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Sobre

Sobre

Sou professor associado do Departamento de Ciência de Computadores da Faculdade de Ciências da Universidade do Porto e coordenador do LIAAD, Laboratório de Inteligência Artificial e de Apoio à Decisão da UP. O LIAAD é um cenrto do INESC TEC desde 2007. Sou doutor em Ciência da Computação pela U. Porto, MSc. em Fundamentos de Tecnologia de Informação Avançada pelo Imperial College e Lic. Em Matemática Aplicada ramo Ciência de Computadores (U. Porto). Os meus interesses de investigação são Extração de Conhecimento (Data Mining) e Aprendizagem Automática (Machine Learning), em particular regras de associação, text mining e sistemas de recomendação. A minha investigação anterior inclui programação em lógica indutiva e data miing colaborativo. Eu leciono cursos relacionados com programação, processamento de informação, data mining e outras áreas da computação. Enquanto na Faculdade de Economia, onde permaneci de 1996 a 2009, lancei, com outros colegas, o mestrado em Análise de Dados e Sistemas de Apoio à Decisão (MADSAD), que coordenei de 2000 a Abril de 2008. Dirijo projetos em data mining e inteligência na web. Fui diretor do Mestrado em Ciência dos Computadores no DCC-FCUP de junho de 2010 a agosto de 2013. Co-organizei conferências internacionais (ECML / PKD 2015, Discovery Science 2009, ECML / PKDD 05 e EPIA 01), workshops e seminários em data mining e inteligência artificial. Fui Vice-Presidente da APPIA Associação Portuguesa para a Inteligência Artificial.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Alípio Jorge
  • Cargo

    Coordenador de Centro
  • Desde

    01 janeiro 2008
020
Publicações

2023

Geovisualisation Tools for Reporting and Monitoring Transthyretin-Associated Familial Amyloid Polyneuropathy Disease

Autores
Lopo, RX; Jorge, AM; Pedroto, M;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

Abstract
Transthyretin-associated Familial Amyloid Polyneuropathy (TTR-FAP) is a chronic fatal disease with a high incidence in Portugal. It is therefore relevant to provide professionals and citizens with a tool that enables a detailed geographical and territorial study. For this reason, we have developed an web based application that brings together techniques applied to spatial data that allow the study of the historical progression and growth of cases in patients' residential areas and areas of origin as well as an epidemic forecast. The tool enables the exploration of geographical longitudinal data at national, district and county levels. High density regions and periods can be visually identified according to parameters selected by the user. The visual evaluation of the data and its comparison across different time spans of the disease era can have an impact on more informed decision making by those working with patients to improve their quality of life, treatment or follow-up. The tool is available online for data exploration and its code is available on GitHub for adaptation to other geospatial scenarios.

2023

Text2Storyline: Generating Enriched Storylines from Text

Autores
Goncalves, F; Campos, R; Jorge, A;

Publicação
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT III

Abstract
In recent years, the amount of information generated, consumed and stored has grown at an astonishing rate, making it difficult for those seeking information to extract knowledge in good time. This has become even more important, as the average reader is not as willing to spare more time out of their already busy schedule as in the past, thus prioritizing news in a summarized format, which are faster to digest. On top of that, people tend to increasingly rely on strong visual components to help them understand the focal point of news articles in a less tiresome manner. This growing demand, focused on exploring information through visual aspects, urges the need for the emergence of alternative approaches concerned with text understanding and narrative exploration. This motivated us to propose Text2Storyline, a platform for generating and exploring enriched storylines from an input text, a URL or a user query. The latter is to be issued on the PortugueseWebArchive (Arquivo.pt), therefore giving users the chance to expand their knowledge and build up on information collected from web sources of the past. To fulfill this objective, we propose a system that makes use of the TimeMatters algorithm to filter out non-relevant dates and organize relevant content by means of different displays: `Annotated Text', `Entities', `Storyline', `Temporal Clustering' and `Word Cloud'. To extend the users' knowledge, we rely on entity linking to connect persons, events, locations and concepts found in the text to Wikipedia pages, a process also known as Wikification. Each of the entities is then illustrated by means of an image collected from the Arquivo.pt.

2023

Annotation and Visualisation of Reporting Events in Textual Narratives

Autores
Silvano, P; Amorim, E; Leal, A; Cantante, I; Silva, F; Jorge, A; Campos, R; Nunes, S;

Publicação
Proceedings of Text2Story - Sixth Workshop on Narrative Extraction From Texts held in conjunction with the 45th European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland, April 2, 2023.

Abstract
News articles typically include reporting events to inform on what happened. These reporting events are not part of the story being told but are nonetheless a relevant part of the news and can pose a challenge to the computational processing of news narratives. They compose a reporting narrative, which is the present study's focus. This paper aims to demonstrate through selected use cases how a comprehensive annotation scheme with suitable tags and links can properly represent the reporting events and the way they relate to the events that make the story. In addition, we put forward a proposal for their visual representation that enables a systematic and detailed analysis of the importance of reporting events in the news structure. Finally, we describe some lexico-grammatical features of reporting events, which can contribute to their automatic detection. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

2023

Proceedings of Text2Story - Sixth Workshop on Narrative Extraction From Texts held in conjunction with the 45th European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland, April 2, 2023

Autores
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Litvak, M;

Publicação
Text2Story@ECIR

Abstract

2023

Text Mining and Visualization of Political Party Programs Using Keyword Extraction Methods: The Case of Portuguese Legislative Elections

Autores
Campos, R; Jatowt, A; Jorge, A;

Publicação
Information for a Better World: Normality, Virtuality, Physicality, Inclusivity - 18th International Conference, iConference 2023, Virtual Event, March 13-17, 2023, Proceedings, Part I

Abstract
Extracting keywords from textual data is a crucial step for text analysis. One such process may involve a considerable amount of time when done manually. In this paper, we show how keyword extraction techniques can be used to untap texts of political nature. To accomplish this objective, we conduct a case-study on top of 16 Portuguese (PT) political party programs made available in the context of the legislative elections that took place in 30th of January 2022. Our contributions are two-fold. At the level of resources, we make available a curated dataset and a python notebook that systematizes the process of transforming text into quantitative data and into visual aspects. At the methodological level, we propose to extend the keyword extraction algorithm used in this study to extract the most relevant keywords, not only from individual political party programs, but also across the entire collection of documents. A further contribution is the case-study itself, which calls attention to the fact that such solutions may be of interest not only to common people, but also to journalists or politicians alike. Broadly, we demonstrate how the discussion and the analysis that stems from the results obtained may foster the political science research by making available large-scale processing of documents with marginal costs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Teses
supervisionadas

2022

Accelerating Deep Learning Training on High-Performance Computing with Storage Tiering

Autor
Marco Filipe Leitão Dantas

Instituição
UM

2022

Time-To-Event Prediction

Autor
Maria José Gomes Pedroto

Instituição
UP-FEUP

2022

Modelos de Negócios: Caso Explicações Online

Autor
Paulo Miguel Ferreira da Silva

Instituição
UP-FEP

2022

Music video generation with machine learning

Autor
Luís Miguel Ferreira Trinta

Instituição
UP-FCUP

2022

Learning Word Sense Representations from Neural Language Models

Autor
Daniel Alexandre Bouçanova Loureiro

Instituição
UP-FCUP