Shaw, R. 2009. From facts to judgments: Theorizing history for information science.
Forthcoming in Bulletin of the American Society for Information Science and
Technology 36 (2).
Databases of historical facts
There are a number of recent projects which seek to represent the past as a database of
historical facts. Drawing upon the increasing availability of digitized historical texts and
advances in text mining and semi-structured databases, these projects seem set to fulfill
Paul Otlet's dream of extracting the factual content from texts and making it available to
answer queries about the past.
The academic project DBpedia [1] aims to extract facts from Wikipedia “infoboxes”, sec-
tions found on certain categories of Wikipedia articles that present basic facts using a
standardized template. For example, the “Historical Event” infobox template includes
fields for the event's preferred name, alternate names, date, location, participants, and
“result,” as well as a representative image. On a Wikipedia article such as “French Revo-
lution,” these values are presented as an HTML table. After parsing and processing by
the DBpedia project's algorithms, these values are transformed into a standardized data
model for representing subject-predicate-object expressions (e.g. “French_Revolution :
location : France”). Another project that parses Wikipedia infoboxes into structured
data is Freebase, a commercial service that has the ambitious goal of integrating this
data with all other available public domain data and providing interfaces for editing and
adding to it.
Projects like DBpedia and Freebase mine historical facts primarily from the riches of
Wikipedia, hoping that the collaborative effort there will trickle into their own projects.
Other projects aim to mine the web at large for historical knowledge. Bruce Robertson
calls the web “a historian's fantasy” and envisions sophisticated tools for organizing and
querying not only encyclopedia articles but also digitized archives, scholarly editions,
journal articles, and blog posts [2]. Pursuing a similar vision, digital historian Dan Co-
hen and programmer Simon Kornblith have developed a system called H-Bot [3] that
parses Google search results to answer historical questions. Google itself has begun in-
corporating “timeline results”—historical facts mined from the web—into its search re-
sults for historical persons or events. All of these systems might be considered descen-
dants of IBM's “Professor RAMAC” computer program, which at the 1958 World Fair
impressed audiences with its ability to answer historical questions using its “stack of 50
fast spinning disks” on which were stored “the principal historical events of the world
from the birth of Christ to the launching of Sputnik I” [4].
Of all these projects, H-Bot provides the most interesting case study, as it was developed
by a historian who has reflected on the project in print. H-Bot claims to demonstrate the
“automatic discovery of historical knowledge” by using Google search results to answer
Ryan Shaw From facts to judgments: Theorizing history for information science 1 of 9

simple factual questions such as “When was George Washington born?” or “Who was
Lao-Tse?” The program uses sentence parsing techniques to transform questions into
keyword searches that are passed to Google. It then uses statistical techniques to extract
frequently repeated information such as dates and names from the returned web pages.
(In practice, however, H-Bot usually draws its answers from a handful of online refer-
ence sources such as Wikipedia or Wordnet.) With its focus on simple factoids, its crea-
tors admit that H-bot “offers an impoverished view of the past.” But they divert atten-
tion from this critique by implicitly equating such fact-finding with “historical knowl-
edge” and by promising greater things to come (a favorite technique of AI researchers
for decades). Most tellingly, however, they argue that the profusion of historical evi-
dence surviving from the recent past poses a “problem” for historians, who must adopt
the same techniques for managing “information overload” that scientists use.
Information overload and reading avoidance
This rhetoric should be familiar to information scientists. From Paul Otlet to Vannevar
Bush to the NSF-funded projects of today, much research in information management
and retrieval has focused on the needs of scientists, and specifically on how to help sci-
entists avoid reading. The problem as it has been framed by this research is that scien-
tists must stay current with ever more scientific literature in a finite amount of time.
This has led to a focus on text summarization, filtering of “irrelevant” information, and
extraction of “key facts” from explanatory narrative. With the advent of large-scale cor-
puses of digitized texts, these techniques are now being proffered for the humanities as
well. The call for proposals from the recent Digging into Data program funded by the
NEH, NSF and others to develop data-mining tools and techniques for humanist schol-
ars exemplifies how the problem of “too much text” is being framed: “Now that scholars
have access to huge repositories of digitized data—far more than they could read in a
lifetime—what does that mean for research?” (emphasis added). Databases of historical
facts are in part a response to this perceived problem: when there are too many histories
to be read, boil them down to bare facts that can be subjected to powerful selection and
retrieval mechanisms.
Such approaches are better suited to the sciences, given that scientists are assumed to be
engaged in a cumulative research effort in which later researchers build upon the work
of earlier researchers. This ideal of cumulative research effort requires that the complex-
ity of earlier work be distilled down to reusable conclusions or “facts”. Bruno Latour in
his Science in Action [5] famously describes this process of fact production as “black-
boxing.” A black box is a metaphor for a mechanical or computational component that is
used to fulfill a functional requirement without knowledge of its internal implementa-
tion. While it may be possible to know how the black box works, all that is relevant to its
users is that it produces expected outputs from given inputs. While scientists can in
principle go back and recreate the experiments of their predecessors, efficient cumula-
Ryan Shaw From facts to judgments: Theorizing history for information science 2 of 9

