这本经典教材提供了知识表示方面广泛的知识，作为这一领域的公认权威著作，Sowa在介绍新思想的同时捕捉到这一学科的最新成就，并且将逻辑学、哲学、语言学和计算机科学结合到知识及其可计算形式转化的研究中。本书强调了作为人工智能、数据库设计和面向对象编程的各种技术和表示法基础的逻辑原理。因为这是一门交叉学科，所以书中还包含了大量的哲学和语言学的知识。每种新思想在第一次提到时都会给出定义，所有的形式体系都在正文中讲座或在附录中综述。
本书特点
■ 示例用多种计算机语言（规则、框架、PROLOG、SQL、Java和CLIPS）说明，以便读者可以学习如何将理论概念应用到使用相应软件的现实环境中，不要求读者具有语言或系统方面的任何预备知识
■ 逻辑过程以标准谓词演算表示法和更易读的概念图表示法介绍，这两种表示法在附录中作了概述
■ 每个理论主题都用实际的例子说明，书中包括大量不同难度的习题，酒店预定系统是每章结尾一系列习题的基础
■ 最后一章知识获取和知识共享阐述了理论如何用于集成知识库以及在异构系统间用于知识交换
Preface
CHAPTER ONE Logic
1.1 Historical Background
1.2 Representing Knowledge in Logic
1.3 Varieties of Logic
1.4 Names, Types, and Measures
1.5 Unity Amidst Diversity
CHAPTER TWO Ontology
2.1 Ontological Categories
2.2 Philosophical Background
2.3 Top-Level Categories
2.4 Describing Physical Entities
2.5 Defining Abstractions
2.6 Sets, Collections, Types, and Categories
2.7 Space and Time
CHAPTER THREE Knowledge Representations
3.1 Knowledge Engineering
3.2 Representing Structure in Frames
3.3 Rules and Data
3.4 Object-Oriented Systems
3.5 Natural Language Semantics
3.6 Levels of Representation
CHAPTER FOUR Processes
4.1 Times, Events, and Situations
4.2 Classification of Processes
4.3 Procedures, Processes, and Histories
4.4 Concurrent Processes
4.5 Computation
4.6 Constraint Satisfaction
4.7 Change
CHAPTER FIVE Purposes, Contexts, and Agents
5.1 Purpose
5.2 Syntax of Contexts
5.3 Semantics of Contexts
5.4 First-Order Reasoning in Contexts
5.5 Modal Reasoning in Contexts
5.6 Encapsulating Objects in Contexts
5.7 Agents
CHAPTER SIX Knowledge Soup
6.1 Vagueness, Uncertainty, Randomness, and Ignorance
6.2 Limitations of Logic
6.3 Fuzzy Logic
6.4 Nonmonotonic Logic
6.5 Theories, Models, and the World
6.6 Semiotics
CHAPTER SEVEN Knowledge Acquisition and Sharing
7.1 Sharing Ontologies
7.2 Conceptual Schema
7.3 Accommodating Multiple Paradigms
7.4 Relating Different Knowledge Representations
7.5 Language Patterns
7.6 Tools for Knowledge Acquisition
APPENDIX A Summary of Notations
A.1 Predicate Calculus
A.2 Conceptual Graphs
A.3 Knowledge Interchange Format
APPENDIX B Sample Ontology
B.1 Principles of Ontology
B.2 Top-Level Categories
B.3 Role and Relation Types
B.4 Thematic Roles
B.5 Placement of the Thematic Roles
APPENDIX C Extended Example
C.1 Hotel Reservation System
C.2 Library Database
C.3 ACE Vocabulary
C.4 Translating ACE to Logic Answers to Selected Exercises Bibliography
Name Index
Subject Index
Special Symbols
Like Socrates, knowledge engineers and systems analysts play the role of midwife in bringing knowledge forth and making it explicit. They display the implicit knowledge about a subject in a form that programmers can encode in algorithms and data structures. In the programs themselves, the link to the original knowledge is only mentioned in comments, which the computer cannot understand. To make the hidden knowledge accessible to the computer, knowledge-based systems and object-oriented systems are built around declarative languages whose form of expression is closer to human languages. Such systems help the programmers and knowledge engineers reflect on "the treasures contained in the knowledge" and express it in a form that both the humans and the computers can understand.
