An Inductive Logic Programming Approach to Statistical Relational Learning 1st Edition by Kristian Kersing ISBN 1586036742 9781586036744

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ISBN 10: 1586036742
ISBN 13: 9781586036744
Author: Kristian Kersing

In this publication, the author Kristian Kersting has made an assault on one of the hardest integration problems at the heart of Artificial Intelligence research. This involves taking three disparate major areas of research and attempting a fusion among them. The three areas are: Logic Programming, Uncertainty Reasoning and Machine Learning. Every one of these is a major sub-area of research with its own associated international research conferences. Having taken on such a Herculean task, Kersting has produced a series of results which are now at the core of a newly emerging area: Probabilistic Inductive Logic Programming. The new area is closely tied to, though strictly subsumes, a new field known as ‘Statistical Relational Learning’ which has in the last few years gained major prominence in the American Artificial Intelligence research community. Within this book, the author makes several major contributions, including the introduction of a series of definitions which circumscribe the new area formed by extending Inductive Logic Programming to the case in which clauses are annotated with probability values. Also, Kersting investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.

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An Inductive Logic Programming Approach to Statistical Relational Learning 1st Edition Table of contents:

Part I: Foundations of Inductive Logic Programming

2. Basics of Logic Programming

   • Introduction to Logic Programming and Prolog
   • Syntax and Semantics of Logic Programs
   • First-Order Logic and Its Application in ILP
   • Basic Concepts: Facts, Rules, and Queries
   • Resolution and Inference in Logic Programming

3. Inductive Logic Programming (ILP)

   • The Principles of ILP
   • ILP Systems and Algorithms
   • Learning from Examples and Background Knowledge
   • Generalization and Specialization in ILP
   • ILP Applications in Machine Learning

4. Statistical Relational Learning (SRL)

   • Introduction to Statistical Relational Models
   • Relational Data and Its Challenges
   • Probabilistic Graphical Models
   • SRL Frameworks: Markov Logic Networks, Relational Bayesian Networks
   • SRL Algorithms and Inference Techniques

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Part II: Bridging ILP and SRL

5. Combining ILP with Statistical Methods

   • Overview of the Need for Combining ILP and SRL
   • Statistical Models in ILP
   • Learning Relational Patterns with Uncertainty
   • Methods for Integrating Logic and Probability
   • Examples of ILP and SRL Hybrid Approaches

6. Logic-based Approaches to SRL

   • Logic Programs with Probabilistic Rules
   • Defining Probabilistic Dependencies in Logic Programs
   • Learning from Relational Data with Probabilistic Logic
   • Algorithms for Learning Relational Probabilistic Models
   • Practical Applications of Logic-based SRL

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Part III: Algorithms and Methods

7. Inductive Learning of Relational Structures

   • Learning Relational Representations in ILP
   • Constructing Models of Complex Relational Data
   • Incremental Learning Algorithms for Relational Data
   • Evaluation and Generalization in Relational Learning
   • Case Studies and Applications in Relational Databases

8. Probabilistic Inference in Relational Domains

   • Introduction to Probabilistic Inference Techniques
   • Exact and Approximate Inference Methods
   • Inference in Relational Graphical Models
   • Techniques for Scaling Probabilistic Inference
   • Applications of Inference Methods in SRL

9. Learning with Noisy and Incomplete Data

   • Dealing with Noise in Relational Data
   • Handling Missing Data and Uncertainty
   • Robust Learning Techniques in SRL
   • Probabilistic Approaches to Missing Information
   • Case Studies on Real-World Noisy Data

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Part IV: Applications and Case Studies

10. Applications of ILP and SRL in Real-World Problems

    • Knowledge Representation and Discovery in Databases
    • Learning Models in Bioinformatics and Genomics
    • SRL Applications in Social Networks and Recommendation Systems
    • ILP and SRL in Robotics and Autonomous Systems
    • Using ILP for Pattern Discovery in Complex Domains

11. Case Studies: Combining ILP and SRL

    • Case Study 1: Learning Probabilistic Models in Healthcare
    • Case Study 2: Relational Data Mining in Social Media
    • Case Study 3: Predictive Modeling in Complex Systems
    • Case Study 4: Environmental Data Modeling Using SRL
    • Lessons Learned from Real-World ILP and SRL Applications

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Conclusion
12. Conclusion: The Future of ILP and SRL
– Challenges and Opportunities in Combining ILP and SRL
– Emerging Trends in Relational Machine Learning
– Directions for Future Research in Statistical Relational Learning
– Closing Thoughts on ILP and SRL in Modern AI Systems

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