tive research requires that most of the time they simply trust that the facts they inherit
work as advertised.
Critique of the historical database
Yet as the philosopher of history Louis Mink [6] points out, work in history does not
produce “detachable” conclusions of this kind. It is rare for historians to simply accept
an earlier account of some historical subject. Re-examination of primary evidence is the
rule. Mink argues that this is not because historiography is less developed than the sci-
ences, but instead is due to a difference in the nature of the conclusions that historians
produce. Rather than simply producing facts about the past, the historian aims to pro-
duce what Mink calls “synoptic judgments” of some complex of actions and events in the
past.
A synoptic judgment is an interpretive act in which one moves from seeing that a series
of things happened (the facts about the past) to seeing those happenings as a synthetic
whole. Once she has reached such a judgment herself after immersion in the historical
evidence, the historian's task is to lead others through the interpretive process via the
medium of a written text. Through the techniques of narrative representation, the
author of a historical text aims to show past actions and events as a coherent whole
when seen from a certain perspective. The exhibition of this whole as represented by the
“thick description” of the historian's narrative is the conclusion, and as such cannot be
detached from that narrative. In other words, the historian's conclusions inhere in the
structure and organization of her narrative. Even when the historian summarizes her
narrative in separate statements, Mink argues, these statements are not detached con-
clusions but simply reminders to the reader of how the historian has ordered and organ-
ized her true conclusion, the narrative itself.
Databases of historical facts purport to help us answer questions about the past, and in a
narrow sense they do that. But few of these systems take us further than the initial “Hey,
neat!” reaction inspired by Professor RAMAC. The problem is not that the facts are
wrong—Cohen and Rosenzweig show that, on the the contrary, they can be quite accu-
rate by most standards—or that they are incomplete (though they certainly are). Nor is
insufficiently advanced technology to blame—even if we were able to perfectly extract
historical facts from texts, disambiguating every name and indexing each fact in the ab-
solute grid of time and space, we would still face this problem. The problem is that sys-
tems like this are grounded in an impoverished conception of how we represent the his-
torical past, a conception that focuses on atomic facts rather than synoptic judgments.
The problem is an old one. It can seem obvious that what we need to understand the
past are facts about the past, and that a perfect history is thus one that identifies and
enumerates “everything that happened” in terms of such facts. Yet upon careful consid-
eration these notions are quite problematic. Philosophers often have hypothesized a
Ryan Shaw From facts to judgments: Theorizing history for information science 3 of 9

complete historical database in order to demonstrate what these problems are. Arthur
Danto imagines an “Ideal Chronicle” with descriptions of “absolutely everything that
happened,” in the order it happened, thus providing the “whole map of the Past” [7].
Danto argues that even such a complete database of the past would not obviate the need
for historiography, since the role of historians is not simply to recount factual data about
the past, but to represent the significance of episodes in the past from the perspectives
of the present. These perspectives (and thus our criteria for significance) are constantly
changing. It is this kind of change, not simply the discovery or refutation of historical
evidence (addition or deletion of facts from the database) that results in new historiog-
raphical conclusions. Or, as Mink puts it, even if we could “sit before a screen and di-
rectly review the past in its minutest details,” we would still need some imaginative rep-
resentation of the past to help us make sense of it all in light of our current historical
situation, and it is the role of history to develop such imaginative representations and
not simply gather the detailed facts.
Colligation and colligatory concepts
Acts of synoptic judgment produce imaginative representations that are articulated as
historical narratives. Once historians have developed narratives that relate sets of facts
under some synthesizing ideas, they usually label these narratives with phrases like “The
Renaissance” or “The French Revolution.” The philosopher of history W. H. Walsh calls
this process colligation, appropriating the philosopher of science William Whewell's
term for “the binding together or connection of a number of isolated facts by a suitable
general conception or hypothesis” [8]. Walsh supports a hermeneutic conception of his-
torical method in which the goal of historians is to imaginatively and empathetically re-
construct the experiences and thoughts of people in the past. Accordingly, Walsh's no-
tion of colligation initially depended upon happenings being “intrinsically” connected by
virtue of of being intentions or consequences of some past actor's plans. A “suitable gen-
eral conception” for binding together facts was one which illuminated those facts as be-
ing part of a (conscious or unconscious) “policy” guiding the behavior of people in the
past. Later Walsh expanded his notion of colligation to include any case in which some
set of events is interpreted as a connected process or development, whether or not such
a policy could be discerned [9].
Even though concepts are of primary interest in library and information studies, colliga-
tory concepts have been mostly overlooked. Even the most sophisticated theoretical dis-
cussions of concepts in the literature tend to equate concepts with classes or categories.
For example in his recent survey of concept theories Hjørland [10] asserts that “Con-
cepts are dynamically constructed and collectively negotiated meanings that classify the
world according to interests and theories” (emphasis added). This preoccupation with
classification is perhaps understandable in light of the aforementioned focus on scien-
tific domains. The sciences seek to abstract away from unique individuals to generalized
Ryan Shaw From facts to judgments: Theorizing history for information science 4 of 9

