Terminological reasoning is inherently intractable (research note)
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Reasoning and revision in hybrid representation systems
Reasoning and revision in hybrid representation systems
Induction as nonmonotonic inference
Proceedings of the first international conference on Principles of knowledge representation and reasoning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Inductive logic programming and learnability
ACM SIGART Bulletin
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
The Learnability of Description Logics with Equality Constraints
Machine Learning - Special issue on computational learning theory, COLT'92
Pac-learning non-recursive Prolog clauses
Artificial Intelligence
On the relative expressiveness of description logics and predicate logics
Artificial Intelligence
Combining Horn rules and description logics in CARIN
Artificial Intelligence
ECML '93 Proceedings of the European Conference on Machine Learning
Tableau Algorithms for Description Logics
TABLEAUX '00 Proceedings of the International Conference on Automated Reasoning with Analytic Tableaux and Related Methods
KI '98 Proceedings of the 22nd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
Introducing possibilistic logic in ILP for dealing with exceptions
Artificial Intelligence
Improving inductive logic programming by using simulated annealing
Information Sciences: an International Journal
Building rules on top of ontologies for the semantic web with inductive logic programming
Theory and Practice of Logic Programming
Inductive Logic Programming
ECML '07 Proceedings of the 18th European conference on Machine Learning
Coping with exceptions in multiclass ILP problems using possibilistic logic
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Inductive logic programming in databases: From datalog to $\mathcal{dl}+log}^{\neg\vee}$
Theory and Practice of Logic Programming
Logic programming languages for databases and the web
A 25-year perspective on logic programming
Practice of inductive reasoning on the semantic web: a system for semantic web mining
PPSWR'06 Proceedings of the 4th international conference on Principles and Practice of Semantic Web Reasoning
A counterfactual-based learning algorithm for ALC description logic
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
A methodology for building semantic web mining systems
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Mining the semantic web: a logic-based methodology
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
ILP meets knowledge engineering: a case study
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Principles of inductive reasoning on the semantic web: a framework for learning in AL-log
PPSWR'05 Proceedings of the Third international conference on Principles and Practice of Semantic Web Reasoning
A bisimulation-based method of concept learning for knowledge bases in description logics
Proceedings of the Third Symposium on Information and Communication Technology
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information Systems
AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining
International Journal on Semantic Web & Information Systems
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Carin-ALN is an interesting new rule learning bias for ILP. By allowing description logic terms as predicates of literals in datalog rules, it extends the normal bias used in ILP as it allows the use of all quantified variables in the body of a clause. It also has at-least and at-most restrictions to access the amount of indeterminism of relations. From a complexity point of view Carin-ALN allows to handle the difficult indeterminate relations efficiently by abstracting them into determinate aggregations. This paper describes a method which enables the embedding of Carin-ALN rule subsumption and learning into datalog rule subsumption and learning with numerical constraints. On the theoretical side, this allows us to transfer the learnability results known for ILP to Carin-ALN rules. On the practical side, this gives us a preprocessing method, which enables ILP systems to learn Carin-ALN rules just by transforming the data to be analyzed. We show, that this is not only a theoretical result in a first experiment: learning Carin-ALN rules from a standard ILP dataset.