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|a Tiddi, Ilaria,
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|a Explaining data patterns using knowledge from the web of data /
|c Ilaria Tiddi.
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| 264 |
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|a Amsterdam, Netherlands :
|b IOS Press,
|c [2018]
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|a 1 online resource
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|a Studies on the semantic web ;
|v vol. 034
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| 504 |
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|a Includes bibliographical references.
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0 |
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|a Online record; title from digital title page (viewed on October 29, 2018).
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|a 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.
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| 505 |
8 |
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|a 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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|a 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.
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|a 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.
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| 505 |
8 |
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|a 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.
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|i Print version:
|a Ilaria, Tiddi.
|t Explaining data patterns using knowledge from the web of data.
|d Amsterdam, Netherlands : IOS Press, [2018]
|z 9781614998594
|w (OCoLC)1045491790
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