User Preference Web Search -- Experiments with a System Connecting Web and User

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients

UPRE: User Preference Based Search System

We present a middleware system UPRE system enabling personalized web search for users with different preferences. The input for UPRE is user evaluation of some objects in scale from the worst to the best. Our model is inspired by existing models of distributed middleware search. We use both inductive and deductive tasks to find user preferences and consequently best objects. 1. Introduction an

Fuzzy RDF in the Semantic Web: Deduction and Induction

Abstract. This work integrates two diploma theses: Logic Programming on Ranked RDF Data and Fuzzy ILP on RDF Data. Both work with fuzzy logic and RDF data, the first one from inductive and the second from deductive point of view. We analyze the possibilities of using RDF for the purpose of logic programming. This includes defining rules for user ranking, transforming them to database select queries, taking the results as positive examples for ILP and finally learning the rules from data.