Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Models of incremental concept formation
Artificial Intelligence
Logic programming and databases
Logic programming and databases
Incremental concept formation with composite objects
Proceedings of the sixth international workshop on Machine learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
ACM Computing Surveys (CSUR)
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Distances and Limits on Herbrand Interpretations
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A Generalization Model Based on OI-implication for Ideal Theory Refinement
Fundamenta Informaticae - Intelligent Systems
Similarity-Guided Clause Generalization
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
Unsupervised discretization using kernel density estimation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning in FOL with a similarity measure
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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
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First-Order Logic formulæ are a powerful representation formalism characterized by the use of relations, that cause serious computational problems due to the phenomenon of indeterminacy (various portions of one description are possibly mapped in different ways onto another description). Being able to identify the correct corresponding parts of two descriptions would help to tackle the problem: hence, the need for a framework for the comparison and similarity assessment. This could have many applications in Artificial Intelligence: guiding subsumption procedures and theory revision systems, implementing flexible matching, supporting instance-based learning and conceptual clustering. Unfortunately, few works on this subject are available in the literature. This paper focuses on Horn clauses, which are the basis for the Logic Programming paradigm, and proposes a novel similarity formula and evaluation criteria for identifying the descriptions components that are more similar and hence more likely to correspond to each other, based only on their syntactic structure. Experiments on real-world datasets prove the effectiveness of the proposal, and the efficiency of the corresponding implementation in the above tasks.