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XML Topic Maps: Creating and Using Topic Maps for the Web
- By Jack Park, Sam Hunting
- Published Jul 16, 2002 by Addison-Wesley Professional.
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- Sorry, this book is no longer in print.
Description
- Copyright 2003
- Dimensions: 7-3/8" x 9-1/4"
- Pages: 640
- Edition: 1st
- Book
- ISBN-10: 0-201-74960-2
- ISBN-13: 978-0-201-74960-1
XML Topic Maps (XTM) represent a powerful new tool for transforming the Web from a vast, chaotic sea of data into a highly usable information resource. XML Topic Maps is the first comprehensive, authoritative guide to this new technology. Edited by Jack Park, a leader of the XTM community, with contributions from leading members of the community, it covers every aspect of XML Topic Map creation and usage. Drawing on the XTM 1.0 standard, a complete XML grammar for interchanging Web-based Topic Maps, this book shows how XML Topic Maps can be utilized as an enabling technology for the new "Semantic Web," in which information is given well-defined meaning, making it possible for computers and people to cooperate more effectively than ever before. Coverage includes: creating, using, and extending XML Topic Maps; ontological engineering; and the use of XML Topic Maps to create next-generation knowledge representation systems and search tools. Park shows how to use Topic Maps to visualize data; how Topic Maps relate to RDF and semantic networks; and finally, how Topic Maps presage a profound paradigm shift in the way information is represented, shared, and learned on the Internet -- and everywhere else. For every Web designer, developer, and content specialist concerned with delivering and sharing information in more useful and meaningful forms.
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3
Table of Contents
Foreword.
Preface.
Acknowledgments.
Contributors.
1. Let There Be Light, Jack Park.
Opening Salvo.
Resources.
Topic Maps: General.
Topic Map Software: Commercial.
Topic Map Software: Open Source.
What's in Here?
Historical and Background Chapters.
Technical Chapters.
Forward-Thinking Chapters.
2. Introduction to the Topic Maps Paradigm, Michel Biezunski.
Managing Complex Knowledge Networks.
Primary Constructs.
Topics.
Associations.
Names.
Scopes and Namespaces.
Rules for Merging Topic Maps.
The Big Picture: Merging Information and Knowledge.
A Step Toward Improved Interconnectivity.
Design Principles for XTM.
Simplicity.
Neutrality.
From ISO/IEC 13250 to XTM.
Summary.
Acknowledgments.
3. A Perspective on the Quest for Global Knowledge Interchange, Steven R. Newcomb.
Information Is Interesting Stuff.
Information and Structure Are Inseparable.
Formal Languages Are Easier to Compute Than Natural Languages.
Generic Markup Makes Natural Languages More Formal.
A Brief History of the Topic Maps Paradigm.
Data and Metadata: The Resource-Centric View.
Metametadata, Metametametadata . . ..
Subjects and Data: The Subject-Centric View.
Understanding Sophisticated Markup Vocabularies.
The Topic Maps Attitude.
Summary.
4. The Rise and Rise of Topic Maps, Sam Hunting.
Milestones in Standards and Specifications.
XTM 1.0 versus ISO 13250.
OASIS.
Current ISO Activities.
Milestones in Software.
The Future of Topic Maps.
The State of the Paradigm.
The Near Future.
5. Topic Maps from Representation to Identity: Conversation, Names, and Published Subject Indicators, Bernard Vatant.
What Is the Conversation About?
A Finger Pointing at a Planet.
So What about Published Subject Indicators?
PSIs Are Binding Points for Subject Identity.
PSIs Have to Meet High Quality Requirements.
PSIs Are Good for Pragmatic Bottom-up Tasks.
PSIs Cannot Pretend to Universality nor Strong Symbolic Signification.
Back to the Conversation Subject.
Addendum: A Note on the Figures.
6. How to Start Topic Mapping Right Away with the XTM Specification, Sam Hunting.
XTM Topic Mapping.
Why Topic Maps?
Appetizer.
Introducing <topic>, <baseName>, <scope>, <baseNameString>, and <occurrence>.
Introducing <subjectIdentity>.
Introducing <scope>.
Main Course.
Introducing <association>, <member>, and <roleSpec>.
Introducing <instanceOf>.
Dessert.
Brandy, Cigars.
Introducing <variant>, <variantName>, and <parameters>.
Introducing <resourceData>.
Paying the Bill and Putting on Your Coat.
Summary.
Acknowledgments.
Resources.
7. Knowledge Representation, Ontological Engineering, and Topic Maps, Leo Obrst and Howard Liu.
Knowledge as Interpretation.
