Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
An introduction to inductive logic programming
Relational Data Mining
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
An Induction Algorithm Based on Fuzzy Logic Programming
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Bayesian Logic Programs
AI Magazine
Learning Different User Profile Annotated Rules for Fuzzy Preference Top-k Querying
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Towards learning fuzzy DL inclusion axioms
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms
Fundamenta Informaticae - Special Issue on the Italian Conference on Computational Logic: CILC 2011
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The new direction of the research in the field of data mining is the development of methods to handle imperfection (uncertainty, vagueness, imprecision,...). The main interest in this research is focused on probability models. Besides these there is an extensive study of the phenomena of imperfection in fuzzy logic. In this paper we concentrate especially on fuzzy logic programs (FLP) and Generalized Annotated Programs (GAP). The lack of the present research in the field of fuzzy inductive logic programming (FILP) is that every approach has its own formulation of the proof-theoretic part (often dealing with linguistic hedges) and lack sound and compete formulation of semantics. Our aim in this paper is to propose a formal model of FILP and induction of GAP programs (IGAP) based on sound and complete model of FLP (without linguistic hedges) and its equivalence with GAP. We focus on learning from entailment setting in this paper. We describe our approach to IGAP and show its consistency and equivalence to FILP. Our inductive method is used for detection of user preferences in a web search application. Finally, we compare our approach to several fuzzy ILP approaches.