Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An introduction to inductive logic programming
Relational Data Mining
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A Data Model for Flexible Querying
ADBIS '01 Proceedings of the 5th East European Conference on Advances in Databases and Information Systems
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
AI Magazine
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Best-k queries on database systems
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Semantic Web Infrastructure Using DataPile
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Induction of Fuzzy and Annotated Logic Programs
Inductive Logic Programming
Uncertainty Issues and Algorithms in Automating Process Connecting Web and User
Uncertainty Reasoning for the Semantic Web I
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Uncertainty querying of large data can be solved by providing top-k answers according to a user fuzzy ranking/scoring function. Usually different users have different fuzzy scoring function --- a user preference model. Main goal of this paper is to assign a user a preference model automatically. To achieve this we decompose user's fuzzy ranking function to ordering of particular attributes and to a combination function. To solve the problem of automatic assignment of user model we design two algorithms, one for learning user preference on particular attribute and second for learning the combination function. Methods were integrated into a Fagin-like top-k querying system with some new heuristics and tested.