Knowledge Representation and the Semantics of Natural Language

1. Front Matter

   Pages I-XVIII

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2. Knowledge Representation with MultiNet

   1. Introduction

      Pages 1-11

   2. Historical Roots

      Pages 13-16

   3. Basic Concepts

      Pages 17-43

   4. Semantic Characterization of Objects

      Pages 45-83

   5. Semantic Characterization of Situations

      Pages 85-111

   6. The Comparison of Entities

      Pages 113-130

   7. The Spatio-temporal Characterization of Entities

      Pages 131-162

   8. Modality and Negation

      Pages 163-196

   9. Quantification and Pluralities

      Pages 197-217

   10. The Role of Layer Information in Semantic Representations

       Pages 219-231

   11. Relations Between Situations

       Pages 233-277

   12. Lexicon and Knowledge Representation

       Pages 279-303

   13. Question Answering and Inferences

       Pages 305-330

   14. Software Tools for the Knowledge Engineer and Sample Applications

       Pages 331-356

   15. Comparison Between MultiNet and Other Semantic Formalisms or Knowledge Representation Paradigms

       Pages 357-391

3. The Representational Means of MultiNet

   1. Overview and Representational Principles

      Pages 395-407

   2. Means for Expressing Classification and Stratification

      Pages 409-438

   3. Relational and Functional Means of Representation

      Pages 439-591

4. Back Matter

   Pages 593-647

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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 arti?cial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.

• artificial intelligence
• computational linguistics
• intelligence
• knowledge representation
• linguistics
• modeling
• natural language processing
• semantics

• FernUniversität Hagen Fak. Mathematik und Informatik, Hagen, Germany

  Hermann Helbig

-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)

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