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Introduction
Mapping knowlecige domains
Richard M. Shiffrin*t and Katy Bornert
*Psychology Department and tSchool of Library and Information Science, Indiana University, Bloomington, IN 47405
The term "mapping knowledge domains" was chosen to
describe a newly evolving interdisciplinary area of science
aimed at the process of charting, mining, analyzing, sorting,
enabling navigation of, and displaying knowledge. This field is
aimed at easing information access, making evident the structure
of knowledge, and allowing seekers of knowledge to succeed in
their endeavors. Although thousands of years old, this area has
undergone a sea change in the last 15 years, a change fostered
by an explosion of the amount of information available, the
accessibility of that information due to electronic storage, and
the new techniques of analysis, retrieval, and visualization that
are made possible by vast increases in computational storage
capacity and processing speed and power. Many of us are so
involved in the new ways of accessing knowledge that we have
forgotten how recent is the change to computerized knowledge
retrieval with search engines operating on the World Wide Web.
Remarkable as these changes are to date, they are only a hint of
the transformation to come. The Arthur M. Sackler Colloquium
on Mapping Knowledge Domains, held at the Arnold and Mabel
Beckman Center of the National Academies of Sciences and
Engineering in Irvine, CA, May 9-11, 2003, was designed to
showcase the ongoing developments in this transformation and
provide pointers toward the directions it will move.
The changes that are taking place profoundly affect the way we
access and use information. Scientists, academics, and librarians
have historically worked hard to codify, classify, and organize
knowledge, thereby making it useful and accessible. The day is
fast approaching when all this knowledge will be coded elec-
tronically, but mixed in a vast and largely disorganized and often
unreliable sea of mostly recent information. Fishing this sea for
desired information is presently no easy task and will continue
to increase in difficulty. However, the speed and power of
modern computation gives hope that this daunting task can be
accomplished. In addition, and perhaps even more important,
the new analysis techniques that are being developed to process
extremely large databases give promise of revealing implicit
knowledge that is presently known only to domain experts, and
then only partially.
Some of these techniques are now being applied in science,
aiming to identify and organize research areas according to
experts, institutions, grants, publications, journals, citations, text,
and figures; discover interconnections among these; establish the
import of research; reveal the export of research among fields;
examine dynamic changes such as speed of growth and diversi-
fication; highlight economic factors in information production
and dissemination; find and map scientific and social networks;
and identify the impact of strategic and applied research funding
by government and other agencies. The new techniques support
and complement human judgment. They dramatically speed up
achievements formerly reached solely by human effort and
provide new results that could not have been reached by humans
unaided. As the flood of new and disorganized information
continues to crest, the new tools are increasingly critical for the
growth of scientific research, and indeed for the functioning of
modern society.
www.pnas.org/cgi/cloi/10.] 073/pnas.03078521 00
The importance and fundamental nature of these new ways of
interacting with information, and accessing knowledge, have led
to considerable interest in for-profit applications. As a result,
many of the algorithms and software developed in this field are
proprietary. Users are given the end products, such as a list of
potentially useful websites or a visual map, without much
knowledge concerning the conceptual basis and technical im-
plementation of the underlying algorithms. The desire to pro-
mote a deeper understanding therefore led us to include leading
researchers not only from academia and government, but also
from businesses such as Google and Microsoft.
We thought it would prove useful and interesting if some of the
techniques used to map knowledge were applied to the contents
of PNAS itself. Thus, we arranged for registered participants to
have access to an electronic compilation of the full text docu-
ments from PNAS covering January 7, 1997, to September 17,
2002 (148 issues containing some 93,000 journal pages). The time
between the first availability of this data set and the deadline for
submissions was rather short; nonetheless, several of the con-
tributors analyzed this set, with results that provide interesting
directions for future research.
The value of mapping knowledge domains of course extends
well beyond the bounds of information science or the PNAS
journal, to scientists, researchers, governmental institutions,
industry, and members of society generally. It should be em-
phasized that, although the extraction and organization of
knowledge may form the scientific core of this field, the results
will be of little use unless the user can understand and interact
with the mapping systems. Knowledge typically is organized
along many thousands of dimensions, but a map with thousands
of dimensions cannot be used effectively by humans. For this
reason, domain visualizations and the ability to interact with
knowledge and view it from a variety of perspectives play a
critical role. The results of algorithms used to extract and
organize relevant data can be displayed in many complementary
ways. For example, maps might depict major researchers, most
cited articles and books, articles too new to receive many
citations but with contents that point to emerging trends, articles
organized into topic trees (by content, citations, and authors),
and grants awarded by topic. Other maps might depict changes
over time. Such techniques hold out the promise that the user
will be able not only to visualize a few nearby trees in the forest
of knowledge, but also to understand the entire landscape. If
these techniques can be made to operate effectively, they may
well change the way that science is conducted and the way the
business of the world is carried out.
