Šiuolaikinių informacinių sistemų kūrimas darosi vis sudėtingesnis ir daugiau išteklių reikalaujantis procesas, nes kuriamoms informacinėms sistemoms keliami vis didesni reikalavimai. Šiame darbe pristatomas ontologijos kūrimo būdas formalių konceptų pagrindu. Ontologijos leidžia saugoti žinias apie dalykinę sritį. Kaip žinoma, ontologijų kūrimas yra sudėtingas procesas, reikalaujantis daug pastangų bei ekspertinių žinių. Dauguma šiuolaikinių informacinių sistemų yra pradedamos kurti iš naujo, nepasinaudojus turimomis žiniomis. Veltui gaištamas laikas, susiduriama su tomis pačiomis problemomis, daromos klaidos. Ontologijų panaudojimas leidžia pasinaudoti jau turimomis žiniomis kuriant naujas informacines sistemas. Disertacijoje siūlomas ontologijų sudarymo metodas naudojant formalius konceptus, kurie praplėsti taisyklėmis. Analitinėje darbo dalyje pristatomos ontologijos ir formalių konceptų sąvokos ir reikšmė šiuolaikinėse informacinėse sistemose. Pateikiamas egzistuojančių formalių konceptų panaudojimo būdų bei ontologijų kūrimo metodų tyrimas. Metodui įvertinti siūloma taikyti naudos vertinimo metodą. Paskutiniame disertacijos skyriuje aprašomi eksperimentai, kuriuose dalykinių sričių ontologijos yra kuriamos naudojant formalius konceptus. Šiems eksperimentams įgyvendinti buvo sukurti programiniai įrankiai, kurie praplėtė šiuolaikinius ontologijų kūrimo metodus. Pabaigoje pateikiami eksperimento rezultatai ir išvados. / Knowledge is widely used in development of modern information systems. One of the ways to represent knowledge is ontologies. They make it possible to shorten the time of information system development and to reduce costs. Moreover, it is an opportunity to re-use the knowledge. The objective of the thesis is to propose a method that allows partially simplifying and automating an ontology development process. Typically, an ontology development process consists of four phases: collection of terms, analysis of terms, specification and representation. A first stage of ontology development process is to capture the entire domain identified in terms of their mutual relations and definitions. During the analysis phase the collected terms are analysed: different terms for describing the same objects or phenomena are searched. The next step can be performed using the selected ontology development tool. This determines the display language and ontology selection. For example, enterprise specialists (low level IT specialists) can compose ontology by using ontology development tools. The above described ontology development process is rather slow and requires scrupulous work. Human involvement in every step of an ontology development process makes big influence on performance. Different people can not create identical ontologies even developing the same subject area ontology. We believe that the situation can be improved by the qualitative leap which would enable the acceleration of the... [to full text]

Draft and proof of an algorithm computing incremental changes within a labeled layouted concept lattice upon insertion or removal of an attribute column in the underlying formal context. Furthermore some implementational details and mathematical background knowledge are presented. / Entwurf und Beweis eines Algorithmus zur Berechnung inkrementeller Änderungen in einem beschrifteten dargestellten Begriffsverband beim Einfügen oder Entfernen einer Merkmalsspalte im zugrundeliegenden formalen Kontext. Weiterhin sind einige Details zur Implementation sowie zum mathematischen Hintergrundwissen dargestellt.

Draft and proof of an algorithm computing incremental changes within a labeled layouted concept lattice upon insertion or removal of an attribute column in the underlying formal context. Furthermore some implementational details and mathematical background knowledge are presented.:1 Introduction 1.1 Acknowledgements 1.2 Supporting University: TU Dresden, Institute for Algebra 1.3 Supporting Corporation: SAP AG, Research Center Dresden 1.4 Research Project: CUBIST 1.5 Task Description und Structure of the Diploma Thesis I Mathematical Details 2 Fundamentals of Formal Concept Analysis 2.1 Concepts and Concept Lattice 2.2 Visualizations of Concept Lattices 2.2.1 Transitive Closure and Transitive Reduction 2.2.2 Neighborhood Relation 2.2.3 Line Diagram 2.2.4 Concept Diagram 2.2.5 Vertical Hybridization 2.2.6 Omitting the top and bottom concept node 2.2.7 