Data, Knowledge, and Information.
Knowledge Issues: Acquisition, Representation, and Manipulation.
The Roots of Ontological Engineering: Knowledge Technologies.
Root: Knowledge Representation.
Root: Knowledge Engineering.
Slightly Shriveled Root: Expert Systems (and Their Deficiencies).
New Knowledge Technology Branches: Toward Ontological Engineering.
Branch: The Formalization of Semantic Networks and the Rise of Description Logics.
Branch: Constraint and Logic Programming.
Ontological Engineering.
Ontologies and Topic Maps.
Ontologies.
How Ontologies Relate to Topic Maps.
How to Build an Ontology.
Ontology-Driven Topic Maps.
The Advantages of the Ontology-Driven Topic Maps Approach.
The Future of the Ontology-Driven Topic Maps Approach.
Summary.
Acknowledgments.
References.
Selected Information and Research Sites.
8. Topic Maps in the Life Sciences, John Park and Nefer Park.
A Literature Review.
The Need for Classification.
The Five Kingdoms.
Kingdom Animalia.
Creating Topic Maps for a Web Site.
A First View.
Developing the XTM Document.
Where Are We Now?
Summary.
Resources for More Information on the Life Sciences.
9. Creating and Maintaining Enterprise Web Sites with Topic Maps and XSLT, Nikita Ogievetsky.
The XTM Framework for the Web.
XTM as Source Code for Web Sites.
HTML Visualization of Topic Map Constructs.
Topics.
Special <topic> Elements: Root.
The Special Topic Map Website Ontology Layer.
XSLT Layers.
The XSLT Layout Layer.
The XSLT Back-End and Presentation Layers.
Querying Topic Types.
Querying and Displaying Topic Names 190
Querying and Displaying Topic Occurrences.
Querying and Displaying Topic Associations.
Summary.
Acknowledgments.
References.
10. Open Source Topic Map Software.
About Open Source Software.
Four Projects.
SemanText, Eric Freese.
Browsing Topic Maps.
Creating and Modifying Topic Maps.
Developing Inference Rules.
Future Plans.
Summary.
XTM Programming with TM4J, Kal Ahmed.The TM4J Core API.
File Organization and Packaging.
Package Dependencies.
Getting Started.
Using the Basic API Features.
Loading a Topic Map.
Creating Implicit Topics.
Saving a Topic Map.
Using the Advanced API Features.
Property Change Listeners.
TMP3—A Sample Topic Map Processing Application.
Defining the Topic Map Ontology.
Designing the Application.
Implementing the Application.
Extending the Application.
TM4J Future Directions.
Summary.
Nexist Topic Map Testbed, Jack Park.The Development of Nexist.
The Past.
The Present.
Use Cases.
Design Requirements.
The Persistent XTM Engine.
The Persistent Store.
The XTM Engine.
The User Interface.
The Server User Interface.
The Client User Interface.
Summary.
References.
GooseWorks Toolkit, Sam Hunting.Program Design.
GwTk's Omnivorous Nature.
ISO Compliance.
Use Cases.
Query Language.
Current Tools.
Summary.
11. Topic Map Visualization, Benedicte Le Grand.Requirements for Topic Map Visualization.
Different Uses for Topic Maps.
Representation Requirements.
Navigation Requirements.
Visualization Techniques.
Current Topic Map Visualizations.
General Visualization Techniques.
Summary.
References.
12. Topic Maps and RDF, Eric Freese.A Sample Application: The Family Tree.
RDF and Topic Maps.
An Introduction to RDF.
The RDF Data Model.
RDF XML Syntax.
RDF Schema.
The Similarities.
The Differences.
Combining Topic Maps and RDF.
Modeling RDF Using Topic Map Syntax.
Example 1: Markup Schemes.
Example 2: Topic Reification.
Example 3: Associations 301
Example 4: Bag Data Structure.
Example 5: Another Association.
Example 6: Multiple Occurrences.
Example 7: Another Bag Data Structure.
Example 8: RDF.
Example 9: Sorted Data Structures.
Example 10: Aggregation.
Example 11: Relational Data Structures.
Example 12: Dublin Core Metadata.
Summary.
References.
13. Topic Maps and Semantic Networks, Eric Freese.Semantic Networks: The Basics.
Comparing Topic Maps, RDF, and Semantic Networks.
Building Semantic Networks from Topic Maps.
Published Subject Indicators.
Association Properties.
Type Hierarchies.
Topic Map Schemas.
Harvesting the Knowledge Identified in Markup.
Identifying and Interpreting the Knowledge Found within Documents.
Summary.
References.
