(ed.) Intelligent Agents for Data Mining and Information Retrieval

Chapter III: A Multi-Agent Approach to Collaborative Knowledge Production

Figure 1: Multilevel Architecture of Marts for Knowledge Production

Figure 2: Sequence of Events for the Learning Object Evaluation Scenario

Figure 3: Execution Example of the Interaction Protocol

Chapter IV: Customized Recommendation Mechanism Based on Web Data Mining and Case-Based Reasoning

Figure 1: Taxonomy of Web Data Mining (Adapted from Pyle, 1999, and Srivastava et al., 2000)

Figure 2: Research Methodology of Hybrid Recommendation

Figure 3: The Structure of CAR

Figure 4: Preprocessed Web Log Database

Figure 5: Case-Based Knowledge Base

Figure 6: Hybrid Recommendation Results of CAR

Chapter V: Rule-Based Parsing for Web Data Extraction

Figure 1: Generic Web Multi-Agent Based Architecture

Figure 2: Semi-Automatic Web Parser Architecture

Figure 3: Web Page Example and HTML Code with Several Types of Structures

Figure 4: HTML and DataOutput-Rules to Extract the Information Stored in the Selected Structures

Figure 5: Architecture for a Web Agent

Figure 6: SimpleNews Architecture

Figure 7: HTML and DataOutput-Rules to Extract the Headlines from the Web Page Request

Figure 8: Web Page Example and HTML Code Provided by http://www.elpais.es

Chapter VI: Multilingual Web Content MiningA User-Oriented Approach

Figure 1: User-Oriented, Concept-Based Approach for Multilingual Web Content Mining

Chapter VII: A Textual Warehouse ApproachA Web Data Repository

Figure 1: Architecture of Textual Warehouses

Figure 2: Generic Model of Textual Warehouses

Figure 3: Example of Logical Structure Determination for Well-Formed Documents

Figure 4: Example of Logical Structure Determination for Valid Documents

Figure 5: Visualization of a Multidimensional Table

Figure 6: Generic Logical Structure Chosen by the User

Figure 7: Generic Logical Structure Modified by the User

Figure 8: Schema of Textual Mart

Figure 9: Multidimensional Table "Distribution"

Chapter VIII: Text Processing by Binary Neural Networks

Figure 1: Learning (left side) and Recalling (right side) Phase of the Technique

Figure 2: Learning (left side) and Recalling (right side) Phases of CMM

Figure 3: Histogram of Letters for Non-Repeated English Words

Figure 4: The Comparison of Three Methods of Coding

Figure 5: The Comparison of Speed of Conventional Techniques and CMM

Chapter IX: Extracting Knowledge from Databases and ANNs with Genetic ProgrammingIris Flower Classification Problem

Figure 1: Distribution of the Three Classes

Figure 2: Distributions Obtained for the Three Classes

Figure 3: Distributions Obtained from the Rules and from the Training Set

Figure 4: Obtained ANN

Figure 5: Distribution Obtained of the Three Classes Produced by the Rules from the ANN

Chapter X: Social Coordination with Architecture for Ubiquitous Agents CONSORTS

Figure 1: Theme Park Problem

Figure 2: CONSORTS: Architecture for Ubiuitous Agents

Figure 3: Plans and Congestion in Resource Space

Chapter XI: Agent-Mediated Knowledge Acquisition for User Profiling

Figure 1: A Fragment of a User Model

Figure 2: Architecture for Knowledge-Acquisition Sub-System

Chapter XII: Development of Agent-Based Electronic Catalog Retrieval System

Figure 1: Examples of PLIB Catalog Dictionary and Content

Figure 2: Concept of Multi-Agent Framework Bee-Gent

Figure 3: System Architecture of Agent-Based Electronic Catalog Retrieval System

Chapter XIII: Using Dynamically Acquired Background Knowledge for Information Extraction and Intelligent Search

Figure 1: XML Representation of Background Knowledge

Figure 2: XML Representation of an Unindexed Document

Figure 3: System Components and Interactions

Chapter XV: Taxonomy Based Fuzzy Filtering of Search Results

Figure 1: Recall-Precision Diagram of the Logic Operators for NB Training

Figure 2: Recall-Precision Diagram of the Logic Operators for SVM Training

Figure 3: Client vs. Server-Sided Filtering Systems

Figure 4: Fuzzy Filtering on the Web

Chapter XVI: Generating and Adjusting Web Sub-Graph Displays for Web Navigation

Figure 1: A Web Sub-Graph Display

Figure 2: Some Sub-Graphs Become Visible After the User's Interaction

Figure 3: A Sub-Graph Becoming Visible Makes Another One Invisible

Figure 4: A Web Page Corresponding to a Node is Shown Up

Figure 5: Another Web Site and Its Web Graph

Figure 6: A Web Sub-Graph for the Focused Node "Dept"

Figure 7: Navigating the Web Graph from the Node "Dept" to the Nodes "Staff", etc.

Figure 8: An Abstract Graph Layout

Figure 9: A Practical Graph

Figure 10: Layout Adjustment

Chapter XVIII: Networking E-Learning Hosts Using Mobile Agents

Figure 1: Mobile Agent Paradigm

Figure 2: Faded Information Field

Figure 3: Thesaurus Module

Figure 4: AI Search Engine Architecture

Figure 5: Overall Architecture

Figure 6: Network Configuration

Figure 7: Mobile Agent Traversal

Figure 8: Client-Server Architecture

Figure 9: Response Time Comparison

Figure 10: Course Document Structure

Figure 11: Document Search Process

Figure 12: Keyword Expansion