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

Thanks to their ability to return interesting objects in a database, the skyline queries have received considerable attention from the database community over the last few years. Skyline analysis is a powerful tool in a wide spectrum of real applications including multi-criteria optimal decision making, preference answering and many applications where uncertain, imprecise and noisy data inherently exist. As large amounts of distributed data over an Internet are communicated and shared, an important problem is to retrieve the global skyline from all the distributed local sites. In this paper, based on the skyline query over centralised imperfect data where imperfection is modeled by the evidence theory, we propose to efficiently compute the global skyline from distributed local sites. The efficiency and effectiveness of our proposal are verified by extensive experimental results.

Efficient Distributed Skyline over Imperfect Data Modeled by the Evidence Theory

Top-k queries represent a vigorous tool to rank-order answers and return only the most interesting ones. ETop-k queries were introduced to discriminate answers in the context of evidential databases. Due to their interval degrees, such answers seem to be difficult to rank-order and to interpret. Two methods of ranking intervals were proposed in the evidential context. This paper presents an efficient implementation of these methods and discusses the experimental results obtained.

Evidential Top-k Queries Evaluation: Algorithms and Experiments

Les methodes actuelles pour la decouverte de services basees sur le comportement – decrit comme un processus metier (PM) – retournent encore un grand nombre de services offrant des fonctionnalites similaires. Dans cet article, nous proposons une strategie d’evaluation pour la recherche de services basee sur les quantiﬁcateurs linguistiques ﬂous et qui prend en compte la similarite structurelle entre les PM de services et la satisfaction des preferences sur les attributs de qualite de services (QdS). La requete utilisateur et les PM cibles sont representes a l’aide de graphes annotes ; les preferences de QdS sont modelisees au moyen des ensembles ﬂous.

Intégration de préférences dans la découverte et la sélection des services web

Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as process model). However, these approaches still remain with a high selectivity rate, resulting in a large number of services offering similar functionalities and behaviour. One way to improve the selectivity rate and to provide the best suited services is to cope with user preferences defined on quality attributes. In this paper, we propose and evaluate a novel approach for service retrieval that takes into account the service process model and relies both on preference satisfiability and structural similarity. User query and target process models are represented as annotated graphs, where user preferences on QoS attributes are modelled by means of fuzzy sets. A fexible evaluation strategy based on fuzzy linguistic quantifiers (such as almost all) is introduced. Then, two families of ranking methods are discussed. Finally, an extensive set of experiments based on real data sets is conducted, on the one hand, to demonstrate the efficiency and the scalability of our approach, and on the other hand, to analyze the effectiveness and the accuracy of the proposed ranking methods compared to expert evaluation.

https://hal.archives-ouvertes.fr/hal-00670615/document

Selecting and Ranking Business Process with Preferences: An Approach Based on Fuzzy Set

In the recent years, skyline queries become one of the predominant and most frequently used queries among preference queries in the database system. Based on the concept of Pareto dominance, skyline queries attempt to incorporate and provide a flexible query operator that returns objects (skylines) which are not being dominated (in sense of Pareto) by other objects in all dimensions (attributes) of the database. However, this process leads to two scenarios: either (i) a huge number of skyline objects are returned which are less informative for the end-users or (ii) a small number of skyline objects are retrieved which could be insufficient to serve the user needs. In this paper we tackle the second problem and we propose a new efficient approach to relax the skyline and increase its size. The basic idea is to build the fuzzy formal concept lattice for dominated objects, i.e., no skyline objects based on minimal distance between each concept and the ideal concept (i.e., the ideal object from the set of dominated objects w.r.t the user query). The relaxed skyline is given by the set S relax , formed by the union of skyline objects and the objects of the concept who has the minimal distance to the ideal concept and the size of S relax is equal to k. (k is the user defined parameter). Furthermore, we develop efficient algorithm to compute the relaxed skyline. A set of experiments is conducted to demonstrate the effectiveness and efficiency of the proposed approach.

Allel Hadjali

Papers

Efficient Distributed Skyline over Imperfect Data Modeled by the Evidence Theory

Evidential Top-k Queries Evaluation: Algorithms and Experiments

Intégration de préférences dans la découverte et la sélection des services web

Selecting and Ranking Business Process with Preferences: An Approach Based on Fuzzy Set

Concept Dissimilarity Based Approach for Skyline Relaxation