Knowledge representation developed as a branch of artificial intelligence-- the science of designing computer systems to perform tasks that would normally require human intelligence. But today, advanced systems everywhere are performing tasks that used to require human intelligence: information retrieval, stock- market trading,resource allocation, circuit design, virtual reality, speech recognition, and machine translation. As a result, the A1 design techniques have converged with techniques from other fields, especially database and object-oriented systems. This book is a general textbook of knowledge-base analysis and design, intended for anyone whose job is to analyze knowledge about the real world and map it to a computable form.
LOGIC, ONTOLOGY, AND COMPUTATION.Knowledge representation is a multidisciplinary subject that applies theories and techniques from three other fields:
1. Logic provides the formal structure and rules of inference.
2. Ontology defines the kinds of things that exist in the application domain.
3. Computation supports the applications that distinguish knowledge representation from pure philosophy.Without logic, a knowledge representation is vague, with no criteria for determining whether statements are redundant or contradictory. Without ontology, the terms and symbols are ill-defined, confused, and confusing. And without computable models, the logic and ontology cannot be implemented in computer programs.Knowledge representation is the application of logic and ontology to the task of constructing computable models for some domain.
The readers of this book should have some experience in analyzing a problem,identifying the kinds of things that have to be represented, and mapping them to a computable form. This level of experience can be expected of computer science students. Yet because of the interdisciplinary nature of the subject, the book contains considerable material on philosophy and linguistics. Therefore, it is also suitable for philosophy and linguistics students who have some background in artificial intelligence or computer programming. While writing the book, I have used early drafts in graduate-level courses in computer science at Polytechnic University and in the program on Philosophy and Computers and Cognitive Science at Binghamton University.
EXERCISES. At the end of each chapter, the exercises introduce topics that illustrate, supplement, and extend the main presentation. Instead of emphasizing symbol manipulation, the exercises address the problems of analyzing informal specifications and selecting an appropriate ontology for representing them. In effect, the "word problems," which usually give high-school algebra students the most difficulty, are closer to the central issues of knowledge representation than the purely technical problems of manipulating symbols. Answers and hints for a representative sample of the exercises are induded at the end of the book.
All of the major knowledge representations are discussed, analyzed, and related to logic: rules, frames, semantic networks, object-oriented languages, Prolog, Java,SQL, Petri nets, and the Knowledge Interchange Format (KIF). The two bask notations used for logic are predicate calculus and conceptual graphs. Predicate calculus is the traditional logic notation that students must know in order to read the literature of AI and computer science. Conceptual graphs are a two-dimensional form of logic that is based on the semantic networks of AI and the logical graphs of C. S. Peirce. Both notations are exactly equivalent in their semantics, and instructors may choose to use either or both in lectures and exercises.
Examples in this book are illustrated in several languages, but no prior knowledge of any of them is expected. The emphasis is on the semantic principles underlying all languages rather than the syntactic details of particular languages.Although computer exercises can help to show how the theory is applied, this book can be used without any special computer accompaniment.
ORGANIZATION. Chapter 1 introduces logic through a historical survey, ranging from Aristode's syllogisms to the modern graphic and algebraic systems. The details of the predicate calculus and conceptual graph notations are summarized in Appendix A. For students who have little or no background in logic, the instructor can spend extra time on Chapter 1 and Appendix A to use this book as an introduction to logic. For more advanced students, the instructor can cover Chapter 1 quickly and spend more time on the topics in later chapters.
Chapter 2, which is the most philosophical in the book, introduces ontolog3 the study of existence. Ontology defines the categories of things that are expressed in the predicates of predicate logic, the slots in frames, the tables of a database, or the classes of an object-oriented system. Logic is pure form, and ontology provides the content that is expressed in that form. Depending on the interests of students and the instructor, this chapter can be surveyed briefly or covered in depth.
