TY  - BOOK
AU  - Berthold,Michael R.
TI  - Bisociative Knowledge Discovery: An Introduction to Concept, Algorithms, Tools, and Applications
T2  - Lecture Notes in Computer Science Ser.
SN  - 9783642318306
AV  - Q334-342 
U1  - 006.3/12 
PY  - 2012///
CY  - Berlin, Heidelberg
PB  - Springer Berlin / Heidelberg
KW  - Electronic books
N1  - 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
N2  - 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
UR  - https://ebookcentral.proquest.com/lib/ostimteknik/detail.action?docID=6422610
ER  -