Knowledge Representation and the Semantics of Natural Language / Edition 1

by Hermann Helbig

Overview

Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the pres- vation of cultural achievements and their transmission from one generation to the other. During the last few decades, the—ood of digitalized information has been growing tremendously. This tendency will continue with the globali- tion of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical und- standing and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this c- text, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the g- eration of natural language expressions from formal representations. This book presents a method for the semantic representation of natural l- guage expressions (texts, sentences, phrases, etc. ) which can be used as a u- versal knowledge representation paradigm in the human sciences, like lingu- tics, cognitive psychology, or philosophy of language, as well as in com- tational linguistics and in artificial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.

Product Details

ISBN-13:	9783540244615
Publisher:	Springer Berlin Heidelberg
Publication date:	11/02/2005
Series:	Cognitive Technologies
Edition description:	2006
Pages:	647
Product dimensions:	6.10(w) x 9.25(h) x 0.05(d)

About the Author

-1961-68 M.S. in Physics from the University Leipzig (Diploma in Quantum Theory)

-1968-69 Research assistant at the University Leipzig

-1970-89 Researcher in the fields of Artificial Intelligence (AI) and Computational Linguistics (CL)

with Robotron, Head of the AI Laboratory

-1976 Ph.D. in Computer Science (Promotion, Dr.rer.nat., in the field of AI)

-1986 Habilitation (Dr.rer.nat.habil.) in the field of Knowledge Representation

-1988-92 Lecturer for Artificial Intelligence at the TU Dresden

-1989-92 Researcher at SRS Dresden and Siemens-Nixdorf (Development of Geographic Information Systems)

- since 1992 Full Professorship at FernUniversität in Hagen

Head of the Chair: Intelligent Information and Communication Systems

-1997-02 Sabbatical Stays at ICSI in Berkeley and at the Universities of Buffalo (USA), Edinburgh, Sheffield and London (Great Britain)

Table of Contents

Knowledge Representation with MultiNet.- Historical Roots.- Basic Concepts.- Semantic Characterization of Objects.- Semantic Characterization of Situations.- The Comparison of Entities.- The Spatio-temporal Characterization of Entities.- Modality and Negation.- Quantification and Pluralities.- The Role of Layer Information in Semantic Representations.- Relations Between Situations.- Lexicon and Knowledge Representation.- Question Answering and Inferences.- Software Tools for the Knowledge Engineer and Sample Applications.- Comparison Between MultiNet and Other Semantic Formalisms or Knowledge Representation Paradigms.- The Representational Means of MultiNet.- Overview and Representational Principles.- Means for Expressing Classification and Stratification.- Relational and Functional Means of Representation.