sources
and theory based on the research question.
2 Historical Research using Factoids
Bradley and Short discuss the use of factoids in relation
to prosopography as a means to provide material for future
research and assert that “a collection of factoids does
not record a ‘scholarly overview’ of a person [event] that
a scholar has derived from the sources s/he has read” [2].
In this context the factoids represent
pure information
and are not driven or shaped by any research question or
agenda. Projects such as The Prospography of the Byzantine
Empire, described by Bradley and Short, provide
a vast array of information searchable by, amongst others,
factoid type and source type, and are invaluable to
anyone interested in this fi eld of study. The use of factoids
ensures information is presented in a structured,
relation-based fashion. It provides interaction between
different sources and enhances scholarly research. However,
Bradley and Short argue that “one of the diffculties
with a factoid approach is to establish what kind of ‘factoids’
should be collected from the source” [2]. With this
approach, those creating the prosopography database
must predict what future users may or may not require.
In contrast, MIHS is research driven – factoids are produced
in the context of the research question. The software
is not only a digital research tool but forms an integral
part of the methodology as the software creates
factoids based on a “scholarly overview” [2] and scholarly
interpretation. The source is encoded using XML
(see Figure 1,2,3), though an abstraction of XML is presented
to the user where element names and attributes
are defined. This ensures the user controls the schema
and subsequent representations of the source within the
software (tags and categories previously used will be
highlighted to ensure continuity) and the actual “task of
categorising, grouping and ordering” [2] sources opens
a conversation between the user, the sources and the research
<factlet id = “SS0012-F0001” title = “Contesting
<category> Penal Laws </category>
<category>Catholic identity</category>
The laws helped fashion an
<key> identity for Catholics</key> who in fact had developed differences
among themselves over the centuries of
<key>colonialism</key> ...
<source id = “SS0012” imageid=”SS00012.
jpg” x=”200” y=”197”>
<source id = “SS0012” type = “secondary”
imageid = “SS00012.jpg”
Contesting Ireland, Irish voices
against English in the 18th century
<author> Thomas McLoughlin </author>
<date> 1999 </date>
<publisher> Four Courts Press Ltd. </
<location> Dublin </location>
<isbn> 1851824480 </isbn>
<factoid id = Penal Laws>
<factlet id = SS0012-F0001/>
<factlet id = PS0001-F0002/>
<text date = 09.09.08>
The <link> Penal Laws </link> were used to
alienate a whole people; yet by doing this
the <link> Protestant elite</link> ensured
that <link>Irish Catholics</link> formed
and solidify an identity rooted in the exclusive
nature of the laws.
<text date = 15.09.08>
Associative groups such as the
<link>Catholic Committee</link> develop
and articulate the Catholic voice through
protest against these laws. Instead of
disabling the Catholic majority the laws
provide an important target of organised
<text date = 25.09.08>
(Shows the contradictions of
the <link>Age of Enlighten
Factoids are produced through user and source interaction
and the utilisation of the software. To create factoids
the user must fi rst define numerous factlets from a
source. Each factlet inherits core information from the
original source such as location, date, author, etc. Factlets
from different sources that are related by subject or
category, for instance, then merge to create a factoid.
The user will extract information from encoded sources
whilst preserving the integrity of the information through
easy access to the original source image, thus maintaining
source context. Metadata, providing original source
information, is available for each factlet contained in a
factoid (and indeed factoids presented in data clusters)
and is expressed using the Dublin Core Metadata standard.
A typical use, for example is as follows “How do the
Penal Laws categorise Catholics as the Other in Irish society?”
To answer this question, pamphlets, periodicals,
newspapers and other text based sources are used. Figure
4 demonstrates
the interaction between source material
and the creation of factoids. This example is constructed
from a small dataset. As the number of sources increase
the benefit of this type of data organisation becomes
more obvious. The factoid in Figure 4 represents sources
which are concerned with the Penal Laws. In a larger
dataset, this factoid can be refined or defined by using
relational operators and set relations, creating factoids
specific to the user.
Another project that has yielded a database system for
prosopography, the COEL database, states a fundamental
approach requires that “the data must always be protected
against contamination by the interpretation” [4].
