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Elkhlifi A.,Paris-Sorbonne University | Faiz R.,LARODEC
Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 | Year: 2010

Event extraction is a significant task in information extraction. This importance increases more and more with the explosion of textual data available on the Web, the appearance of Web 2.0 and the tendency towards the Semantic Web. Thus, we propose a generic approach to extract events from text and to analyze them. We propose an event extraction algorithm with a polynomial complexity O(n5), and a new similarity measurement between events. We use this measurement to gather similar events. We also present a semantic map of events, and we validate the first component of our approach by the development of the "EventEC" system. Copyright © 2010, American Association for Artificial Intelligence (www.aaai.org ). All rights reserved. Source


Azaza L.,University of Burgundy | Faiz R.,LARODEC | Benslimane D.,University Claude Bernard Lyon 1
Ingenierie des Systemes d'Information | Year: 2015

Online-resource reputation has become paramount. With the democratization of the Web and online social media (e.g., social networks) users have the opportunity to share their opinions on resources with millions of peers. These opinions also allow suppliers to understand the expectations and needs of consumers. Specialized sites such as eBay and Amazon allow users to give their opinions on a variety of products and services. Unfortunately, conflicting opinions and some users’ malicious behaviors do not help develop concise opinions. Users have different expertise level and spammers are joining the community with malicious behavior. It is then important to filter the opinions foremost reputation assessment. In this paper, we present an approach for filtering opinions using different techniques that address concerns such as user multi-identity and lack of user credibility. The proposed approach reduces first redundancy of opinions masked under different identifiers. Then, it detects the influences between users in the case of shared opinions and favors the most consistent profiles. The credibility assessment is based on a heterogeneous social graph module connecting users, opinions, and resources to capture the various relationships between them. The filtered opinions can then be used to calculate the reputation of Web services and sources. Our proposed approach efficiency is demonstrated through a set of experiments based on random data generation and variation of the different criteria considered in the detection of the real physical users. © 2015 Lavoisier. Source


Benferhat S.,University of Artois | Smaoui S.,LARODEC
Fuzzy Sets and Systems | Year: 2011

Many algorithms deal with non-experimental data in possibilistic networks. Most of them are direct adaptations of the probabilistic approaches. In this paper, we propose to represent another kind of data which is experimental data caused by external interventions in possibilistic networks. In particular, we present different and equivalent graphical interpretations of such manipulations using an adaptation of the 'do' operator to a possibilistic framework. We then propose an efficient algorithm to evaluate effects of non-simultaneous sequences of both experimental and non-experimental data. The main advantage of our algorithm is that it unifies treatments of the two kinds of data through the conditioning process with only a small extra-cost. © 2010 Published by Elsevier B.V. All rights reserved. Source


Elkhlifi A.,Paris-Sorbonne University | Faiz R.,LARODEC
2010 ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2010 | Year: 2010

Event extraction is a significant task in information extraction. This importance increases more and more with the explosion of textual data available on the Web, the appearance of Web 2.0 and the tendency towards the Semantic Web. Thus, we propose a generic approach to extract events from text and to analyze them. We propose an event extraction algorithm with a polynomial complexity O(n5), and a new similarity measurement between events. We use this measurement to gather similar events. We also present a semantic map of events, and we validate the first component of our approach by the development of the "EventEC" system. Source


Elkhlifi A.,Paris-Sorbonne University | Faiz R.,LARODEC
31st INFORSID 2013 | Year: 2013

A new challenge is added to the Natural Language Processing Community; how to analyze the new documents forms resulting from the Web 2.0? We are interested in a particular kind of information which is events. Thus, we propose a generic approach to extract and analyze events from text. We propose an event extraction algorithm with a polynomial complexity O(n5). This algorithm is based on developed semantic map of events. We validate the first component of our approach by the development of the "EventEC" system. Copyright © (2013) by INFORSID. Source

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