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About

I am an Associate Professor with the Department of Informatics Engineering of FEUP, University of Porto, and a senior researcher at INESC TEC. I have collaborated on and been in charge of projects in the areas of cultural heritage, multimedia databases, information retrieval and combinatorial optimisation. I have supervised 9 PhD and 33 MSc dissertations. I was the technical leader of the SAPO/U.Porto extension laboratory for 5 years. My teaching activities include courses in the Informatics Engineering and Information Science programmes. Research Data Management is the core of my current research activity. I am the PI of TAIL (FCT/POCI), on research data management workflows for data publication, and I lead the DataPublication@U.Porto pilot in the EUDAT european initiative. I am a member of the Working Group for the National Policy on Open Science with SECTES. My research interests include information retrieval, digital preservation and knowledge representation.

Interest
Topics
Details

Details

005
Publications

2019

Data Deposit in a CKAN Repository: A Dublin Core-Based Simplified Workflow

Authors
Karimova, Y; Castro, JA; Ribeiro, C;

Publication
Communications in Computer and Information Science - Digital Libraries: Supporting Open Science

Abstract

2019

Interplay of Documents' Readability, Comprehension and Consumer Health Search Performance Across Query Terminology

Authors
Lopes, CT; Ribeiro, C;

Publication
Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, CHIIR 2019, Glasgow, Scotland, UK, March 10-14, 2019

Abstract
Because of terminology mismatches, health consumers frequently face difficulties while searching the Web for health information. Difficulties arise in query formulation but also in understanding the retrieved documents. In this work we analyze how documents' readability affects users' comprehension and how both affect the retrieval performance, measured in different ways. In addition, we analyze how performance measures relate with each other. For this purpose we have conducted a laboratory user study with 40 participants. We found that readability is essential for a document to be at least partially relevant and that it becomes even more important if the document has medico-scientific terminology. Moreover, the relevance of a document to a specific user highly depends on its comprehension. In lay queries we found the medical accuracy of users' answers is related to the session's relevance assessments. This shows that users can, at least in part, relate their relevance assessments with the medical accuracy of the documents. On the other hand, this relationship does not exist with medico-scientific queries. © 2019 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

2018

The influence of document characteristics on the quality of health web documents

Authors
Oroszlanyova, M; Lopes, CT; Nunes, S; Ribeiro, C;

Publication
Iberian Conference on Information Systems and Technologies, CISTI

Abstract
The quality of consumer-oriented health information on the Web is usually assessed through the medical certification of websites. These tools are built upon quality indicators but, so far, no standard set of indicators has been defined. The objective of the present study is to explore the popularity of specific document features and their influence on the quality of health web documents, using HON code as ground truth. A set of top-ranked health documents retrieved from a major search engine was characterized in a univariate analysis, and then used in a bivariate analysis to seek features that affect documents' quality. The univariate analysis provides insights into the characteristics of the overall population of the health web documents. The bivariate analysis reveals strong relations between documents' quality and a set of features (namely split content, videos, images, advertisements, English language) that are potential quality indicators. We characterized health web documents and identified specific document features that can be used to assess whether the information in such documents is trustworthy. The main contribution of this work is to provide other features as candidate indicators of quality. Non-health professionals can use these indicators in automatic and manual assessments of health content. © 2018 AISTI.

2018

Can user and task characteristics be used as predictors of success in health information retrieval sessions?

Authors
Oroszlanyova, M; Lopes, CT; Nunes, S; Ribeiro, C;

Publication
Information Research

Abstract
Introduction. The concept and study of relevance has been a central subject in information science. Although research in information retrieval has been focused on topical relevance, other kinds of relevance are also important and justify further study. Motivational relevance is typically inferred by criteria such as user satisfaction and success. Method. Using an existing dataset composed by an annotated set of health Web documents assessed for relevance and comprehension by a group of users, we build a multivariate prediction model for the motivational relevance of search sessions. Analysis. The analysis was based on lasso variable selection, followed by model selection using multiple logistic regression. Results. We have built two regression models; the full model, which considers all variables of the dataset, has a lower estimated prediction error than the reduced model, which contains the statistically-significant variables from the full model. The higher values of evaluation metrics, including accuracy, specificity and sensitivity in the full model support this finding. The full model has an accuracy of 91.94%, and is better at predicting motivational relevance. Conclusions. Our findings suggest features that can be considered by search engines to estimate motivational relevance, to be used in addition to topical relevance. Among these features, a high level of success in Web search and in health information search on social networks and chats are some of the most influencing user features. This shows that users with higher computer literacy might feel more satisfied and successful after completing the search tasks. In terms of task features, the results suggest that users with clearer goals feel more successful. Moreover, results show that users would benefit from the help of the system in clarifying the retrieved documents.

2018

Digital Libraries for Open Knowledge, 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, Porto, Portugal, September 10-13, 2018, Proceedings

Authors
Méndez, E; Crestani, F; Ribeiro, C; David, G; Lopes, JC;

Publication
TPDL

Abstract

Supervised
thesis

2017

SocialDendro: Aplicação de técnicas das redes sociais à gestão colaborativa de conjuntos de dados

Author
Nelson Miguel da Costa Martins Pereira

Institution
UP-FEUP

2017

Disseminação de conteúdos audiovisuais na web: uso de um perfil de aplicação para a gestão e agregação dos recursos da TVU

Author
Sara Catarina Pinheira de Oliveira

Institution
UP-FEUP

2017

Metadata gamification: Jogos sérios para melhoria de descrição de dados da investigação

Author
Bruno Coelho da Silva

Institution
UP-FEUP

2017

Validação e Certificação digital de CV

Author
Joana Lopes Beleza

Institution
UP-FEUP

2016

Usage-driven Application Profile Generation Using Ontologies

Author
João Miguel Rocha da Silva

Institution
UP-FEUP