Invited Talks -- Usability Issues in Description Logic Knowledge Base Completion -- Concept Lattice Orbifolds – First Steps -- The Advent of Formal Diagrammatic Reasoning Systems -- The Logic of Learning -- What Can Formal Concept Analysis Do for Data Warehouses? -- Time and Logic: A.N. Prior’s Formal Analysis of Temporal Concepts -- Can Ontology Inform Ontologies? -- Theory -- Factor Analysis of Incidence Data via Novel Decomposition of Matrices -- A Unified Hierarchy for Functional Dependencies, Conditional Functional Dependencies and Association Rules -- Robust Elements in Rough Set Abstractions -- Some Computational Problems Related to Pseudo-intents -- Algorithms -- Exploring Finite Models in the Description Logic -- Yet a Faster Algorithm for Building the Hasse Diagram of a Concept Lattice -- Context Graphs — Representing Formal Concepts by Connected Subgraphs -- Handling Large Formal Context Using BDD – Perspectives and Limitations -- Applications -- A Novel Approach to Cell Formation -- Identifying Ecological Traits: A Concrete FCA-Based Approach -- A Concept Lattice-Based Kernel for SVM Text Classification -- Two FCA-Based Methods for Mining Gene Expression Data -- Ontology-Based Formal Concept Differences Analysis in Radiology Report Impact by the Adoption of PACS -- Revisiting the Potentialities of a Mechanical Thesaurus -- FCA-Based Two Dimensional Pattern Matching -- History -- RESTRUCTURING LATTICE THEORY: AN APPROACH BASED ON HIERARCHIES OF CONCEPTS.

This book constitutes the refereed proceedings of the 7th International Conference on Formal Concept Analysis, ICFCA 2009, held in Darmstadt, Germany, in May 2009. The 15 revised full papers presented were carefully reviewed and selected from 29 submissions for inclusion in the book. The papers comprise state of the art research and present new results in Formal Concept Analysis and related fields. These results range from theoretical novelties to advances in FCA-related algorithmic issues, as well as application domains of FCA such as data visualization, information retrieval, machine learning, data analysis and knowledge management.