Sep 6 – 8, 2023
Universidad Católica del Norte
America/Santiago timezone

WDKE Program


(WDKE 2023)

Friday, September 08, 2023

09.00-11.00 hrs. [Chilean Continental Time]

Final Program

Short description:

The Workshop on Data and Knowledge Engineering (WDKE) is a space for the dissemination of scientific and professional academic activity in the area of data and knowledge enginnering, including invited talks and presentations of accepted paper on pattern recognition and data engineering.

Organized by:

Núcleo de Inteligencia Artificial y Data Science, Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte



Claudio Meneses Villegas (

Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte, Antofagasta, Chile

Francisco García Barrera (

Facultad de Ingeniería y Arquitectura

Universidad Arturo Prat, Iquique, Chile

Date and Time:

 Friday, September 08, 2023. 09 - 11 am. [Chile Continental Time]


K-121 C







09.00-10.00 am.

Narrative maps: extraction and visualization of interactive narratives and applications

Dr. Brian Keith N.

10.00-11.00 am.

Lifelong learning con generative memory replay

Dr. Bogdan Raducanu


Invited Talk #1

Friday, September 08, 2023

09.00-10.00 am. [Chile Continental Time]

Narrative maps: extraction and visualization of interactive narratives and applications

Speaker: Dr. Brian Keith Norambuena

PhD in Computer Science, Virginia Tech, USA, Department of Computing and Systems Engineering, Universidad Católica del Norte, Antofagasta, Chile


Abstract. Narratives are fundamental to our understanding of the world and are present in all activities that involve representing events over time. Narrative analysis has a range of applications in computational journalism, intelligence services, and disinformation modeling. In this presentation, we will explore a technique for extracting and visualizing computational narratives through narrative maps in news datasets. Furthermore, we will demonstrate how to enhance this representation through interactive artificial intelligence, thereby providing users of the system with a customizable narrative representation to assist them in their narrative analysis tasks. Finally, we will present the results of a user evaluation in a narrative analysis task. Specifically, we will showcase the different strategies employed by the users and the advantages offered by the combination of narrative maps model and interactive artificial intelligence.





Short Bio.

Brian Keith Norambuena is an Assistant Professor at the Department of Computing and Systems Engineering. He obtained his Ph.D. in Computer Science and Applications at Virginia Tech in 2023. His research areas include visualization, artificial intelligence, and computational narratives. Brian Keith obtained a degree in Civil Engineering in Computing and Informatics in 2016, a bachelor’s degree in mathematics in 2017, and a master’s degree in computer engineering in 2017 from Universidad Católica del Norte. In 2019, he began his doctoral studies in Computer Science and Applications at Virginia Tech under the Fulbright Faculty Development program, with the support of Universidad Católica del Norte and Becas Chile. He has recently completed his doctorate and has returned to Chile to work as an academic at Universidad Católica del Norte. His research focuses on the extraction and visualization of computational narratives using interactive narrative maps based on artificial intelligence techniques.


Invited Talk #2

Friday, September 8, 2023

10.00-11.00 am [Chile Continental Time]

Lifelong learning con generative memory replay

Speaker: Dr. Bogdan Raducanu

Senior Researcher, LAMP (Learning and Machine Perception) Group, Centre for Computer Vision, Barcelona, Spain


Abstract. This talk intends to introduce the concept of lifelong learning (or continuous learning) and its application to the field of computer vision. The most common way to train a neural network is considering that we have all the necessary data from the beginning. But in case of real problems, this is not the proper scenario. For example, if we consider the case of a robot that has to continuously explore new sites or video surveillance, where the representation of a person must be updated to make its identification more robust, the neural network has to be retrained to accommodate this new information. In other words, another training strategy is required which is designated as 'sequential learning'. But a recognized problem associated with this paradigm refers to 'catastrophic forgetting', i.e., the neural network tends to forget prior knowledge when re-trained with new data. There are several strategies to limit the effects of catastrophic forgetting, and one of them is 'generative memory replay'.



Short Bio.

Dr. Bogdan Raducanu received a degree in computer science from the Polytechnic University of Bucharest (Romania, in 1995) and the title of doctor "Cum Laude" also in computer science from the University of the Basque Country, in 2001. Between 2002-2004 he was a researcher postdoctoral fellow at the Technical University of Eindhoven, in the Netherlands. In 2004 he joined the Computer Vision Center (CVC) with a "Ramón y Cajal" scholarship and since 2009 he is a senior researcher and project manager. His research interests are in the areas of machine vision and deep learning with applications to lifelong learning, generative models, and robotics.