Quality Web Information Retrieval: Towards Improving Semantic Recommender Systems with Friendsourcing

Web content quality is crucial in any domains, but it is even more critical in the health and e-learning ones. Users need to retrieve information that is precise, believable, and relevant to their problem. With the exponential growth of web contents, Recommender System has become indispensable for discovering quality information that might interest or be needed by web users. Quality-based Recommender Systems take into account quality criteria like credibility, believability, readability. In this paper, we present an approach to conceive Social Semantic Recommender Systems. In this approach a friendsourcing strategy is applied to better adequate recommendations to the user needs. The friendsourcing strategy focuses on the use of social force to assess quality of web content. In this paper we introduce the main research issues of this approach and detail the road-map we are following in the QHIR Project

Author name extraction in blog web pages: a machine learning approach

International audienceThis paper presents research results concerning the automatic extraction of author names that are explicitly mentioned in blog web pages. It shows that some NLP pre-preprocessing stages (NE recognition, coreference resolution) prior to a SVM classification have a positive impact on accuracy.Cet article présente les résultats de travaux ayant pour but l'extraction automatique de noms d'auteurs explicites dans des articles de blogs. Il montre que l'ajout de pré-traitements relevant du TAL (détection d'entités nommées, résolution des coréférences) avant une classification de type SVM améliore les performances

Ranking Biomedical Annotations with Annotator’s Semantic Relevancy

Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator’s knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining association rules between an annotator and a biomedical entity. The second way is frequent pattern mining from historical annotations, which reveals common features of biomedical entities that an annotator can annotate with high quality. We propose a weighted and concept-extended RDF model to represent an annotator, a biomedical entity, and their background attributes and merge information from the two ways as the context of an annotator. Based on that, we present a method to rank the annotations by evaluating their correctness according to user’s vote and the semantic relevancy between the annotator and the annotated entity. The experimental results show that the approach is applicable and efficient even when data set is large

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Uso de Regressão Logística para Modelar e Avaliar a Credibilidade em Aplicações Web

A popularização das aplicações Web tem feito surgir novos serviços acada dia, demandado mecanismos que assegurem a credibilidade desses servi-ços. Neste trabalho utilizamos regressão logística para modelar e avaliar a cre-dibilidade de um serviço da Web, considerando diferentes critérios associadosao serviço e seus fornecedores. A fim de validar nossa metodologia, executamosexperimentos usando uma base de dados real, a partir da qual avaliamos essesmodelos de credibilidade. Os resultados obtidos são muito bons, apresentandoganhos representativos, quando comparados à linha-de-base, mostrando assimque a metodologia proposta é promissora e pode ser usada para fortalecer aconfiança dos usuários nos serviços providos na Web

On the Use of PU Learning for Quality Flaw Prediction in Wikipedia

[EN] In this article we describe a new approach to assess Quality Flaw Prediction in Wikipedia. The partially supervised method studied, called PU Learning, has been successfully applied in classi cations tasks with traditional corpora like Reuters-21578 or 20-Newsgroups. To the best of our knowledge, this is the rst time that it is applied in this domain. Throughout this paper, we describe how the original PU Learning approach was evaluated for assessing quality flaws and the modi cations introduced to get a quality aws predictor which obtained the best F1 scores in the task \Quality Flaw Prediction in Wikipedia" of the PAN challenge.Edgardo Ferretti and Marcelo Errecalde thank Universidad Nacional de San Luis (PROICO 30310). The collaboration of UNSL, INAOE and UPV has been funded by the European Commission as part of the WIQ-EI project (project no. 269180) within the FP7 People Programme. Manuel Montes is partially supported by CONACYT, No. 134186. The work of Paolo Rosso was carried out also in the framework of the MICINN Text-Enterprise (TIN2009-13391-C04-03) research project and the Microcluster VLC/Campus (International Campus of Excellence) on Multimodal Intelligent Systems.Ferretti, E.; Hernández Fusilier, D.; Guzmán Cabrera, R.; Montes Y Gómez, M.; Errecalde, M.; Rosso, P. (2012). On the Use of PU Learning for Quality Flaw Prediction in Wikipedia. CEUR Workshop Proceedings. 1178. http://hdl.handle.net/10251/46566S117

Social Tag-Based Recommendation Services

Recommendation systems are a staple of Web 2.0. Sites such as Amazon.com and Netflix, for example, use recommendation systems to suggest products to customers. Currently, most of these systems involve looking at numerical ratings to judge user interest. These methods are effective, but they do not take into account the context in which the users rated the objects. This project aims to develop a tag based recommendation system to take context into account. Popular websites such as del.icio.us and Citeulike.org already use this data model, but do not generate recommendations from it.The specific goal is to recommend academic papers to researchers