Achieving such results requires tools from diverse areas of
science: ways to analyze truly enormous amounts of data and
extract meaningful results; ways to sort and cluster information
This paper serves as an introduction to the following papers, which result from the Arthur
M. Sackier Colioquium of the National Academy of Sciences, "Mapping Knowledge Do-
mains," held May 9-11, 2003, at the Arnold and Mabel Beckman Center of the National
Academies of Sciences and Engineering in Irvine, CA.
iTo whom correspondence should be addressed. E-maii: shiffrin~indiana.edu.
2004 by The National Academy of Sciences of the USA
PNAS 1 April 6, 2004 1 vol. 101 1 suppl. 1 1 5183-5185
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by similarity and importance; ways to identify close and distant
interconnections that are not immediately obvious (especially
when terminology differs); ways to display large amounts of
information that lie along multiple dimensions so that the user
can properly interpret the results and guide further exploration;
ways to design interactive interfaces; and ways to analyze the
structure of the database itself. Examples of many of these are
represented in the articles of this special issue, and additional
examples were presented at the colloquium (slides and audio
files of the talks and a video of the poster presentations are
accessible at http://vw.indiana.edu/sacklerO3/~. In the long run,
the promise of this field will not be realized without dynamically
interactive systems; several of our participants have been devel-
oping such systems, but the pages of a journal do not, unfortu-
nately, afford access to dynamic systems.
Overview of Contributions
This special PNAS issue contains three articles that set the stage
by providing general coverage of methods, techniques, and
practices: an analysis of knowledge extraction from the World
Wide Web by Monika Henzinger and Steve Lawrence (Goggle
research); an analysis, correlation, and mapping of paper and
grant data to assess research by Kevin W. Boyack (Sandia
National Laboratories, Albuquerque, NM); and an analysis of
scientific collaboration networks by Mark Newman (University
of Michigan, Ann Arbor).
Several articles address methods to extract and organize
information from large unstructured databases: Simon Dennis
presents an unsupervised method to extract propositional infor-
mation from a "tennis article" database and answer questions
about information implicit in the data. Three articles present
methods to organize databases in terms of the semantic simi-
larity of the contents and to apply the methods to the PNAS
database. Tom Landauer, Darrell Laham, and Marcia Derr use
"Latent Semantic Analysis." Elena Erosheva, Stephen Fienberg,
and John Lafferty use a form of mixed-membership model, and
Tom Griffiths and Mark Steyvers present another form they
term the "Topics Model" (both are a generalization of "Latent
Dirichlet Allocations. Paul Ginsparg, Paul Houle, Thorsten
Joachims, and Jae-Hoon Sul classify research areas inherent in
a large text database of physics articles by using a "support vector
machine."
Other articles assume a knowledge database represented or
representable as a graph structure. Dennis Wilkinson and Ber-
nardo A. Huberman present a stochastic network partitioning
procedure that extends the "Girvan-Neuman" method to large,
complex graphs to account for nodes that belong to several
clusters. They use it to identify communities of genes related to
colon cancer. The method could as well be used to determin
communities of papers or authors from paper-citation or coau-
thor networks.
Most data sets evolve over time, and several articles address
ways to track dynamic changes in structure. One approach
analyzes the changes in users' interactions with the database. The
article by John Hopcroft, Omar Khan, Brian Kulis, and Bart
Selman applied a new clustering algorithm to the NEC CiteSeer
database that would identify real changes in structure without
identifying changes due to random and small perturbations.
Jonathan Aizen, Daniel Huttenlocher, Jon Kleinberg, and Antal
Novak present a stochastic method to analyze the dynamics of
item popularity (web traffic) on the Internet Archive and use it
to identify points of significant change in real world events.
