| Description |
1 online resource |
| Series |
Studies on the semantic web ; vol. 034 |
|
Studies on the Semantic Web ; vol. 034.
|
| Contents |
Intro; Title Page; Contents; Introduction and State of the Art; Introduction; Problem Statement; Research Hypothesis; Research Questions; RQ1: Definition of an Explanation; RQ2: Detection of the Background Knowledge; RQ3: Generation of the Explanations; RQ4: Evaluation of the Explanations; Research Methodology; Approach and Contributions; Applicability; Dedalo at a Glance; Contributions of the Thesis; Structure of the Thesis; Structure; Publications; Datasets and Use-cases; State of the Art; A Cognitive Science Perspective on Explanations; Characterisations of Explanations |
|
The Explanation OntologyResearch Context; The Knowledge Discovery Process; Graph Terminology and Fundamentals; Historical Overview of the Web of Data; Consuming Knowledge from the Web of Data; Resources; Methods; Towards Knowledge Discovery from the Web of Data; Managing Graphs; Mining Graphs; Mining the Web of Data; Summary and Discussion; Looking for Pattern Explanations in the Web of Data; Manually generating Explanations; Introduction; The Inductive Logic Programming Framework; General Setting; Generic Technique; A Practical Example; The ILP Approach to Generate Explanations; Experiments |
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Building the Training ExamplesBuilding the Background Knowledge; Inducing Hypotheses; Discussion; Conclusions and Limitations; Automatically generating Explanations; Introduction; Problem Formalisation; Assumptions; Formal Definitions; An Example; Automatic Discovery of Explanations; Challenges and Proposed Solutions; Description of the Process; Evaluation Measures; Final Algorithm; Experiments; Use-cases; Heuristics Comparison; Best Explanations; Time Evaluation; Conclusions and Limitations; Aggregating Explanations using Neural Networks; Introduction; Motivation and Challenges |
|
Improving Atomic RulesRule Interestingness Measures; Neural Networks to Predict Combinations; Proposed Approach; A Neural Network Model to Predict Aggregations; Integrating the Model in Dedalo; Experiments; Comparing Strategies for Rule Aggregation; Results and Discussion; Conclusions and Limitations; Contextualising Explanations with the Web of Data; Introduction; Problem Statement; Learning Path Evaluation Functions through Genetic Programming; Genetic Programming Foundations; Preparatory Steps; Step-by-Step Run; Experiments; Experimental Setting; Results; Conclusion and Limitations |
|
Evaluation and ConclusionEvaluating Dedalo with Google Trends; Introduction; First Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; Participant Details; User Agreement; Results, Discussion and Error Analysis; Second Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; User Agreement; Results, Discussion and Error Analysis; Final Discussion and Conclusions; Discussion and Conclusions; Introduction; Summary, Answers and Contributions; Definition of an Explanation; Detection of the Background Knowledge; Generation of the Explanations |
| Bibliography |
Includes bibliographical references |
| Notes |
Online record; title from digital title page (viewed on October 29, 2018) |
| Subject |
Data mining.
|
|
Data Mining
|
|
COMPUTERS -- General.
|
|
Data mining
|
| Form |
Electronic book
|
| Author |
IOS Press.
|
| ISBN |
9781614998600 |
|
1614998604 |
|
