Inductive Logic Programming

Name: Inductive Logic Programming | 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers
Brand: Springer
Price: 53.49 EUR
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25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers

Katsumi Inoue Hayato Ohwada Akihiro Yamamoto(Herausgeber*in)

Springer (Verlag)

Erschienen am 25. Juni 2016

X, 215 Seiten

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978-3-319-40566-7 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents
Relational Kernel-Based Grasping with Numerical Features
1 Introduction
2 The Robot Grasping Scenario and Grasping Primitives
3 Relational Grasping: Problem Formulation
3.1 Data Modeling
3.2 Declarative and Relational Feature Construction
3.3 The Relational Problem Definition
3.4 Graphicalization
4 Relational Kernel Features
5 Experiments
5.1 Dataset and Evaluation
5.2 Results and Discussion
6 Related Work
7 Conclusions
References
CARAF: Complex Aggregates within Random Forests
1 Introduction and Context
2 Complex Aggregates
3 Random Forests
4 CARAF: Complex Aggregates with RAndom Forests
5 Experimental Results
6 Aggregation Processes Selection with Random Forests
7 Conclusion and Future Work
References
Distributed Parameter Learning for Probabilistic Ontologies
1 Introduction
2 Description Logics
3 Semantics and Reasoning in Probabilistic DLs
4 Parameter Learning for Probabilistic DLs
5 Distributed Parameter Learning for Probabilistic DLs
5.1 Architecture
5.2 MapReduce View
5.3 Scheduling Techniques
5.4 Overall EDGEMR
6 Experiments
7 Related Work
8 Conclusions
References
Meta-Interpretive Learning of Data Transformation Programs
1 Introduction
2 Related Work
3 Framework
4 Implementation
4.1 Transformation Language
5 Experiments
5.1 XML Data Transformations
5.2 Ecological Scholarly Papers
5.3 Patient Medical Records
6 Conclusion and Further Work
A Appendix 1
B Appendix 2
References
Statistical Relational Learning with Soft Quantifiers
1 Introduction
2 PSLQ: PSL with Soft Quantifiers
3 Inference and Weight Learning in PSLQ
3.1 Inference
3.2 Weight Learning
4 Evaluation: Trust Link Prediction
5 Conclusion
References
Ontology Learning from Interpretations in Lightweight Description Logics
1 Introduction
2 Description Logic Preliminaries
3 Learning Model
4 Finite Learning Sets
5 Learning Algorithms
6 Related Work
7 Conclusions and Outlook
References
Constructing Markov Logic Networks from First-Order Default Rules
1 Introduction
2 Background
2.1 Markov Logic Networks
2.2 Reasoning About Default Rules in System P
3 Encoding Ground Default Theories in Markov Logic
4 Encoding Non-ground Default Theories in Markov Logic
5 Evaluation
6 Conclusion
A Proofs
References
Mine 'Em All: A Note on Mining All Graphs
1 Introduction
2 Preliminaries
3 Graph Mining Problems
4 Mining All (Induced) Subgraphs
4.1 Negative Results
4.2 Positive Results for ALLF I and ALLS L
4.3 Positive Results for ALLL S
4.4 Other Negative Results
5 Mining Under Homeomorphism and Minor Embedding
6 Conclusions and Future Work
References
Processing Markov Logic Networks with GPUs: Accelerating Network Grounding
1 Introduction
2 Markov Logic, Tuffy, Datalog and GPUs
2.1 Inference in Markov Logic
2.2 Optimizations
2.3 Learning
2.4 Tuffy
2.5 Evaluation of Datalog Programs
2.6 GPU Architecture and Programming
3 Our GPU-Based Markov Logic Platform
4 Experimental Evaluation
4.1 Applications and Hardware-Software Platform
4.2 Results
5 Related Work
6 Conclusions
References
Using ILP to Identify Pathway Activation Patterns in Systems Biology
1 Introduction and Background
2 Overview of Propositionalization
3 Methods
3.1 Raw Data
3.2 Data Processing
3.3 Searching for Pathway Activation Patterns
4 Results
4.1 Quantitative Evaluation and Comparison with SBV Improver Model
4.2 Results for Warmr Method
4.3 Results for Warmr/TreeLiker Combined Method
5 Conclusions
References
kProbLog: An Algebraic Prolog for Kernel Programming
1 Introduction
2 KProbLogS
3 kProbLog
3.1 Recursive kProbLog Program with Meta-Functions
3.2 The Jacobi Method
3.3 kProbLog TP-Operator with Meta-Functions
4 kProbLogS[x]
4.1 Polynomials for Feature Extraction
4.2 The @id Meta-Function
5 Graph Kernels
5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels
5.2 Graph Invariant Kernels
6 Conclusions
References
An Exercise in Declarative Modeling for Relational Query Mining
1 Introduction
2 Problem Statement
3 Encoding
4 First Order Model
5 Experiments
6 Model Discussion and Generalization
7 Related Work
8 Conclusions
A Appendix: Introduction to IDP
References
Learning Inference by Induction
1 Introduction
2 Learning Logical Inference
2.1 Learning Logics
2.2 Learning from 1-Step Transitions
2.3 Learning Deduction Rules by LF1T
3 Learning Non-logical Inference Rules
3.1 Abduction
3.2 Frame Axiom
3.3 Conversational Implicature
4 Discussion
5 Conclusion
References
Identification of Transition Models of Biological Systems in the Presence of Transition Noise
1 Introduction
2 Transition Identification Under Transition Noise
3 Empirical Evaluation
3.1 Problems
3.2 Data
3.3 Models
3.4 Algorithms and Machines
3.5 Method
3.6 Results
3.7 Transition Identification Worked Example: Water
4 Related Work
5 Conclusion
References
Author Index

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