Bisociative Knowledge Discovery : : An Introduction to Concept, Algorithms, Tools, and Applications.

• Superior document: Lecture Notes in Computer Science Series ; v.7250
• Author: Berthold, Michael R.
• Place / Publishing House: Berlin, Heidelberg : : Springer Berlin / Heidelberg,, 2012.; Ã2012.
• Year of Publication: 2012
• Edition: 1st ed.
• Language: English
• Series: Lecture Notes in Computer Science Series
• Physical Description: 1 online resource (491 pages)

The focus of this book, and the BISON project from which the contributions originate, is a network based integration of data repositories of a variety of types, and the development of new ways to analyse and explore the resulting gigantic information networks.

Table of Contents

• Title
• Foreword
• Table of Contents
• Part I: Bisociation
• Towards Bisociative Knowledge Discovery
• Motivation
• Bisociation
• Types of Bisociation
• Bridging Concepts
• Bridging Graphs
• Bridging by Structural Similarity
• Other Types of Bisociation
• Bisociation Discovery Methods
• Future Directions
• Conclusions
• References
• Towards Creative Information Exploration Based on Koestler's Concept of Bisociation
• Introduction
• Creativity
• What Is Creativity?
• Three Roads to Creativity
• Computational Creativity
• Koestler's Concept of Bisociation
• Elements of Bisociative Computational Creativity
• Towards a Formal Definition of Bisociation
• Related Work
• Discussion and Conclusion
• References
• From Information Networks to Bisociative Information Networks
• Introduction
• Different Categories of Information Network
• Properties of Information Units
• Properties of Relations
• Prominent Types of Information Networks
• Ontologies
• Semantic Networks
• Topic Maps
• Weighted Networks
• BisoNets: Bisociative Information Networks
• Summary
• Patterns of Bisociation in BisoNets
• Bridging Concept
• Bridging Graphs
• Bridging by Graph Similarity
• Conclusion
• References
• Part II: Representation and Network Creation
• Network Creation: Overview
• References
• Selecting the Links in BisoNets Generated from Document Collections
• Introduction
• Reminder: Bisociation and BisoNets
• BisoNet Generation
• Data Access and Pre-processing
• Creating Nodes
• Linking Nodes: Different Metrics
• Cosine and Tanimoto Measures
• The Bison Measure
• The Probabilistic Measure
• Benchmarks
• The Swanson Benchmark
• The Biology and Music Benchmark
• Conclusion
• References
• Bridging Concept Identification for Constructing Information Networks from Text Documents
• Introduction.
• Problem Description
• Document Acquisition and Preprocessing
• Document Acquisition
• Document Preprocessing
• Background Knowledge
• Candidate Concept Detection
• Distance Measures between Vectors
• Identifying Bridging Concept Candidates for High Quality Network Entities Extraction
• Heuristics Description
• Frequency Based Heuristics
• Tf-idf Based Heuristics
• Similarity Based Heuristics
• Outlier Based Heuristics
• Baseline Heuristics
• Heuristics Evaluation
• Evaluation Procedure
• Migraine-Magnesium Dataset
• Comparison of the Heuristics
• Network Creation
• References
• Discovery of Novel Term Associations in a Document Collection
• Introduction
• Related Work
• The tpf-idf-tpu Model of Important Term Pair Associations
• Term Pair Frequency (tpf) and Inverse Document Frequency (idf)
• Term Pair Uncorrelation (tpu)
• Experiments
• Tpf-idf-tpu vs. tf-idf
• Sentence vs. Document-Level tpf-idf-tpu Methods
• Comparison of tpf-idf-tpu and tf-idf Using Annotated Test Set
• Conclusion
• References
• Cover Similarity Based Item Set Mining
• Introduction
• Frequent Item Set Mining
• Jaccard Item Sets
• The Eclat Algorithm
• The JIM Algorithm (Jaccard Item Set Mining)
• Other Similarity Measures
• Experiments
• Conclusions
• References
• Patterns and Logic for Reasoning with Networks
• Introduction
• The Biomine and ProbLog Frameworks
• Using Graphs: Biomine
• Using Logic: ProbLog
• Summary
• Inference and Reasoning Techniques
• Deduction: Reasoning about Node Tuples
• Abduction: Reasoning about Subgraphs
• Induction: Finding Patterns
• Combining Induction and Deduction
• Modifying the Knowledge Base
• Summary
• Using Probabilistic or Algebraic Labels
• The Probabilistic Model of Biomine and ProbLog
• Probabilistic Deduction
• Probabilistic Abduction and Top-k Instantiations.
• Patterns and Probabilities
• Combining Induction and Deduction
• Modifying the Probabilistic Knowledge Base
• Beyond Probabilities
• Conclusions
• References
• Part III: Network Analysis
• Network Analysis: Overview
• References
• BiQL: A Query Language for Analyzing Information Networks
• Introduction
• Motivating Example
• Requirements
• Data Representation
• Basic Data Manipulation
• Illustrative Examples
• Related Work
• Knowledge Discovery
• Databases
• Conclusions
• References
