Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Polynomial Algorithms for Projection and Matching
Proceedings of the 7th Annual Workshop on Conceptual Structures: Theory and Implementation
Structural Machine Learning with Galois Lattice and Graphs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
A Comparison of Different Conceptual Structures Projection Algorithms
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Extensions of simple conceptual graphs: the complexity of rules and constraints
Journal of Artificial Intelligence Research
Graph-Based relational learning with a polynomial time projection algorithm
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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The projection problem (conceptual graph projection, homomorphism, injective morphism, 茂戮驴-subsumption, OI-subsumption) is crucial to the efficiency of relational learning systems. How to manage this complexity has motivated numerous studies on learning biases, restricting the size and/or the number of hypotheses explored. The approach suggested in this paper advocates a projection operator based on the classical arc consistency algorithm used in constraint satisfaction problems. This projection method has the required properties : polynomiality, local validation, parallelization, structural interpretation. Using the arc consistency projection, we found a generalization operator between labeled graphs. Such an operator gives the structure of the classification space which is a concept lattice.