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Table of contents
Chapter 1 Formal Concept Analysis and Web Clustering Engines
1.1 Formal Concept Analysis
1.1.1 Many-Valued Context
1.1.2 Iceberg Concept Lattice
1.1.3 Stability Index
1.1.4 Association Rule Mining
1.2 Pattern Structures
1.3 Relational Concept Analysis
1.4 Interactivity over Web Clustering Engines
1.4.1 Exploratory Data Mining
1.4.2 Web Clustering Engines
1.4.3 Interactive Exploration over Web Search Results
1.5 Formal Concept Analysis for Web Clustering Engines
1.5.1 Clustering Web Search Results
1.5.2 Adding Background Knowledge to Concept Lattices
1.6 User Interfaces for Navigating Concept Lattices
1.6.1 Tree-Folder Display
1.6.2 Systems using Hasse Diagram
1.6.3 User Friendly Interface.
1.6.4 Querying
1.6.5 Categorization of Lattice Visualisation Tools
1.7 Discussion
Chapter 2 Semantic Web
2.1 From Web of Documents to Semantic Web
2.1.1 Schema.org
2.1.2 Linked Data
2.2 Principles of Linked Data
2.2.1 Resource Description Framework (RDF)
2.2.2 SPARQL
2.2.3 Accessing and Consuming Linked Data
2.3 Clustering SPARQL Query Answers.
2.4 RDF Graphs and Formal Concept Analysis
2.5 Discussion
Chapter 3 Lattice-Based View Access
3.1 Introduction
3.1.1 Motivation
3.2 Lattice-Based View Access
3.2.1 SPARQL Queries with Classication Capabilities
3.2.2 Designing a Formal Context of Answer Tuples
3.2.3 Building a Concept Lattice
3.2.4 Interpretation Operations over Lattice-Based Views
3.3 Tool for Interaction with SPARQL Query Answers
3.3.1 Motivating Example
3.4 The RV-Xplorer
3.4.1 Local View
3.4.2 Spy
3.4.3 Statistics about the next level
3.5 Navigation Operations
3.5.1 Guided Downward (Drill down)/ Upward Navigation (Roll-up):
3.5.2 Direct Navigation
3.5.3 Navigating Across Point-of-Views
3.5.4 Altering Navigation Space
3.5.5 Area Expansion
3.5.6 Hiding Non-Interesting Parts of the View
3.5.7 Other Functionalities
3.6 Experimentation
3.6.1 YAGO
3.6.2 DBpedia
3.6.3 Evaluation
3.6.4 Application to Biomedical Data
3.7 Discussion
Chapter 4 Mining denitions from RDF annotations using Formal Concept Analysis
4.1 Introduction
4.2 Improving DBpedia with FCA
4.2.1 Problem context
4.2.2 The completion of DBpedia data
4.2.3 Pattern structures for the completion process
4.2.4 Heterogeneous pattern structures
4.3 Experimentation
4.3.1 Evaluation
4.4 Discussion
Chapter 5 Revisiting Pattern Structures for Structured Attribute Sets
5.1 Introduction
5.2 Pattern Structures for Structured Attributes
5.2.1 Two original propositions on structured attribute sets
5.2.2 From Structured Attributes to Tree-shaped Attributes
5.2.3 On Computing Intersection of Antichains in a Tree
5.3 Experiments and Discussion
5.4 Discussion
Chapter 6 Exploratory Data Analysis of RDF Resources using Formal Concept Anal- ysis
6.1 Introduction
6.1.1 Motivating Example
6.2 Towards RDF-Pattern Structures
6.2.1 From RDF Triples to RDF-Pattern Structures
6.2.2 Similarity Operation Over Classes
6.2.3 Building the RDF Pattern Concept Lattice
6.3 Navigation and Interactive Exploration over Pattern Concept Lattice
6.3.1 Navigation Operations
6.3.2 Interactive Data Exploration over Navigation Space
6.4 Experimentation
6.4.1 Drug Search
6.4.2 DBLP
6.4.3 Visualization
6.5 Related Work
6.6 Discussion
Chapter 7 Conclusion and Perspectives
7.1 Summary of Contributions
7.1.1 Lattice-Based View Access (LBVA)
7.1.2 Mining denitions from RDF annotations using Formal Concept Analysis
7.1.3 Pattern Structures for Structured Attribute Sets
7.2 Perspectives
7.3 List of Publications
Appendix
Appendix A
A Study on the Correspondence between FCA and ELI Ontologies
A.1 Introduction
A.2 Preliminaries
A.3 Transforming ELI Ontologies into Formal Contexts
A.3.1 Motivation
A.3.2 Proposal
A.4 Querying Concept Lattice
A.5 SPARQL query answering over ontologies vs LQL query answering over concept lattices
A.6 Related work
A.7 Conclusion
Appendix B Enriching Transcriptomic Data with Linked Open Data
B.1 Introduction
B.2 Enriching Transcriptomic Data with Hierarchical Information from Linked Data120
B.3 Complex Biological Data Integration
B.3.1 Molecular Signature Database (MSigDB)
B.3.2 Domain Knowledge
B.4 From Data to Knowledge
B.4.1 Test Data Sets
B.4.2 Using FCA for Analyzing Genes
B.5 Results
B.6 Conclusion
