Knowledge Processing and Data Analysis

Name: Knowledge Processing and Data Analysis | First International Conference, KONT 2007, Novosibirsk, Russia, September 14-16, 2007,and First International Conference, KPP 2007, Darmstadt, Germany, September 28-30, 2007. Revised Selected Papers
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First International Conference, KONT 2007, Novosibirsk, Russia, September 14-16, 2007,and First International Conference, KPP 2007, Darmstadt, Germany, September 28-30, 2007. Revised Selected Papers

Karl Erich Wolff Dmitry E. Palchunov Nikolay G. Zagoruiko Urs Andelfinger(Herausgeber*in)

Springer (Verlag)

Erschienen am 28. Juli 2011

XVI, 321 Seiten

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Inhalt

Title
Preface
Organization
Table of Contents
Part I: Applications of Conceptual Structures
Conceptual Knowledge Processing: Theory and Practice
Conceptual Knowledge Processing
Theory
Practice
Exploring
Searching
Recognizing
Identification
Investigating
Analyzing
Making Aware
Deciding
Improving
Restructuring
Memorizing
Informing
Summary
References
Non-symmetric Indiscernibility
Indiscernibility
Functional Dependencies and Indiscernibility
Linguistic Variables
Decision Making
The Lattice of Rough Set Approximations
Conclusion
References
Computing Graph-Based Lattices from Smallest Projections
Introduction
Pattern Structures on Sets of Graphs
Analyzing Graph Datasets Using Lattice-Based Approaches
A Top-Down Algorithm
Top-Down and Bottom-Up Algorithms
Conclusions
References
Combined Logics of Knowledge, Time, and Actions for Reasoning about Multi-agent Systems
Introduction
Elements of Modal Logic
Combining Knowledge, Actions and Time
Propositional Logic for Epistemic Agents
Branching Temporal Logic with Actions
Combined Logics of Knowledge, Actions, and Time
Model Checking Problem for Combined Logics
References
Applications of Temporal Conceptual Semantic Systems
Introduction
Example: A Moving High Pressure Zone
Basic Conceptual Notions
The Scales of Our Example
Conceptual Semantic Systems
Main Ideas
Definition of a Conceptual Semantic System
Object Representation by Tuples of Semantic Concepts
Views, Selections, and Traces
Precise and Distributed Tuples
Temporal Conceptual Semantic Systems
States
Life Space and Life Track
Transitions
The Life Space Digraph
Particles and Waves in Temporal Conceptual Semantic Systems
An Application of TCSS: The Behavior of a Distillation Column
The Data of the Distillation Column
Visualization of a Life Track in a Nested Line Diagram
Conclusions and Future Research
References
Conceptual Representation of Gene Expression Processes
Introduction
Gene Expression Processes in Arthritic Patients
Temporal Concept Analysis
Data of Gene Expression Processes and Its Conceptual Representation
Organization of the Data
Conceptual Visualization of Data
Conceptual Scaling
Conceptual Time Systems with Actual Objects and a Time Relation (CTSOT)
Conceptual Time Systems for Six Arthritic Patients
Results
Two Destructive Proteins and Their Antagonist
Transcriptional Regulation of TGFB1 Effects
Discussion
References
From Published Expression and Phenotype Data to Structured Knowledge: The Arabidopsis Gene Net Supplementary Database and Its Applications
Introduction
The Structure of the AGNS Data
Terminological Systems
AGNS Ontology
Discussion of the Ontology
Implementation of the Ontology
Comparison with Existing Formalizations
Functions of Terminological Systems
Input and Processing of Data in AGNS
Tools for Data Analysis
Conclusion
References
Part II: Concept-Based Software
How Can Ontologies Contribute to Software Development?
Introduction
Basic Concepts
Analytics
Interactive Designing
Verbal Representation of Knowledge Bases
Conclusions
References
A Comparison of Content-Based Tag Recommendations in Folksonomy Systems
Introduction
Social Resource Sharing and Folksonomies
Related Work
Tag Recommendations as Text Classification Problem
Definition of the Problem
Classifiers
Evaluation Setting
Preprocessing
Training and Test Datasets
Experiments
Evaluation Settings
Comparison of the Classifiers
Conclusion and Outlook
References
Data Weeding Techniques Applied to Roget's Thesaurus
Introduction
Visual Reduction Techniques
Faceting and Plain Scaling
Pruning and Restriction
Decomposition and General Scaling
Data Weeding Techniques for Roget's Thesaurus
Conclusion
References
Part III: Ontologies as Conceptual Structures
Virtual Catalog: The Ontology-Based Technology for Information Retrieval
Introduction
Information Retrieval on the Internet
Measures of Efficiency of Information Retrieval
The Search for Scientific and Technical Information on the Internet
Mathematical Basis: Model-Theoretical Approach to the Formalization of Ontologies
Logical Means of Ontology Representation
Model-Theoretical Approach to the Formalization of Ontologies
