In a former life, I built microprocessor-based data acquisition systems, originally for locating and monitoring wind and solar energy systems. I suppose it is fair to say that I have long been involved in roaming solution space. Along the way, farmers, on whose land the energy systems were often situated, discovered that my monitoring tools helped them form better predictions of fruit frost, irrigation needs, and pesticide needs. My program, which ran on an Apple II computer that had telephone access to the distributed monitoring stations, printed out large piles of data. Epiphany happened on the day that a manager of one of those monitoring systems came to me and asked, "What else is this data good for?" That was the day I entered the field of artificial intelligence, looking for ways to organize all that data and mine it for new knowledge. A recent discussion on National Public Radio focused on the nature and future of literature. Listening to that conversation while navigating the perils of Palo Alto traffic, I heard two comments that I shall paraphrase, with emphasis placed according to my own whims, as follows: In the past, we turned to the great works of literature to ponder what is life. Today, we turn to the great works of science to ponder the same issues. In some sense, the message I pulled out of that is that we (the really big we) tend to appeal to science and technology to find comfort and solutions to our daily needs. In that same sense, I found justification for this book and the vision I had when the book was conceived. Make no mistake here--I already had plenty of justification for the vision and the book. As is often pontificated by many, we are engulfed in a kind of information overload that threatens to choke off our ability to solve major problems that face all of humanity. No, the vision is not an expression of doom and gloom. Rather, it is an expression of my own deep and optimistic belief that it is through education, through an enriched human intellect, that solutions will be found, or at least, the solution space will become a more productive environment in which to operate. The vision expressed here is well grounded in the need to organize and mine data, all part of the solution space. While walking along a corridor at an XML conference in San Jose early in the year 2000, I noticed a sign that said "Topic Maps," with an arrow pointing to the right. I proceeded immediately to execute a personal "column right" command, entered a room, and met Steve Newcomb. The rest all makes sense. While in Paris later that year, I saw the need to take the XTM technology to the public. This book was then conceived at XML 2000 in Paris, and several authors signed on immediately. This book came with a larger vision than simply taking XTM to the public. I saw topic maps as an important tool in solution space. The vision included much more; topic maps are just one of many tools in that space. I wanted to start a book series, one that is thematically associated with my view of solution space. This book is the first in that series, flying under the moniker Open Knowledge Systems. By using the word open, I am saying that the series is about making the tools and information required to operate in solution space completely open and available to all who would participate. Open implies that each book in the series intends to include an Open Source Software project, one that enables all readers to immediately "play in the sandbox" and, hopefully, go beyond by extending the software and contributing that new experience to solution space. This book is about topic maps, particularly topic maps implemented in the XTM Version 1.0 specification format, as conceived by the XTM Authoring Group, which was started by an experienced group of individuals along with the vision and guidance of Steve Newcomb and Michel Biezunski, both contributing authors for this book. As with many new technologies, the XTM specification is, in most regards, not yet complete. In fact, a standard like XTM can never be complete simply because such standards must coevolve with the environment in which they are applied. In the same vein, a book such as this cannot be a coherent work simply because much of what is evolving now is subject to differing opinions, views, and so forth. There are a few assumptions made by all of the authors who contributed to this book. Mostly, the assumptions presume some minimal familiarity with Extensible Markup Language (XML), Extensible Style Language (XSL and XSLT), and Resource Description Framework (RDF). Please keep in mind that the book presents many Web site references. Web sites occasionally disappear. While the links presented were tested during the writing phase and again during final manuscript editing, do not be surprised if some of them fail to remain in service. Since this book will remain a living document on the Web, we hope to keep all links up-to-date on the book's Web site. Because of my view that solution space itself is coevolving along with the participants in that space, I have adopted an editorial management style that I suspect should be explained. My style is based on the understanding that I am combining contributions from many different individuals, each with a potentially different worldview and each with a different writing style. The content focus of this book is, of course, on topic maps, but I believe that it is not necessary to force a coherent worldview on the different authors--it is my hope that readers and, indeed, solution space will profit by way of exposure to differing views and opinions. There will, by the very nature of this policy, be controversy. Indeed, we are exploring the vast universe of discourse on the topic of knowledge, and there exists plenty of controversy just in that sandbox alone. There is also the possibility of overlap. Some chapters are likely to offer the same or similar (or even differing) points of view on the same point. Case in point: knowledge representation. This book has several chapters on that topic: one on ontological engineering, one on knowledge representation, and one on knowledge organization. Two chapters talk in some detail about semantic networks, and other chapters discuss how people learn. It's awfully easy to see just how these can overlap, and they do. My management style has been that which falls out of research in chaos theory: use the least amount of central management, and let the authors sort it out for themselves. History will tell us whether this approach works. 0201749602P08282002
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