Models of incremental concept formation
Artificial Intelligence
KBG: a knowledge based generalizer
Proceedings of the seventh international conference (1990) on Machine learning
Graph clustering and model learning by data compression
Proceedings of the seventh international conference (1990) on Machine learning
Four stances on knowledge acquisition and machine learning
EWSL-91 Proceedings of the European working session on learning on Machine learning
Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Learning Logical Definitions from Relations
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Flexible matching for noisy structural descriptions
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Semantic model for induction of first order theories
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Semantic distance measure between ontology concept's attributes
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Guiding the search in the NO region of the phase transition problem with a partial subsumption test
ECML'06 Proceedings of the 17th European conference on Machine Learning
Extending HCONE-Merge by approximating the intended meaning of ontology concepts iteratively
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
A vector space model for semantic similarity calculation and OWL ontology alignment
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Ontology-based concept similarity in Formal Concept Analysis
Information Sciences: an International Journal
A General Similarity Framework for Horn Clause Logic
Fundamenta Informaticae
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There are still very few systems performing a Similarity Based Learning and using a First Order Logic (FOL) representation. This limitation comes from the intrinsic complexity of the learning processes in FOL and from the difficulty to deal with numerical knowledge in this representation. In this paper, we show that major learning processes, namely generalizatiorl and clustering, can be solved in a homogeneous way by using a similarity measure. As this measure is defined, the similarity computation comes down to a problem of solving a set of equations in several unknowns. The representation language used to express our examples is a subset of FOL allowing to express both quantitative knowledge and a relevance scale on the predicates.