Actions on Concept Diagrams 2.2.8 Metrics on Concept Diagrams 2.2.9 Heatmaps for Concept Diagrams 2.2.10 Biplots of Concept Diagrams 2.2.11 Seeds Selection 2.3 Apposition of Contexts 3 Incremental Updates for Concept Diagrams 3.1 Insertion & Removal of a single Attribute Column 3.1.1 Updating the Concepts 3.1.2 Structural Remarks 3.1.3 Updating the Order 3.1.4 Updating the Neighborhood 3.1.5 Updating the Concept Labels 3.1.6 Updating the Reducibility 3.1.7 Updating the Arrows 3.1.8 Updating the Seed Vectors 3.1.9 Complete IFOX Algorithm 3.1.10 An Example: Stepwise Construction of FCD(3) 3.2 Setting & Deleting a single cross 4 Iterative Exploration of Concept Lattices 4.1 Iceberg Lattices 4.2 Alpha Iceberg Lattices 4.3 Partly selections 4.3.1 Example with EMAGE data 4.4 Overview on Pruning & Interaction Techniques II Implementation Details 5 Requirement Analysis 5.1 Introduction 5.2 User-Level Requirements for Graphs 5.2.1 Select 5.2.2 Explore 5.2.3 Reconfigure 5.2.4 Encode 5.2.5 Abstract/Elaborate 5.2.6 Filter 5.2.7 Connect 5.2.8 Animate 5.3 Low-Level Requirements for Graphs 5.3.1 Panel 5.3.2 Node and Edge 5.3.3 Interface 5.3.4 Algorithm 5.4 Mapping of Low-Level Requirements to User-Level Requirements 5.5 Specific Visualization Requirements for Lattices 5.5.1 Lattice Zoom/Recursive Lattices/Partly Nested Lattices 5.5.2 Planarity 5.5.3 Labels 5.5.4 Selection of Ideals, Filters and Intervalls 5.5.5 Restricted Moving of Elements 5.5.6 Layout Algorithms 5.5.7 Additional Feature: Three Dimensions and Rotation 5.5.8 Additional Feature: Nesting 6 FCAFOX Framework for Formal Concept Analysis in JAVA 6.1 Architecture A Appendix A.1 Synonym Lexicon A.2 Galois Connections & Galois Lattices A.3 Fault Tolerance Extensions to Formal Concept Analysis / Entwurf und Beweis eines Algorithmus zur Berechnung inkrementeller Änderungen in einem beschrifteten dargestellten Begriffsverband beim Einfügen oder Entfernen einer Merkmalsspalte im zugrundeliegenden formalen Kontext. Weiterhin sind einige Details zur Implementation sowie zum mathematischen Hintergrundwissen dargestellt.:1 Introduction 1.1 Acknowledgements 1.2 Supporting University: TU Dresden, Institute for Algebra 1.3 Supporting Corporation: SAP AG, Research Center Dresden 1.4 Research Project: CUBIST 1.5 Task Description und Structure of the Diploma Thesis I Mathematical Details 2 Fundamentals of Formal Concept Analysis 2.1 Concepts and Concept Lattice 2.2 Visualizations of Concept Lattices 2.2.1 Transitive Closure and Transitive Reduction 2.2.2 Neighborhood Relation 2.2.3 Line Diagram 2.2.4 Concept Diagram 2.2.5 Vertical Hybridization 2.2.6 Omitting the top and bottom concept node 2.2.7 Actions on Concept Diagrams 2.2.8 Metrics on Concept Diagrams 2.2.9 Heatmaps for Concept Diagrams 2.2.10 Biplots of Concept Diagrams 2.2.11 Seeds Selection 2.3 Apposition of Contexts 3 Incremental Updates for Concept Diagrams 3.1 Insertion & Removal of a single Attribute Column 3.1.1 Updating the Concepts 3.1.2 Structural Remarks 3.1.3 Updating the Order 3.1.4 Updating the Neighborhood 3.1.5 Updating the Concept Labels 3.1.6 Updating the Reducibility 3.1.7 Updating the Arrows 3.1.8 Updating the Seed Vectors 3.1.9 Complete IFOX Algorithm 3.1.10 An Example: Stepwise Construction of FCD(3) 3.2 Setting & Deleting a single cross 4 Iterative Exploration of Concept Lattices 4.1 Iceberg Lattices 4.2 Alpha Iceberg Lattices 4.3 Partly selections 4.3.1 Example with EMAGE data 4.4 Overview on Pruning & Interaction Techniques II Implementation Details 5 Requirement Analysis 5.1 Introduction 5.2 User-Level Requirements for Graphs 5.2.1 Select 5.2.2 Explore 5.2.3 Reconfigure 5.2.4 Encode 5.2.5 Abstract/Elaborate 5.2.6 Filter 5.2.7 Connect 5.2.8 Animate 5.3 Low-Level Requirements for Graphs 5.3.1 Panel 5.3.2 Node and Edge 5.3.3 Interface 5.3.4 Algorithm 5.4 Mapping of Low-Level Requirements to User-Level Requirements 5.5 Specific Visualization Requirements for Lattices 5.5.1 Lattice Zoom/Recursive Lattices/Partly Nested Lattices 5.5.2 Planarity 5.5.3 Labels 5.5.4 Selection of Ideals, Filters and Intervalls 5.5.5 Restricted Moving of Elements 5.5.6 Layout Algorithms 5.5.7 Additional Feature: Three Dimensions and Rotation 5.5.8 Additional Feature: Nesting 6 FCAFOX Framework for Formal Concept Analysis in JAVA 6.1 Architecture A Appendix A.1 Synonym Lexicon A.2 Galois Connections & Galois Lattices A.3 Fault Tolerance Extensions to Formal Concept Analysis