Models of incremental concept formation
Artificial Intelligence
Logic programming and databases
Logic programming and databases
Attributive concept descriptions with complements
Artificial Intelligence
{\cal A}{\cal L}-log: Integrating Datalog and Description Logics
Journal of Intelligent Information Systems
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning and Concept Formation
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Clustering Ontology-Based Metadata in the Semantic Web
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Hierarchical Conceptual Clustering in a First Order Representation
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Ontological Engineering
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Two Orthogonal Biases for Choosing the Intensions of Emerging Concepts in Ontology Refinement
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
ILP meets knowledge engineering: a case study
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
A pattern-based approach to conceptual clustering in FOL
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Proceedings of the Third international conference on Formal Concept Analysis
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Building rules on top of ontologies for the semantic web with inductive logic programming
Theory and Practice of Logic Programming
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Concept Formation is a unsupervised learning task usually decomposed into the two subtasks of clustering and characterization. This paper presents a novel approach to Concept Formation in First Order Logic (FOL) which adopts a pattern-based approach to clustering and a bias-based approach to characterization. The resulting method extends therefore the levelwise search method for Frequent Pattern Discovery. The FOL fragment chosen is $\mathcal{AL}$-log, a hybrid language that merges the description logic $\mathcal{ALC}$ and the clausal logic Datalogand turns out to be suitable for applications in the context of Ontology Refinement. Indeed the method returns a taxonomy rooted into the concept that occurs in an existing taxonomic ontology and needs to be refined in the light of new knowledge coming from an external data source. Experimental results have been obtained on an $\mathcal{ALC}$ ontology enriched with Datalogdata extracted from the on-line CIA World Fact Book.