Process and observation ontology PO2

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Table of contents

1 Background and State of the Art 
1.1 Probabilistic Models
1.1.1 Discrete Probability Theory
1.1.2 Bayesian Networks
1.1.3 Learning Bayesian Networks
1.1.4 Essential Graphs
1.1.5 Probabilistic Relational Models
1.1.6 Learning under constraints
1.1.7 Using Ontologies to learn Bayesian networks
1.2 Causality
1.2.1 Overview
1.2.2 Causal discovery
1.2.3 Ontologies and Explanation
1.3 Conclusion
2 Learning a Probabilistic Relational Model from a Specific Ontology 
2.1 Domain of application
2.1.1 Transformation Processes
2.1.2 Process and Observation Ontology PO2
2.2 ON2PRM Algorithm
2.2.1 Overview
2.2.2 Building the Relational Schema
2.2.3 Learning the Relational Model
2.3 Evaluation
2.3.1 Generation of synthetic data sets
2.3.2 Experiments
2.3.3 Results
2.4 Discussion
2.4.1 Determination of explaining and explained attributes
2.4.2 Defining the temporality
2.5 Conclusion
3 Interactive Building of a Relational Schema From Any Knowledge Base 
3.1 Definition of a generic relational schema
3.1.1 Explicitation of constraints
3.1.2 Structure of the Stack Model
3.2 CAROLL Algorithm
3.2.1 Expert assumption
3.2.2 Assumption’s Attributes Identification
3.2.3 Enrichment
3.2.4 Validation
3.3 Towards causal discovery
3.3.1 Validating causal arcs
3.3.2 Possible conclusions
3.3.3 Incompatibility of constraints
3.3.4 Discussion
3.4 Evaluation
3.4.1 Synthetic data set
3.4.2 Movies
3.4.3 Control parameters in cheese fabrication
3.5 Discussion
3.6 Conclusion
4 Semi-Automatic Building of a Relational Schema from a Knowledge Base
4.1 Closing the Open-World Assumption
4.1.1 General Idea
4.1.2 Defining the Transformation Rules
4.1.3 Limits and Conclusion
4.2 ACROSS Algorithm
4.2.1 Comparison between CAROLL and ACROSS
4.2.2 Initialization
4.2.3 Relational Schema’s Automatic Generation
4.2.4 User modifications
4.2.5 Learning
4.3 Evaluation
4.3.1 Domain
4.3.2 Experiments
4.3.3 Results
4.3.4 Discussion
4.4 Final Remarks
4.4.1 Limits
4.4.2 Expert feedback
4.5 Conclusion
Conclusion and Perspectives
Summary of Results
Discussion and Future Works
A User’s modifications 
A.0.1 Delete a class
A.0.2 Fuse two classes of the same type
A.0.3 Divide a class
A.0.4 Create a Mutually Explaining class
A.0.5 Remove an attribute
A.0.6 Create a relational slot
A.0.7 Remove a relational slot
A.0.8 Reverse a relational slot
A.0.9 Filter the instances used for the learning