𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Learning Description Logic Knowledge Bases from Data Using Methods from Formal Concept Analysis [PhD Thesis]

✍ Scribed by Felix Distel


Publisher
Technische UniversitΓ€t Dresden
Year
2011
Tongue
English
Leaves
224
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Table of Contents


1 Introduction 1
1.1 Description Logics . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Describing Knowledge and Standard Reasoning . . 3
1.1.2 Reasoning Services to Support Ontology Design and Maintenance . . . . . . . . . . . . . . . . . . . 5
1.2 Formal Concept Analysis . . . . . . . . . . . . . . . . . . 7
1.2.1 Foundations . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Implications and Attribute Exploration . . . . . . 8
1.3 Existing Exploration Formalisms . . . . . . . . . . . . . . 10
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Description Logics 19
2.1 Concept Descriptions in EL . . . . . . . . . . . . . . . . . 19
2.2 Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 Reasoning in DL . . . . . . . . . . . . . . . . . . . . . . . 26
2.4 EL and its Offspring . . . . . . . . . . . . . . . . . . . . . 29
2.4.1 Extending EL by Terminological Cycles . . . . . . 30
2.4.2 Reasoning in ELgfp . . . . . . . . . . . . . . . . . . 34
3 Formal Concept Analysis 41
3.1 Formal Contexts and Formal Concepts . . . . . . . . . . . 41
3.2 Closure Operators and the Next-Closure Algorithm . . . . 44
3.3 Implications and the Duquenne-Guigues Base . . . . . . . 47
3.4 Attribute Exploration . . . . . . . . . . . . . . . . . . . . 52
4 General Frameworks for Combining FCA and DL 59
4.1 Model-Based Most Specific Concepts . . . . . . . . . . . . 60
4.1.1 General Definition . . . . . . . . . . . . . . . . . . 60
4.1.2 Existence in the EL-family . . . . . . . . . . . . . 63
4.1.3 Classical FCA from a DL Perspective . . . . . . . 69
4.2 Induced Contexts . . . . . . . . . . . . . . . . . . . . . . . 70
5 Axiomatization of Finite Models 77
5.1 Existence of Finite Bases in EL?gfp . . . . . . . . . . . . . 77
5.1.1 A Base in EL?gfp with Only Acyclic Left-Hand Sides 79
5.1.2 Finite Bases . . . . . . . . . . . . . . . . . . . . . . 89
5.2 Reducing the Size of the Base . . . . . . . . . . . . . . . . 97
5.2.1 Removal of Redundancy Using Induced Contexts . 97
5.2.2 Minimal Cardinality . . . . . . . . . . . . . . . . . 102
5.3 Obtaining an EL?-Base from an EL?gfp-Base . . . . . . . . 109
6 Exploration of EL?gfp-Models 113
6.1 A Practical Algorithm for Computing Bases . . . . . . . . 114
6.1.1 A Next-Closure-Algorithm for Growing Sets of Attributes . . . . . . . . . . . . . . . . . . . . . . . . 114
6.1.2 Computing Mi on the Fly . . . . . . . . . . . . . . 121
6.1.3 Acyclic Left-Hand Sides . . . . . . . . . . . . . . . 125
6.2 Model Exploration . . . . . . . . . . . . . . . . . . . . . . 130
7 ABox Exploration 139
7.1 Counterexamples in an EL?-Ontology . . . . . . . . . . . 140
7.1.1 Explicit Counterexamples and Extended Signatures140
7.1.2 Completely Describing the Background Model . . 142
7.1.3 Counterexamples Need not be Explicit . . . . . . . 145
7.2 Minimal Possible Consequences and Their Approximations147
7.2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . 147
7.2.2 Existence . . . . . . . . . . . . . . . . . . . . . . . 151
7.2.3 Approximation of Minimal Possible Consequences 164
7.3 ABox Exploration . . . . . . . . . . . . . . . . . . . . . . 170
7.3.1 Exploration Using Minimal Possible Consequences 170
7.3.2 Exploration Using Approximations . . . . . . . . . 177
8 Related Work 181
8.1 Bridging the Gap between FCA and Logics . . . . . . . . 181
8.1.1 Logical Scaling and Terminological Attribute Logic 182
8.1.2 Logic Information Systems . . . . . . . . . . . . . 186
8.2 Exploration Formalisms in DL . . . . . . . . . . . . . . . 188
8.2.1 FCA-based Ontology Completion and OntoComp . 188
8.2.2 Relational Exploration . . . . . . . . . . . . . . . . 191
8.3 EL and Fixpoint Semantics . . . . . . . . . . . . . . . . . 194
8.3.1 EL with Hybrid TBoxes . . . . . . . . . . . . . . . 194
8.3.2 EL and EL+ . . . . . . . . . . . . . . . . . . . . 195
9 Conclusions 197
Bibliography 214


πŸ“œ SIMILAR VOLUMES


Learning from Data: Concepts, Theory, an
✍ Vladimir S. Cherkassky, Filip Mulier πŸ“‚ Library πŸ“… 2007 πŸ› Wiley-IEEE 🌐 English

An interdisciplinary framework for learning methodologiesβ€”covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods f

From Formal to Non-Formal : Education, L
✍ Igor Ε½. Ε½agar; Polona Kelava πŸ“‚ Library πŸ“… 2014 πŸ› Cambridge Scholars Publishing 🌐 English

The monograph From Formal to Non-Formal: Education, Learning and Knowledge presents a review of selected aspects of non-formal education and learning, and is written by António Fragoso, Petra Javrh, Polona Kelava, Taja Kramberger, Nives Ličen, Marko Radovan, Drago B. Rotar, Klara Skubic Ermenc, Tade

Utility Based Learning from Data
πŸ“‚ Library πŸ“… 2010 πŸ› CRC Press 🌐 English

"Utility-Based Learning from Data is an excellent treatment of data-driven statistics for decision-making. Friedman and Sandow lucidly describe the connections between different branches of statistics and econometrics, such as utility theory, maximum entropy, and Bayesian analysis. A must-read for s