Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. 

This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.

*Authors are well-recognized experts in the field who have applied the techniques to real-world problems 
* Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems 
*Offers the first true synthesis of the field in over a decade

Table of Contents

1 Introduction * 2 The Language of First-Order Logic *3 Expressing Knowledge * 4 Resolution * 5 Horn Logic * 6 Procedural Control of Reasoning * 7 Rules in Production Systems * 8 Object-Oriented Representation * 9 Structured Descriptions * 10 Inheritance * 11 Numerical Uncertainty *12 Defaults *13 Abductive Reasoning *14 Actions * 15 Planning *16 A Knowledge Representation Tradeoff * Bibliography * Index

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Knowledge Representation and Reasoning

We argue that accessing the transitive closure of relationships is an important component of both databases and knowledge representation systems in Artificial Intelligence. The demands for efficient access and management of large relationships motivate the need for explicitly storing the transitive closure in a compressed and local way, while allowing updates to the base relation to be propagated incrementally. We present a transitive closure compression technique, based on labeling spanning trees with numeric intervals, and provide both analytical and empirical evidence of its efficacy, including a proof of optimality.

Efficient management of transitive relationships in large data and knowledge bases

In this book, Nirenburg and Raskin present an important body of work in computational linguistics that they and their colleagues have been developing over the past 20 years. For a unifying perspective, they organize their assumptions, theories, and techniques around the theme of ontological semantics. Along the way, they critique many alternative views of semantics, which they distinguish from their own. Their analyses contribute to a much-needed debate about the history and future of computational linguistics, but to preserve some balance, teachers and students should keep a few of the alternatives on their reference shelf. The book is divided into two parts: a philosophical part I and a practical part II. The first part consists of an introductory chapter 1 and four chapters that survey important but controversial issues about linguistics, both theoretical and computational. In those chapters, the authors make a good case for their version of ontological semantics, but the alternatives are not treated in detail. In part II, the authors present their text-meaning representation (TMR) and demonstrate how it is used in language analysis. Any discussion of technical material must use some notation, and TMR is sufficiently flexible to illustrate a wide range of semantic-based methods that could be adapted to many other formalisms. For most readers, part II would be the more important. Chapter 1 is a good 25-page overview of computational linguistics with an emphasis on semantics. Students and novices, however, need examples, and none are given until chapter 6. The authors suggest that ‘‘a well-prepared and/or uninterested reader’’ skip the remainder of part I and go straight to chapter 6, which begins with an excellent five-page example. The authors follow that advice when they teach courses from this text. In Chapter 2, the authors present their ‘‘Prolegomena to the Philosophy of Linguistics.’’ Their ideas are well taken, and some are as old as Socrates: Examine the assumptions, challenge conventional wisdom, and test conclusions against experience. The basis of their approach is what they call the four components of a scientific theory:

Ontological semantics

To develop sophisticated database management systems, there is a need to incorporate more understanding of the real world in the information that is stored in a database. Semantic data models have been developed to try to capture some of the meaning, as well as the structure, of data using abstractions such as inclusion, aggregation, and association. Besides these well-known relationships, a number of additional semantic relationships have been identified by researchers in other disciplines such as linguistics, logic, and cognitive psychology. This article explores some of the lesser-recognized semantic relationships and discusses both how they could be captured, either manually or by using an automated tool, and their impact on database design. To demonstrate the feasibility of this research, a prototype system for analyzing semantic relationships, called the Semantic Relationship Analyzer, is presented.

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Understanding semantic relationships

https://hal.inria.fr/inria-00415930/document

EASY: Efficient semAntic Service discoverY in pervasive computing environments with QoS and context support

The development of a simple question-answering system is considered. In particular, methods are described that are designed to supplement a deductive question-answering algorithm that is now operational. The algorithm draws on a base of logical propositions organized as a semantic net. The net permits selective access to the contents of individual mental worlds and narratives, to sets of entities of any specified type, and to propositions involving any specified entity and classified under any specified topic. The problems involved in determining type, part-of, color, and time relationships are discussed. It is shown that much combinatory reasoning in a question-answering system can be short-circuited by the use of special graphical and geometrical methods. 13 references.

Taugher

Papers

Determining Type, Part, Color, and Time Relationships