Mapping knowledge domains.

T 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 electronically, 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 diversification; 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. 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 implementation of the underlying algorithms. The desire to promote 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 documents 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 contributors 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 emphasized 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