Bottom-Up Induction of Feature Terms
Machine Learning
Phase Transitions in Relational Learning
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
KIDS: An Iterative Algorithm to Organize Relational Knowledge
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Abstractions for Knowledge Organization of Relational Descriptions
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Automatic Construction and Refinement of a Class Hierarchy over Multi-valued Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Making Decision Trees More Accurate by Losing Information
FoIKS '00 Proceedings of the First International Symposium on Foundations of Information and Knowledge Systems
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Computing Probabilistic Least Common Subsumers in Description Logics
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Building Classes in Object-Based Languages by Automatic Clustering
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
A Refinement Operator for Description Logics
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Data mining tasks and methods: Clustering: conceptual clustering
Handbook of data mining and knowledge discovery
Relational learning as search in a critical region
The Journal of Machine Learning Research
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
A database clustering methodology and tool
Information Sciences—Informatics and Computer Science: An International Journal
Learning Object Models from Semistructured Web Documents
IEEE Transactions on Knowledge and Data Engineering
Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A Hierarchical Clustering Procedure for Semantically Annotated Resources
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
DL-FOIL Concept Learning in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Learning with Kernels in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Fuzzy Clustering for Categorical Spaces
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Learnability of description logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
A hierarchical clustering method for semantic knowledge bases
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Part-whole reasoning in an object-centered framework
Part-whole reasoning in an object-centered framework
Conceptual clustering applied to ontologies: a distance-based evolutionary approach
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Conceptual clustering and its application to concept drift and novelty detection
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Foundations of refinement operators for description logics
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Fuzzy Clustering for Semantic Knowledge Bases
Fundamenta Informaticae - Methodologies for Intelligent Systems
Supporting small teams in cooperatively building application domain models
Expert Systems with Applications: An International Journal
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
Fr-ONT: an algorithm for frequent concept mining with formal ontologies
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
A counterfactual-based learning algorithm for ALC description logic
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
A pattern-based approach to conceptual clustering in FOL
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
ON SETS OF TERMS: A STUDY OF A GENERALISATION RELATION AND OF ITS ALGORITHMIC PROPERTIES
Fundamenta Informaticae
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information Systems
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The representation formalism as well as the representation language is of great importance for the success of machine learning. The representation formalism should be expressive, efficient, useful, and applicable. First-order logic needs to be restricted in order to be efficient for inductive and deductive reasoning. In the field of knowledge representation, term subsumption formalisms have been developed which are efficient and expressive. In this article, a learning algorithm, KLUSTER, is described that represents concept definitions in this formalism. KLUSTER enhances the representation language if this is necessary for the discrimination of concepts. Hence, KLUSTER is a constructive induction program. KLUSTER builds the most specific generalization and a most general discrimination in polynomial time. It embeds these concept learning problems into the overall task of learning a hierarchy of concepts.