A Fuzzy-Ontology Based Information Retrieval System for Relevant Feedback

International audienceObtaining correct and relevant information at the right time to user's query is quite a difficult task. This becomes even complex, if the query terms have many meanings and occur in different varieties of domain. This paper presents a fuzzy-ontology based information retrieval system that determine the semantic equivalence between terms in a query and terms in a document by relating the synonyms of query terms with those of document terms. Hence, documents could be retrieved based on the meaning of query terms. The challenge has been that surface form does not sufficiently retrieve relevant document to user's query. However, the results presented showed that the Fuzzy-Ontology Information Retrieval system successfully retrieve relevant documents to user's query. This is irrespective of different meaning and varieties of domain. The System was tested on words with different meanings and some set of user's query from varied domains

A fuzzy semantic information retrieval system for transactional applications

In this paper, we present an information retrieval system based on the concept of fuzzy logic to relate vague and uncertain objects with un-sharp boundaries. The simple but comprehensive user interface of the system permits the entering of uncertain specifications in query forms. The system was modelled and simulated in a Matlab environment; its implementation was carried out using Borland C++ Builder. The result of the performance measure of the system using precision and recall rates is encouraging. Similarly, the smaller amount of more precise information retrieved by the system will positively impact the response time perceived by the users

Valuation of real estate investments through Fuzzy Logic

This paper aims to outline the application of Fuzzy Logic in real estate investment. In literature, there is a wide theoretical background on real estate investment decisions, but there has been a lack of empirical support in this regard. For this reason, the paper would fill the gap between theory and practice. The fuzzy logic system is adopted to evaluate the situations of a real estate market with imprecise and vague information. To highlight the applicability of the Possibility Theory, we proceeded to reconsider an example of property investment evaluation through fuzzy logic. The case study concerns the purchase of an office building. The results obtained with Fuzzy Logic have been also compared with those arising from a deterministic approach through the use of crisp numbers

Personalized Web Content with Fuzzy System

In this paper, we will develop a new computational intelligence methodology to automatically analyze and summarize web content during a user surfing sessions. The output of this process is meaningful keywords or phrases which will be used to bring the user other contents such as images that closely relate to the web pages that he or she is currently surfing

(VANET IR-CAS): Utilizing IR Techniques in Building Context Aware Systems for VANET

Most of the available context aware dissemination systems for the Vehicular Ad hoc Network (VANET) are centralized systems with low level of user privacy and preciseness. In addition, the absence of common assessment models deprives researchers from having fair evaluation of their proposed systems and unbiased comparison with other systems. Due to the importance of the commercial, safety and convenience services, three IR-CAS systems are developed to improve three applications of these services: the safety Automatic Crash Notification (ACN), the convenience Congested Road Notification (CRN) and the commercial Service Announcement (SA). The proposed systems are context aware systems that utilize the information retrieval (IR) techniques in the context aware information dissemination. The dispatched information is improved by deploying the vector space model for estimating the relevance or severity by calculating the Manhattan distance between the current situation context and the severest context vectors. The IR-CAS systems outperform current systems that use machine learning, fuzzy logic and binary models in decentralization, effectiveness by binary and non-binary measures, exploitation of vehicle processing power, dissemination of informative notifications with certainty degrees and partial rather than binary or graded notifications that are insensitive to differences in severity within grades, and protection of privacy which achieves user satisfaction. In addition, the visual-manual and speech-visual dual-mode user interface is designed to improve user safety by minimizing distraction. An evaluation model containing ACN and CRN test collections, with around 500,000 North American test cases each, is created to enable fair effectiveness comparisons among VANET context aware systems. Hence, the novelty of VANET IR-CAS systems is: First, providing scalable abstract context model with IR based processing that raises the notification relevance and precision. Second, increasing decentralization, user privacy, and safety with the least distracting user interface. Third, designing unbiased performance evaluation as a ground for distinguishing significantly effective VANET context aware systems