Practical Realization. The Virtual Catalog
Description of the Virtual Catalog
Automation of the Development of Subject Domain Ontologies
Elaboration of Virtual Catalogs for Mathematics and Information Security
Conclusion
References
Ontology Development for Domains with Complicated Structures
Introduction
Domain Class Definition
Defining Level of Generality of Ontologies
Properties of Multilevel Ontologies for Domains with Complicated Structures
Method of Developing Ontology for Domain with Complicated Structure
Structure of Multilevel Ontology of Chemistry
A Fragment of the Ontology of Level 4
Using the Ontology of Fourth Level
Distinguishing Features of This Ontology Development Method
Intelligent Systems for Domains with Complicated Structures
Conclusion
References
Technology of Ontology Building for Knowledge Portals on Humanities
Introduction
Requirements to Knowledge Portal Ontology
Structure of Knowledge Portal Ontology
Definition of Portal Ontology
Structuring of Knowledge Portal Ontology
Technology of Ontology Building
Ontology Description Language
Ontology Editor
Features of Methodology of Ontology Building for a Knowledge Portal
Content-Based Access to Portal Content
Building an Ontology for Knowledge Portal on Archeology and Ethnography
Conclusion
References
Methods and Technologies of Digital Historical Factography
Introduction
Common Information Field
Basic Ontology
Data Input and Editing, Information Visualization
Conclusion
References
Establishment of Taxonomic Relationships in Linguistic Ontologies
Introduction
Criteria for Verification of Taxonomic Relationships
Confusion of Types and Roles
Defining Roles
Causes of Type-Role Confusion
Description of Roles in Thesaurus RuThes and Ontology ONST
Confusion of Taxonomic and Instance-Class Relationships
Confusion of Taxonomic Relationships and Part-Whole Relationships
Confusion of Taxonomic and Origin Relationships
Taxonomic Relationships and Clustering of Word Senses
Conclusion
References
Part IV: Data Analysis
Problems in Constructing an Empirical Theoryof Data Mining
Introduction
What Is an Empirical Theory?
State-of-the-Art DM Problem
Reference Points for Further Development
Function of Rival Similarity
Construction the Decision Rule (Algorithm FRiS-Stolp)
Selection of Informative Attributes (Algorithm FRiS-GRAD)
Construction of Classifications (Algorithm FRiS-Tax)
Application of FRiS-Function to the Decision of Other DM tasks
Strengthening of the Empirical DM Theory
Conclusion
References
Use of the FRiS-Function for Taxonomy, Attribute Selection and Decision Rule Construction
Introduction
Definition of the Function of Rival Similarity
Use of the FRiS-Function for Selecting Stolps of Classes
FRiS-Function as Criterion for the Choice of an Informative Subset of Attributes in Problem DX
FRiS-Function in a Taxonomy Task
Clustering (Stage FRiS-Cluster)
Construction of Classification (Stage FRiS-Class)
Choice of an Optimum Number of Taxons
FRiS-Function in Problem SX as a Criterion for Natural Classifications
FRiS-Function in the Problem SDX
Conclusion
References
Similarity Determination for Clustering Textual Documents
Introduction
Similarity Determination for a Set of Documents
Methods for Documents Clustering
Choice of the Optimal Algorithm
Looking for the Optimum Method for Specifying the Similarity in the Set of Documents
Conclusion
References
On the Problem of Prediction
Introduction
The Statistical Ambiguity Problem
Inductive-Statistical Inference
The Requirement of Maximal Specificity in Default Logic
The Solution of the Statistical Ambiguity Problem
Probabilistic Approximation of Empirical Theories
Approximation of Logical Inference by Semantic Probabilistic Inference
Laws
The Probability of Events and Sentences
The Probabilistic Laws on M
Semantic Probabilistic Inference
Probabilistic Maximally Specific Laws
The Solution of the Statistical Ambiguity Problem
Probabilistic Herbrand Models
Logical Programs
Estimations of the Probability and Conditional Probability of Requests
Inductive Synthesis of Probabilistic Logic Programs
Predictions Based on Semantic Probabilistic Inference
The Relational Data Mining and Program System 'Discovery'
References
Visual Data Mining and Discovery in Multivariate Data Using Monotone n-D Structure
Introduction
Chains and Similarity Distances between Chains
Representing and Drawing of n-D Boolean Space in 2-D
Learning Process by Monotone Extension
Computational Experiment
Conclusion and Future Work
References
Construction of an Event Tree on the Basis of Expert Knowledge and Time Series
Introduction
Event Tree and Decision Tree
Multidimensional Time Series and Decision Trees
From Decision Trees to Event Trees
Bayesian Criteria for Decision Tree Pruning
Experiments
Summary
References
Author Index

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