Knowledge-Based Intelligent Information and Engineering Systems

1. Front Matter

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2. Keynote Lectures

   1. Distributed Prediction and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

      • Gail A. Carpenter

      Pages 1-4

   2. The Brain’s Cognitive Dynamics: The Link between Learning, Attention, Recognition, and Consciousness

      • Stephen Grossberg

      Pages 5-12

   3. Adaptive Data Based Modelling and Estimation with Application to Real Time Vehicular Collision Avoidance

      • Chris J. Harris

      Pages 13-14

   4. Creating a Smart Virtual Personality

      • Nadia Magnenat-Thalmann

      Pages 15-16

   5. Intelligent Navigation on the Mobile Internet

      • Barry Smyth

      Pages 17-18

   6. The Evolution of Evolutionary Computation

      • Xin Yao

      Pages 19-20

3. General Session Papers

   1. Knowledge-Based Systems

      1. A Unified Model Maintains Knowledge Base Integrity

         • John Debenham

         Pages 21-27

      2. Formal Argumentation Frameworks for the Extended Generalized Annotated Logic Programs

         • Takehisa Takahashi, Yuichi Umeda, Hajime Sawamura

         Pages 28-38

      3. Specifying and Validating Reactive Systems with CommonKADS Methodology

         • Maamar El-Amine Hamri, Claudia Frydman, Lucile Torres

         Pages 39-44

      4. TMMT: Tool Supporting Knowledge Modelling

         • Guy Camilleri, Jean-Luc Soubie, Joseph Zalaket

         Pages 45-52

      5. State-Based Planning with Numerical Knowledge

         • Joseph Zalaket, Guy Camilleri

         Pages 53-60

      6. KAMET II: An Extended Knowledge-Acquisition Methodology

         • Osvaldo Cairó, Julio César Alvarez

         Pages 61-67

      7. Automated Knowledge Acquisition by Relevant Reasoning Based on Strong Relevant Logic

         • Jingde Cheng

         Pages 68-80

      8. ConcepTool: Intelligent Support to the Management of Domain Knowledge

         • Ernesto Compatangelo, Helmut Meisel

         Pages 81-88

      9. Combining Revision Production Rules and Description Logics

         • Chan Le Duc, Nhan Le Thanh

         Pages 89-98

      10. Knowledge Support for Modeling and Simulation

          • Michal Ševčenko

          Pages 99-103

      11. Expert System for Simulating and Predicting Sleep and Alertness Patterns

          • Udo Trutschel, Rainer Guttkuhn, Anneke Heitmann, Acacia Aguirre, Martin Moore-Ede

          Pages 104-110

      12. Two Expert Diagnosis Systems for SMEs: From Database-Only Technologies to the Unavoidable Addition of AI Techniques

          • Sylvain Delisle, Josée St-Pierre

          Pages 111-125

      13. Using Conceptual Decision Model in a Case Study

          • Miki Sirola

          Pages 126-133

2.1 Text Summarization “Text summarization is the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks)” [3]. Basic and classical articles in text summarization appear in “Advances in automatic text summarization” [3]. A literature survey on information extraction and text summarization is given by Zechner [7]. In general, the process of automatic text summarization is divided into three stages: (1) analysis of the given text, (2) summarization of the text, (3) presentation of the summary in a suitable output form. Titles, abstracts and keywords are the most common summaries in Academic papers. Usually, the title, the abstract and the keywords are the first, second, and third parts of an Academic paper, respectively. The title usually describes the main issue discussed in the study and the abstract presents the reader a short description of the background, the study and its results. A keyword is either a single word (unigram), e.g.: ‘learning', or a collocation, which means a group of two or more words, representing an important concept, e.g.: ‘machine learning', ‘natural language processing'. Retrieving collocations from text was examined by Smadja [5] and automatic extraction of collocations was examined by Kita et al. [1].

• case-based reasoning
• data mining
• fuzzy
• fuzzy logic
• human-computer interaction (HCI)
• information retrieval
• intelligent systems
• knowledge discovery
• knowledge-based system
• knowledge-based systems
• learning
• machine learning
• ontology
• uncertainty

• Computing Laboratory, Oxford University, United Kingdom

  Vasile Palade

• Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, Brighton, United Kingdom

  Robert J. Howlett

• Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, Adelaide, Australia

  Lakhmi Jain

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