Chapter 3 introduces the principles of knowledge representation and their role in adapting logic and ontology to the task of constructing computable models of an application domain. It shows how logic and ontology are embodied in a variety of computational languages. This chapter is central to computer applications, but it can be surveyed for students of linguistics or philosophy.
Chapter 4 presents methods for representing dynamically changing processes and events. Petri nets and daraflow graphs are introduced as supplementary notations, which can be translated either to conventional programming languages or to logic in the predicate calculus or conceptual graph notations. Petri nets serve as a bridge between the procedural programming techniques and the declarative logicbased approach that is emphasized in the other chapters.
Chapter 5 shows how purpose and context affect knowledge representation and the various theories of modal and intentional logic. These theories are applied to the encapsulated objects of 0-0 systems and to the design of interacting agents. This chapter has the most detailed logical development, but much of it can be skipped for students whose background in logic is weak.
Chapter 6, on "knowledge soup," stresses the limitations of logic. It discusses the vague, uncertain, unanalyzed, and often inconsistent mix of facts, opinions, and rules of thumb that people have in their heads. It presents the techniques for reconciling logic to the unpredictable, continuously variable aspects of reality.These techniques are not rejections of logic, but methods for adapting logic to the complexities of the real world.
Chapter 7 discusses the problems of knowledge sharing and the ongoing efforts related to the ANSI and ISO projects on ontology and conceptual schemes. It illustrates critical issues in using logic-based techniques to facilitate communication and interoperability of heterogeneous computer systems.
The first section of every chapter is more introductory and less technical than the remaining sections, and the first paragraph of every section gives a quick overview of the rest. Therefore, readers can survey any chapter by reading just the first section and the first paragraph of each remaining section. While skimming through a chapter, readers should glance at the illustrations to get.an overview of the topics that are covered.
CAST Or CHARACTERS.Science is a human subject, developed by people who step on each other's toes at least as often as they stand on each other's shoulders. The five philosophers to whom this book is dedicated have been admired and trampled more than most. Their theories and practices are among the best available examples of how logic and ontology can be applied to the representation of knowledge in science, business, and everyday life. For a testimonial to their influence, note the references to them in the index of this book.
As Peirce said, every scientist is deeply indebted to a "community of inquirers" whose contributions, criticisms, and collaboration are essential to the development of the science. While writing this book, I benefited enormously from the overlapping communities in which I participated. Among them are my students and colleagues at SUNY Binghamton and Polytechnic University; the members of the ANSI and ISO working groups on conceptual schemas, ontologies, and the CG and KIF standards, which were chaired by Sandra Perez, Tony Sarris, John Sharp,and Baba Piprani; and the FANTA project at IBM, which included Fan Hsu, Bob Spillers, and Martin van den Berg.
My greatest debt is to the community of the conceptual graph workshops and the International Conferences on Conceptual Structures. Since I don't have the space to list all the participants, I'll just list the organizers of the conferences and the editors of the proceedings: Michel Chein, Walling Cyre, Harry Delugach, Judy Dick, Peter Eklund, Gerard Ellis, John Esch, Jean F-argues, Mary Keeler, Bob Levinson, Dickson Lukose, Guy Mineau, Bernard Moulin, Marie-Laure Mugnier,Tim Nagle, Heather Pfeiffer, Bill Rich, Leroy Searle, Bill Tepfenhart, Eileen Way, and RudolfWille. I gratefully thank them and everyone mentioned in the proceedings they edited, which are listed in the bibliography of this book.
My community also indudes many people whose contributions are not ade-quately represented in the above lists: Jaime Carbonell, Norman Foo, Benjamin Grosof, Mike Genesereth, Nicola Guarino, Ed Hovy, Fritz Lehmann, John McCarthy, Michael McCord, Robert Meersman, Julius Moravcsik, Mary Neff,Paula Newman, Paul Rosenbloom, Peter Simons, Doug Skuce, Cora Sowa, and Wlodek Zadrozny. Finally; I thank the editors and staff of Brooks/Cole for their patience in waiting for this book to be finished in December for more Decembers than I would like to admit.
John E Sowa
Croton-on-Hudson, New York
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