Foundations of logic programming
Foundations of logic programming
NP-completeness of the set unification and matching problems
Proc. of the 8th international conference on Automated deduction
Subsumption in KL-ONE is undecidable
Proceedings of the first international conference on Principles of knowledge representation and reasoning
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The logic of typed feature structures
The logic of typed feature structures
Tractable default reasoning
Learning Boolean Functions in an Infinite Attribute Space
Machine Learning
The Learnability of Description Logics with Equality Constraints
Machine Learning - Special issue on computational learning theory, COLT'92
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Propositionalization approaches to relational data mining
Relational Data Mining
Learning Logical Definitions from Relations
Machine Learning
Feature Construction with Version Spaces for Biochemical Applications
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A semantics and complete algorithm for subsumption in the classic description logic
Journal of Artificial Intelligence Research
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Pac-learning recursive logic programs: efficient algorithms
Journal of Artificial Intelligence Research
Pac-learning recursive logic programs: negative results
Journal of Artificial Intelligence Research
Relational learning for NLP using linear threshold elements
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
P-CLASSIC: a tractable probablistic description logic
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
An inference model for semantic entailment in natural language
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Learning with kernels and logical representations
Probabilistic inductive logic programming
AutoSPARQL: let users query your knowledge base
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
A declarative kernel for concept descriptions
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
An inference model for semantic entailment in natural language
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Tractable feature generation through description logics with value and number restrictions
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
An output-polynomial time algorithm for mining frequent closed attribute trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Hi-index | 0.00 |
We present a paradigm for efficient learning and inference with relational data using propositional means. The paradigm utilizes description logics and concepts graphs in the service of learning relational models using efficient propositional learning algorithms. We introduce a Feature Description Logic (FDL) - a relational (frame based) language that supports efficient inference, along with a generation function that uses inference with descriptions in the FDL to produce features suitable for use by learning algorithms. These are used within a learning framework that is shown to learn efficiently and accurately relational representations in terms of the FDL descriptions. The paradigm was designed to support learning in domains that are relational but where the amount of data and size of representation learned are very large; we exemplify it here, for clarity, on the classical ILP tasks of learning family relations and mutagenesis. This paradigm provides a natural solution to the problem of learning and representing relational data; it extends and unifies several lines of works in KRR and Machine Learning in ways that provide hope for a coherent usage of learning and reasoning methods in large scale intelligent inference.