Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Trust management for the Semantic Web

The B Book

http://www.umbertostraccia.it/cs/download/papers/JWS08/JWS08.pdf

Managing uncertainty and vagueness in description logics for the Semantic Web

https://www.um.edu.mt/library/oar/bitstream/123456789/22142/1/bt45-aij.pdf

Automatic generation of textual summaries from neonatal intensive care data

Preferences in AI: An overview

Uncertain, imprecise and noisy data arise in a number of domains including sensor networks and data integration. Skyline analysis is a powerful tool in a wide spectrum of real applications involving multi-criteria optimal decision making. The skyline operator aims at returning the most interesting objects in a database. Previous researches showed that the skyline size over uncertain data is too large to be exploited. In this paper, we propose an advanced skyline analysis over uncertain databases where uncertainty is modelled by the evidence theory. We particularly tackle the following two important issues: (1) model the skyline query over an evidential database (2) rank the evidential skyline result and retrieve k skyline objects that are expected to have the highest score with considering the confidence level of the objects. We also study its impact on the top-k result. The efficiency and effectiveness of our proposal are verified by extensive experimental results.

Imperfect top-k skyline query with confidence level

IoT (Internet of Things) is a paradigm that connects multiple and diverse smart objects via internet, it is currently widely used in a number of real-life fields. Smart interconnected devices generate tremendous amounts of raw data using their sensors. Such data need to be processed, analyzed, and mined efficiently to obtain improved information that help to take corrective actions and provide advanced services. This paper provides a critical review of existing methods on data fusion in IoT context with a particular focus on the most applicable and mature mathematical methods. Inherent characteristics of data fusion theories are restated, then a comparative study is conducted by pointing out the advantages and limitations of each method taking into account data imperfections (uncertainty, inaccuracy, etc.) and IoT environments constraints (real-time, etc.). This work constitutes a big step towards building a guide for researchers working in the field of data fusion in IoT. 

Mathematical Methods for Data Fusion in IoT: A Survey

Resume. Les requetes skyline constituent un outil puissant pour l’analyse de donnees multidimensionnelles et la decision multicritere. Elles permettent d’extraire les meilleurs objets a partir d’un ensemble de donnees. Elles s’appuient sur le principe de dominance de Pareto. En pratique, le calcul du skyline peut conduire a deux scenarios : soit (i) un nombre important d’objets sont retournes, ce qui est generalement peu informatif du point de vue de l’utilisateur, soit (ii) un nombre reduit d’objets sont retournes, ce qui peut etre insuffisant pour les decisions des utilisateurs. Dans cet article, nous abordons le second probleme et proposons une approche permettant de le traiter. L’idee consiste a rendre le skyline plus permissive en lui ajoutant des objets qui, proprement dit, ne lui appartiennent pas mais qui sont proches des objets skyline. L’approche s’appuie sur une nouvelle relation de dominance floue appelee «Much Preferred». Un algorithme efficace pour calculer le skyline relaxe est propose. Une serie d’experiences sont menees pour demontrer la pertinence de notre approche et la performance de l’algorithme propose.

Relaxation des Requêtes Skyline : Une Approche Centrée Utilisateur.

Volume and veracity of Resource Description Framework (RDF) data in the web are two main issues in managing information. Due to the diversity of RDF data, several researchers enriched the basic RDF data model with trust information to rate the trustworthiness of the collected data.

Statistical Methods for Use in Analysis of Trust-Skyline Sets

In recent years, crowdsourcing has become essential in a wide range of Web applications. Human factors play a key role in achieving high quality answers in crowdsourcing-based solving tasks. The most major factor is pertained to the uncertainty of workers about the responses that they provide to resolve the task at hand. On the other hand, workers may have diverse levels of expertise and skill. It is then important to take into account both the degrees of uncertainty and expertise when aggregating the set of worker answers. In this paper, we investigate some advanced crowdsourcing aggregation methods to find the correct answers by leveraging both expertise and uncertainty of workers in a unified way. Workers' uncertainty is represented in a possibilistic way, while a fine-grained scale for interpreting the degrees of skill is introduced. Finally, we present some comprehensive experiments to validate the effectiveness of our proposal.

Allel Hadjali

Papers

Imperfect top-k skyline query with confidence level

Mathematical Methods for Data Fusion in IoT: A Survey

Relaxation des Requêtes Skyline : Une Approche Centrée Utilisateur.

Statistical Methods for Use in Analysis of Trust-Skyline Sets

Leveraging human factors to enhance query answering in crowdsourcing systems