Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Models of incremental concept formation
Artificial Intelligence
Artificial Intelligence
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Structuring Knowledge Bases Using Automatic Learning
Proceedings of the Sixth International Conference on Data Engineering
Accounting for Domain Knowledge in the Construction of a Generalization Space
ICCS '97 Proceedings of the Fifth International Conference on Conceptual Structures: Fulfilling Peirce's Dream
Structural Machine Learning with Galois Lattice and Graphs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
An application of AI techniques to structuring objects into an optimal conceptual hierarchy
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Approaches to conceptual clustering
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Demand-Driven Construction of Structural Features in ILP
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Multistrategy Operators for Relational Learning and Their Cooperation
Fundamenta Informaticae
| Hi-index | 0.00 |
The goal of conceptual clustering is to construct a hierarchy of concepts which cluster objects based on their similarities. Knowledge organization aims at generating the set of maximally specific concepts for all possible classifications: the Generalization Space. Our research focuses on the organization of relational data represented using conceptual graphs. Unfortunately, the generalization of relational descriptions necessary to build the Generalization Space leads to a combinatorial explosion. This paper proposes to incrementally introduce the relations by using a sequence of languages that are more and more expressive. The algorithm proposed, called KIDS, is based upon an iterative reformulation of the objects descriptions. Initially represented as conceptual graphs, they are reformulated into abstract objects represented as 〈attribute, value〉 pairs. This representation allows us to use an efficient propositional knowledge organization algorithm. Experiments on Chinese character databases show the interest of using KIDS to build organizations of relational concepts.