This type of approach may be required when presenting
historical sources and information
for general consumption
but in MIHS, user interpretations are paramount.
MIHS presents users arguments and reflects his/her line
of thought through the construction of factoids. The ability
to return to original sources ensures context remains
and leaves a forum open for historical debate. The production of factoids is followed by the use of data
mining techniques to generate data clusters. This approach
will help with the complexities and magnitude
of large data sets and the presentation of large quantities
of factoids. 3 Text mining using factoids in MIHS
Data mining methodologies are commonly used within
retail, marketing and fi nancial industries. They are used
to derive patterns, associations and correlations
small to large datasets, utilising database architecture
such as data warehousing, which result in the extraction
of data knowledge [3]. Data mining adds value to raw
data. Yet apart from its advantages in terms of statistics,
data mining techniques can be used for text documents
– text mining. Like data mining, it derives patterns and
relations from texts and can be used “as partof-
tagging, word sense disambiguation and bilingual dictionary
creation” [5]. It can also be used to create synopsis
of text by (excluding -the, of, is etc.) calculating
frequently used words.
MIHS will use text mining to provide graphical models
of data where factoids are represented as nodes on a tree.
By using text mining we can extrapolate extra information
either from the original text and/or related factoids.
Text mining techniques enable the software to carry out
text analysis on source material. For instance, the researcher
can input milestones like the 1798 Rebellion,
and, through language comparison and analysis, interpret
changes in society. This may allow the researcher
to move towards answering questions such as “After
the failure of the 1798 Rebellion is there evidence of a
change in attitude towards Irish Catholics?” This technique
often yields interesting results because “even if a
word only appears once or twice it can be significant if
it does not appear at all in the [text] used for comparison”
[6]. By mining factoids related to the Penal Laws
and specifying dates such as 1798 – which has important
consequences for both Irish Protestants and Catholics –
the changing socio dynamics emerge through text comparisons,
highlighting changes in descriptions and attitudes
towards and within the different religious groups.
4 Conclusion
The ultimate aim of the software is to create an on-line
community of historians, research and sources, supporting
individual and collaborative projects. It will facilitate
access to sources, facts and factoids related to research
projects, which can be viewed as separate entities or part
of scholarly interpretation. MIHS will provide a generic
information platform for historians, moving away from
software designed for specific research projects, to create
a platform for historical debate where users can share
sources, factoids and, of course, ideas. MIHS will allow
for the construction of database architectures without the
complexities often inherent in the creation of historical
The process of managing the vast array of sources required
for historical research can be a mammoth task,
both in terms of handling the large volumes of data and
the subsequent interpretation of sources. MIHS, through
the creation of factoids and use of text mining, provides
the means to store and organise sources collected. By
allowing for self organisation of data, recommendations
for data mining, references to related data and manuscripts,
the creation of factlets and factoids, among others,
MIHS will serve as an important tool in creating
well-read and well-informed historical projects.
1The Penal Laws were a series of anti-Catholic laws
passed in Ireland during the seventeenth and eighteenth
1. McGrane, B.: Beyond Antropology, Society and the
Other. Columbia University Press (1989)
2. Bradley, J., Short, H.: Texts into databases: The evolving
fi eld of new-style prosopography.
Literary and linguistic
computing 20 (2005) 3–24
3. C.R. Rao, E.W., Solka, J., eds.: Handbook of statistics
24, Data mining and data visualiztion. Elservier (2005)
4. Keats-Rohan, K.: Historical text archives and prosopography:
the coel database system. History and Computing
10 (1998) 57–72
5. Hearst, M.A.: Untangling text data mining. http://people.
ischool.berkeley.edu/ hearst/papers/ac199/ac199-
6. Welling, G.: Can computers help us read history better?
computerised text-analysis of four editions of the
outline of american history. History and Computing 13
(2) (2004) 151–160

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Conference Info


ADHO - 2009

Hosted at University of Maryland, College Park

College Park, Maryland, United States

June 20, 2009 - June 25, 2009

176 works by 303 authors indexed

Series: ADHO (4)

Organizers: ADHO

  • Keywords: None
  • Language: English
  • Topics: None