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Table of Contents
Intro
Foreword
Preface
Contents
1 Introduction to Representation Learning
1.1 Motivation
1.2 Representation Learning in Knowledge Discovery
1.2.1 Machine Learning and Knowledge Discovery
1.2.2 Automated Data Transformation
1.3 Data Transformations and Information Representation Levels
1.3.1 Information Representation Levels
1.3.2 Propositionalization: Learning Symbolic Vector Representations
1.3.3 Embeddings: Learning Numeric Vector Representations
1.4 Evaluation of Propositionalization and Embeddings
1.4.1 Performance Evaluation
1.4.2 Interpretability
1.5 Survey of Automated Data Transformation Methods
1.6 Outline of This Monograph
References
2 Machine Learning Background
2.1 Machine Learning
2.1.1 Attributes and Features
2.1.2 Machine Learning Approaches
2.1.3 Decision and Regression Tree Learning
2.1.4 Rule Learning
2.1.5 Kernel Methods
2.1.6 Ensemble Methods
2.1.7 Deep Neural Networks
2.2 Text Mining
2.3 Relational Learning
2.4 Network Analysis
2.4.1 Selected Homogeneous Network Analysis Tasks
2.4.2 Selected Heterogeneous Network Analysis Tasks
2.4.3 Semantic Data Mining
2.4.4 Network Representation Learning
2.5 Evaluation
2.5.1 Classifier Evaluation Measures
2.5.2 Rule Evaluation Measures
2.6 Data Mining and Selected Data Mining Platforms
2.6.1 Data Mining
2.6.2 Selected Data Mining Platforms
2.7 Implementation and Reuse
References
3 Text Embeddings
3.1 Background Technologies
3.1.1 Transfer Learning
3.1.2 Language Models
3.2 Word Cooccurrence-Based Embeddings
3.2.1 Sparse Word Cooccurrence-Based Embeddings
3.2.2 Weighting Schemes
3.2.3 Similarity Measures
3.2.4 Sparse Matrix Representations of Texts
3.2.5 Dense Term-Matrix Based Word Embeddings
3.2.6 Dense Topic-Based Embeddings
3.3 Neural Word Embeddings
3.3.1 Word2vec Embeddings
3.3.2 GloVe Embeddings
3.3.3 Contextual Word Embeddings
3.4 Sentence and Document Embeddings
3.5 Cross-Lingual Embeddings
3.6 Intrinsic Evaluation of Text Embeddings
3.7 Implementation and Reuse
3.7.1 LSA and LDA
3.7.2 word2vec
3.7.3 BERT
References
4 Propositionalization of Relational Data
4.1 Relational Learning
4.2 Relational Data Representation
4.2.1 Illustrative Example
4.2.2 Example Using a Logical Representation
4.2.3 Example Using a Relational Database Representation
4.3 Propositionalization
4.3.1 Relational Features
4.3.2 Automated Construction of Relational Features by RSD
4.3.3 Automated Data Transformation and Learning
4.4 Selected Propositionalization Approaches
4.5 Wordification: Unfolding Relational Data into BoW Vectors
4.5.1 Outline of the Wordification Approach
4.5.2 Wordification Algorithm
4.5.3 Improved Efficiency of Wordification Algorithm
4.6 Deep Relational Machines
Foreword
Preface
Contents
1 Introduction to Representation Learning
1.1 Motivation
1.2 Representation Learning in Knowledge Discovery
1.2.1 Machine Learning and Knowledge Discovery
1.2.2 Automated Data Transformation
1.3 Data Transformations and Information Representation Levels
1.3.1 Information Representation Levels
1.3.2 Propositionalization: Learning Symbolic Vector Representations
1.3.3 Embeddings: Learning Numeric Vector Representations
1.4 Evaluation of Propositionalization and Embeddings
1.4.1 Performance Evaluation
1.4.2 Interpretability
1.5 Survey of Automated Data Transformation Methods
1.6 Outline of This Monograph
References
2 Machine Learning Background
2.1 Machine Learning
2.1.1 Attributes and Features
2.1.2 Machine Learning Approaches
2.1.3 Decision and Regression Tree Learning
2.1.4 Rule Learning
2.1.5 Kernel Methods
2.1.6 Ensemble Methods
2.1.7 Deep Neural Networks
2.2 Text Mining
2.3 Relational Learning
2.4 Network Analysis
2.4.1 Selected Homogeneous Network Analysis Tasks
2.4.2 Selected Heterogeneous Network Analysis Tasks
2.4.3 Semantic Data Mining
2.4.4 Network Representation Learning
2.5 Evaluation
2.5.1 Classifier Evaluation Measures
2.5.2 Rule Evaluation Measures
2.6 Data Mining and Selected Data Mining Platforms
2.6.1 Data Mining
2.6.2 Selected Data Mining Platforms
2.7 Implementation and Reuse
References
3 Text Embeddings
3.1 Background Technologies
3.1.1 Transfer Learning
3.1.2 Language Models
3.2 Word Cooccurrence-Based Embeddings
3.2.1 Sparse Word Cooccurrence-Based Embeddings
3.2.2 Weighting Schemes
3.2.3 Similarity Measures
3.2.4 Sparse Matrix Representations of Texts
3.2.5 Dense Term-Matrix Based Word Embeddings
3.2.6 Dense Topic-Based Embeddings
3.3 Neural Word Embeddings
3.3.1 Word2vec Embeddings
3.3.2 GloVe Embeddings
3.3.3 Contextual Word Embeddings
3.4 Sentence and Document Embeddings
3.5 Cross-Lingual Embeddings
3.6 Intrinsic Evaluation of Text Embeddings
3.7 Implementation and Reuse
3.7.1 LSA and LDA
3.7.2 word2vec
3.7.3 BERT
References
4 Propositionalization of Relational Data
4.1 Relational Learning
4.2 Relational Data Representation
4.2.1 Illustrative Example
4.2.2 Example Using a Logical Representation
4.2.3 Example Using a Relational Database Representation
4.3 Propositionalization
4.3.1 Relational Features
4.3.2 Automated Construction of Relational Features by RSD
4.3.3 Automated Data Transformation and Learning
4.4 Selected Propositionalization Approaches
4.5 Wordification: Unfolding Relational Data into BoW Vectors
4.5.1 Outline of the Wordification Approach
4.5.2 Wordification Algorithm
4.5.3 Improved Efficiency of Wordification Algorithm
4.6 Deep Relational Machines