• Review of BisoNet Abstraction Techniques
• Introduction
• Preference-Free Methods
• Relative Neighborhood Graph
• Node Centrality
• PageRank and HITS
• Birnbaum's Component Importance
• Graph Partitioning
• Hierarchical Clustering
• Edge Betweenness
• Frequent Subgraphs
• Preference-Dependent Methods
• Relevant Subgraph Extraction
• Detecting Interesting Nodes or Paths
• Personalized PageRank
• Exact Subgraph Search
• Similarity Subgraph Search
• Conclusion
• References
• Simplification of Networks by Edge Pruning
• Introduction
• Lossy Network Simplification
• Definitions
• Example Instances of the Framework
• Analysis of the Problem
• Multiplicativity of Ratio of Connectivity Kept
• A Bound on the Ratio of Connectivity Kept
• A Further Bound on the Ratio of Connectivity Kept
• Algorithms
• Naive Approach
• Brute Force Approach
• Path Simplification
• Combinational Approach
• Experiments
• Experimental Setup
• Results
• Related Work
• Conclusion
• References
• Network Compression by Node and Edge Mergers
• Introduction
• Problem Definition
• Weighted and Compressed Graphs
• Simple Weighted Graph Compression
• Generalized Weighted Graph Compression
• Optimal Superedge Weights and Mergers
• Bounds for Distances between Graphs
• A Bound on Distances between Nodes
• Related Work
• Algorithms.
• Experiments
• Experimental Setup
• Results
• Conclusions
• References
• Finding Representative Nodes in Probabilistic Graphs
• Introduction
• Related Work
• Similarities in Probabilistic Graphs
• Clustering and Representatives in Graphs
• Experiments
• Test Setting
• Results
• Conclusions
• References
• (Missing) Concept Discovery in Heterogeneous Information Networks
• Introduction
• Bisociative Information Networks
• Concept Graphs
• Preliminaries
• Detection
• Application
• Results
• Conclusion and Future work
• References
• Node Similarities from Spreading Activation
• Introduction
• Related Work
• Spreading Activation
• Linear Standard Scenario
• Node Signatures
• Node Similarities
• Activation Similarity
• Signature Similarity
• Experiments
• Schools-Wikipedia
• Conclusion
• References
• Towards Discovery of Subgraph Bisociations
• Motivation
• Networks, Domains and Bisociations
• Knowledge Modeling
• Domains
• Bisociations
• Finding and Assessing Bisociations
• Domain Extraction
• Scoring Bisociation Candidates
• Complexity and Scalability
• Preliminary Evaluation
• Related Work
• Conclusion
• References
• Part IV: Exploration
• Exploration: Overview
• Introduction
• Contributions
• Conclusions
• References
• Data Exploration for Bisociative Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods
• Introduction
• Bisociative Data Exploration
• Different Meanings of Exploration
• Definition of Bisociative Exploration
• Implications for User Interface Design
• Supporting Bisociative Data Exploration
• Tools for Data Exploration
• Evaluation of Knowledge Discovery Tools
• Evaluation Challenges
• Open Issues
• Benchmark Evaluation for Discovery Tools
• Conclusion and Future Work
• References.
• On the Integration of Graph Exploration and Data Analysis: The Creative Exploration Toolkit
• Introduction
• State of the Art in Graph Interaction and Visualization
• The Creative Exploration Toolkit
• Network and Algorithm Providers
• Communication between CET and Other Tools
• The KNIME Information Mining Platform
• Wikipedia
• Evaluation
• Study Design
• Results of the Study
• Conclusion and Future Work
• References
• Bisociative Knowledge Discovery by Literature Outlier Detection
• Introduction
• Related Work in Literature Mining
• The Upgraded RaJoLink Knowledge Discovery Process
• Outlier Detection in the RaJoLink Knowledge Discovery Process
• Application of Outlier Detection in the Autism Literature
• Conclusions
• References
• Exploring the Power of Outliers for Cross-Domain Literature Mining
• Introduction
• Related Work
• Experimental Datasets
• Detecting Outlier Documents
• Classification Noise Filters for Outlier Detection
• Experimental Evaluation
• Conclusions
• References
• Bisociative Literature Mining by Ensemble Heuristics
• Introduction
• Problem Description
• Methodology for Bridging Concept Identification and Ranking
• Base Heuristics
• Ensemble Heuristic
• Evaluation of the Methodology
• Experimental Setting
• Results in the Migraine-Magnesium Dataset
• Results in Autism-Calcineurin Dataset
• The CrossBee System
• A Typical Use Case
• Other CrossBee Functionalities
• Discussion and Further Work
• References
• Part V: Applications and Evaluation
• Applications and Evaluation: Overview
• Introduction
• Contributions
• Lessons Learned
• The BISON Software for Applications Development
• Application Potential of the BISON Methodology
• Evaluation of the BISON Methodology and the Potential for Triggering Creativity.
• The Future of Bisociative Reasoning and Cross-Context Data Mining.