classes that can be related by laws. While historians do generalize, they also—arguably
primarily—assemble descriptions of unique past events into connected and coherent but
no less unique representations. Concepts like “The Renaissance” colligate rather than
classify.
Ankersmit’s theory of colligatory concepts
The most fully developed theory of colligation to date has been developed by Frank
Ankersmit, who in his Narrative Logic: A Semantic Analysis of Historian's Language
[11] seeks to explain how historians construct colligations—which he calls “narrative
substances”—from sets of statements expressing facts. In Ankersmit’s theory, a colliga-
tion is a point of view from which to regard the past. The historian articulates this point
of view by means of a specific historical narrative. Ankersmit contends that each indi-
vidual historiographical narrative constructs a colligation so that, for example, there are
as many “Renaissances” as there are narratives on the subject, since each narrative ar-
ticulates a specific point of view. So when we speak generally about “The Renaissance,”
we are really talking about a whole family or type of colligations which have been given
the same name. We can refer to such a type as a colligatory concept.
Furthermore, Ankersmit claims that when we define such types, we do so extensionally
rather than intensionally. An intensional definition of a type is one that defines some
necessary and sufficient conditions for belonging to the type. For example, one might
define a “mug” intensionally as “a type of cup made of glass or ceramic and having a
handle large enough to accommodate a whole hand.” An extensional definition of a type,
on the other hand, enumerates the members of a set of individuals considered to be in-
stances of that type. An extensional definition of “mug” would collect all the world's in-
dividual coffee mugs and beer steins and so on and thereby declare “these are mugs.”
Ankersmit argues that one can define colligatory concepts extensionally by clustering
colligations that contain overlapping sets of statements. He proposes a thought experi-
ment in which a giant matrix is constructed. Along one axis of the matrix are aligned all
the declarative statements made about the past that have actually appeared in some text
or another. Along the other axis are aligned all the colligations constructed by means of
those statements. Each cell in the matrix is then filled with a “0” or a “1” indicating
whether or not the corresponding statement was used to help construct the correspond-
ing colligation. Given such a matrix, we could then try to identify colligatory concepts by
grouping together colligations with similar patterns of 0s and 1s, in much the same way
that we might identify types of drinking vessels by looking for similar shapes or handles
or materials. Ankersmit posits that we will observe that “certain classificatory patterns
automatically appear.” These clusters in “narrative space” reflect the fact that historians
write in response to other historians and construct their colligations by distinguishing
them from those that came before (which implies a significant degree of overlap).
Ryan Shaw From facts to judgments: Theorizing history for information science 5 of 9