Given the complexity and nonlinearity of the structure and
evolution of, for example, article networks, coauthor networks,
and web page graphs, those who wish to mine such networks for
knowledge must understand the processes by which such net-
works evolve. Filippo Menczer introduces a mixture model that
grows web page or paper-citation networks on the basis both of
5184 1 www.pnas.org/cgi/cloi/10.1073/pnas.0307852100
based existing interlinkages (popularity) and of the content of
the papers and web pages, thereby reproducing network "de-
gree" and content similarity distributions. Katy Borner, Jeegar
Maru, and Robert Goldstone present a simple process model
that simultaneously grows coauthor and paper-citation net-
works; the statistical and dynamic properties of the simulated
network data are validated against a 20-year PNAS data set.
Methods to display and visually explore the results of large-
scale database analyses (i.e., visualization techniques) are of
critical importance, and these can benefit from centuries of work
in geographic metaphors and cartographic techniques. Andre
Skupin reviews major cartographic principles and by way of
example produces a large-format map-like knowledge-domain
visualization. Alan MacEachren, Mark Gahegan, and William
Pike combine geographic visualization techniques and concept
mapping to design a tool that helps individual researchers
describe the process of knowledge construction and enables
teams of collaborators to synthesize common concepts. Ketan
Mane and Katy Borner identify and correlate highly frequent
and highly bursty words in the PNAS database to visualize the
structure and evolution of major research topics over time.
Steven Morris and Gary Yen introduce Crossmaps, a technique
that can be applied to visualize multiple, overlapping relations
among documents such as author collaboration groups vs. topics
or research fronts covered by those documents. Howard White,
Xia Lin, Jan Buzydlowski, and Chaomei Chen present a tool
(and apply it to the PNAS database) that automatically and
rapidly generates small-scale, "local" pathfinder networks and
self-organizing maps as interfaces for document retrieval. The
interfaces show co-cited authors or co-occurring subject head-
ings that can be explored interactively. Chaomei Chen presents
a technique to interrelate and visually present network structures
of, say, paper-citation or coauthor networks, generated for
different time slices.
Opportunities and Challenges
The increasing flood of digitally available data demands the
development of sophisticated tools of analysis and display, but
the tools are limited by the quality of the data. All of the research
described in this special issue requires high-quality data that are
both accessible and available in a usable and common format. A
good deal of "preprocessing" was in most cases required to
transform data to reach this state. We hope to see in the near
future the development of tools to take information in different
and noisy formats and convert it to a common format, or analyze
noisy and inconsistent data directly.
Because present analysis requires clean data, it is often necessary
to make use of proprietary databases. There is often a cost of access,
a fact that contributes to an increasing "information divide." There
are of course efforts to move beyond private information (such as
Medline, the ACM digital library, or the Physics E-pr~nt Archive),
but these are currently unconnected.
Such factors are slowing the development of truly global and
freely accessible maps of science, or of general knowledge, but
we hope and believe that day will arrive. The Sackler Colloquium
on Mapping Knowledge Domains and the present articles that
derived from that colloquium provide provocative glimpses of
that future day.
Many of our colleagues participated in the organization of this collo-
quium. We thank the members of our organizing committee: Kevin
Boyack, Sandia National Laboratories; Chaomei Chen, Drexel Univer-
sity; Susan Dumais, Microsoft Corporation; Jon Kleinberg, Cornell
University; Thomas K. Landauer, University of Colorado; and Josh
Tenenbaum, Massachusetts Institute of Technology. We greatly appre-
ciate the time and effort of our additional reviewers; these included many
of the authors of the present articles, and also Rex G. Cammack, Blaise
Cronin, Tom Erickson, Eugene Garfield, Beth Hetzler, Peter Ingwersen,
Shiffrin and Borner
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Grant Lewison, Francis Narin, Sidney Redner, Ben Shneiderman, and
Colin Ware. We also thank Kevin Boyack, Jason Baumgartner, and
Ketan Mane for processing and managing access to the Colloquium's
PNAS database. Special thanks must also go to Eugene Garfield, the
"father" of this field, who gave the keynote at the Colloquium and
participated actively in the colloquium, adding greatly to its value. The
Shiffrin and Borner
S-year PNAS full text data set was provided by Diane M. Sullenberger,
executive editor, PNAS. The 20-year PNAS citation data set was
extracted from Science Citation Index Expanded "Institute for Scientific
Information, Inc. (ISI), Philadelphia, PA; Copyright ISI]. All rights
reserved. No portion of these data may be reproduced or transmitted in
any form or by any means without the prior written permission of ISI.
PNAS 1 April 6, 2004 1 vol. 101 1 suppl. ~ | 5185
Representative terms from entire chapter:
pnas database