14. Topic Map Fundamentals for Knowledge Representation, H. Holger Rath.A Simple KR Example.
A Quick Review of Concepts for Topic Maps and KR.
Topic Map Templates.
Class Hierarchies.
Superclass-Subclass Relationship as Association.
Class-Instance Relationship as Association.
Association Properties.
Inference Rules.
An Inference Rule Example.
Consistency Contraints.
Constraint Patterns.
Topic Class Example.
Association Class Example.
Constraints and Class Hierarchies.
Summary.
References.
15. Topic Maps in Knowledge Organization, Alexander Sigel.Suggestions for Reading This Chapter.
The Overlap between KO and TMs.
KO, Knowledge Structures, and TMs.
KOxTM: Impact Directions and Open Questions.
What Is KO?
Some Definitions: What Is and Does KO? To What End KO?
Some Elements of KO Theory: On Problems and Principles.
KO in Practice.
KO as a Use Case for TMs.
KO: A Primary Use Case for TMs.
Knowledge Networks in KM: A Typical KOxTM Use Case.
KO on Topic Map Core Concepts (the "T-A-O" and "I-F-S" of Topic Maps).
The Potential Value of TMs for KO.
Temporary Impediments to TM Adoption: KO Prejudices.
KO Challenges That Recur with TMs.
Examples of KO Issues That Recur with TMs.
Illustrative Examples.
Shorter Examples of Fruitful KO with TMs.
Toward a TM on KO Resources: First Experiences.
A Look into the Future: Toward Innovative TM-Based Information Services.
Summary.
Acknowledgments.
Selected Abbreviations.
References.
16. Prediction: A Profound Paradigm Shift, Kathleen M. Fisher.Language.
Transmitting the Word.
Lightness of Being.
A Brief History of Knowledge Representation and Education.
The Ephemeral Nature of Many New Ideas.
What the Research Suggests about Knowledge Representation and Learning.
Students Learn from Semantic Networks.
Students' Models Become Increasingly Similar to Instructors' Models.
Constructing Semantic Networks Alters the Ways We Think and Learn.
Semantic Network-Based Courses Teach, Not Just Tell.
Understanding Relations Is Understanding.
A Paradigm Shift: Patterning Speech to Patterning Thought.
Summary.
Acknowledgments.
References.
17. Topic Maps, the Semantic Web, and Education, Jack Park.What Is the Semantic Web?
How Can Topic Maps Play an Important Role in the Semantic Web?
What's Next?
Education on the Web.
Constructivist Learning Theory.
Principles of Constructivist Learning.
Toward Constructivist Learning Environments.
IBIS.
Topic Maps.
Toward an Implementation.
An Application.
Closing Salvo.
References.
Glossary.Appendix A: Tomatoes Topic Map.Appendix B: Topic Map for Chapter 9.Appendix C: XSLT Style Sheet for Chapter 9.Appendix D: Genealogical Topic Map.Index. 0201749602T07022002Preface
A human being is part of a whole, called by us the "Universe," a part limited in time and space. He experiences himself, his thoughts and feelings, as something separated from the rest--a kind of optical delusion of his consciousness. This delusion is a kind of prison for us, restricting us to our personal desires and to affection for a few persons nearest us. Our task must be to free ourselves from this prison by widening our circles of compassion to embrace all living creatures and the whole of nature in its beauty. Albert Einstein, What I Believe, 1930
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 would help them form better predictions of fruit frost, irrigation needs, and pesticide needs. My program, which ran on an Apple II 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 issue of a National Public Radio discussion focused on the nature and future of literature. Listening to that discussion 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 (thatUs 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 XML2000 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 a book 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.
Each contribution to the Open Knowledge Systems series is intended to be a living document, meaning that each work will be available at a web site, the entire content of which will be browsable and supported with an online forum such that topics discussed in the books can be further discussed online.
This book is about Topic Maps, particularly Topic Maps implemented in the XTM Version 1.0 Standard format, as conceived by the XTM Authoring Group, which was started by an experienced group of individuals along with the vision and guidance of Steven Newcomb and Michel Biezunski, both contributing authors in this book. As with many new technologies, the XTM standard is, in most regards, not yet complete. In fact, a standard like XTM can never be complete simply because such standards must co-evolve 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.
Because of my view that solution space, itself, is co-evolving 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 sand box 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. We have several chapters, one on Ontological Engineering, one on Knowledge Representation, and one on Knowledge Organization. Two talk in some detail about semantic networks, and others go heavily into how people learn. ItUs 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: use the least amount of central management; let the authors sort it out for themselves. History will tell us if this approach works.
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