As Ankersmit points out, such a extensional procedure for identifying types can never be
precise. Depending on how we interpret “similar patterns” there will be many possible
groupings into types. Moreover, for any given interpretation of similarity, there will al-
ways be boundary cases that could belong to more than one cluster. At best, extensional
typification can identify regularities in how we have chosen to conceptualize reality, but
it cannot tell us anything about reality itself. In other words, looking at written history
this way tells us something, not about the reality of the past, but about the contours of
the concepts developed by historians over time. “The Renaissance”, “The Cold War”,
“The French Revolution”, and “9/11” are not objectively existing entities in the past, but
are names of types of stories we tell to understand the past.
Finally, Ankersmit argues that an extensional procedure like this is the only way to iden-
tify colligatory concepts. The alternative would be to intensionally identify types in
terms of logical definitions based on attributes of the things being classified, the way we
define the type “mammal” as “warm-blooded”, “vertebrate”, and “having hair or fur”.
But this is precisely what we cannot do for colligatory concepts. There is no logical defi-
nition, no core set of properties both necessary and sufficient for making a particular
narrative a narrative about “The French Revolution”. While it's easy to identify state-
ments that would not appear in any narrative of the French Revolution—for example
that the storming of the Bastille occurred in 1967 in Tokyo, Japan—we cannot identify
statements that must appear in such stories or by virtue of which we must consider a
given narrative to be a narrative about the French Revolution. Given all the narratives
that have ever been written about the French Revolution, we may not be able to identify
a single statement that appears in every one. Thus we cannot and do not identify colliga-
tory concepts intensionally. Decisions about what “The Cold War” is can only be justi-
fied pragmatically, not logically.
Possibilities for semantic tools
With the large-scale digitization of books it may become possible to investigate Anker-
smit's theory by analyzing the full texts of historical narratives. Before undertaking such
an project we must address some methodological problems. First is the issue of how to
identify statements about the past. Ankersmit's matrix involves statements (also known
as propositions) about the past, not the sentences that express these statements.
(Ankersmit contends that “states of affairs in the past can be unambiguously described
by means of constative statements.”) We cannot conflate statements with the sentences
that appear in historical texts, as it is unlikely that any two texts will contain precisely
the same sentence. So we must find a way to move from the sentences that appear in
texts to the propositions those sentences express, a problem that has occupied linguists
and philosophers of language since Bertrand Russell. Fortunately this is an area where
“information extraction” technology might show its worth, as such technology is pre-
cisely concerned with transforming sentences into logical propositions. Despite well-
Ryan Shaw From facts to judgments: Theorizing history for information science 6 of 9

known problems with propositional theories of language [12] and the fact that informa-
tion extraction technologies are plagued by errors, their output still might be usable for
exploring Ankersmit's theory.
The second problem, as Ankersmit points out, is that a given historical text constructs
multiple colligations, and it is difficult to determine exactly which statements are being
used to construct which colligations. Indeed, Ankersmit argues that in order to identify
colligatory concepts reliably we must “compare historiographical topics studied and dis-
cussed by generations of historians.” Fortunately the catalogers who maintain the Li-
brary of Congress Subject Headings (LCSH) have done this for us, creating subject head-
ings for historiographical topics and assigning them to historical texts. Using the LCSH
is not ideal for investigating colligatory concepts, however. Catalogers usually do not
identify more than a couple of the colligations constructed in a given text. Since they are
concerned with characterizing whole books, they will not identify historical narratives in
books that are not primarily works of historiography. And since the goal is to collocate
texts, there is no effort to distinguish differences among the colligations constructed by
different texts. In essence, what catalogers have done is group various colligations into
types a priori.
Notwithstanding these problems, it is plausible that one could use a historiographical
subject heading to obtain a set of texts within which authors have constructed compara-
ble colligations. Ankersmit suggests that we can refer to colligations with terminology
such as “Renaissance
1
”, “Renaissance
2
”, “Renaissance
3
”, etc. where the subscript n indi-
cates that we are referring to the specific representation of the Renaissance constructed
in the historical text n. Likewise, we could posit that a set of N texts found under the
LCSH for the “Renaissance” constitute a set of colligations “Renaissance
1
”, “Renais-
sance
2
”, “Renaissance
3
”... “Renaissance
n
”. We could then compare the propositions
made in these texts to each other to build a model of the extension of the colligatory
concept “Renaissance.” This model could provide a basis for highlighting differences
among the individual narratives constructed by the different texts. Though such a model
would enable us to examine the structure of types already identified by the LCSH, it
could not reveal new types not yet identified there. But we have to start somewhere.
Whether or not initial attempts to automatically discern and model them prove success-
ful, it is time information science paid closer attention to colligatory concepts. In the
digital environment, traditional components of the scholarly apparatus such as term
lists, classification and categorization schemes, and thesauri are evolving into general-
ized semantic tools for enumerating and disambiguating concepts and mapping the rela-
tions among them [13]. Though in the past such tools have mainly been used for index-
ing and retrieval, in an era of full-text search I believe we will see other applications
move to the fore. Specifically, semantic tools that map a given conceptual domain can be
integrated into reading and writing environments to help users contextualize some
Ryan Shaw From facts to judgments: Theorizing history for information science 7 of 9

fragment of interest. Given such applications, “fuzzy” concepts reflecting particular in-
terpretive stances are at least as important as traditional categorical concepts. Anker-
smit and his predecessors’ theory of colligation provides grounds for investigation of
such concepts. Done properly, such an investigation may lead to new tools—or new ways
of using existing tools—that avoid reducing conceptions of the past to bare facts.
Acknowledgements
Support from the Institute of Museum and Library Services through award LG-06-06-
0037-06 “Bringing Lives to Light: Biography in Context” and from the Advancing
Knowledge grant PK-50027-07 “Context and Relationships: Ireland and Irish Studies,”
jointly funded by the National Endowment for the Humanities and the Institute of Mu-
seum and Library Services, is gratefully acknowledged.
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