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  • Name

    André Fernandes Santos
  • Cluster

    Computer Science
  • Role

    External Student
  • Since

    05th April 2017


Derzis: A Path Aware Linked Data Crawler

dos Santos, AF; Leal, JP;

10th Symposium on Languages, Applications and Technologies, SLATE 2021, July 1-2, 2021, Vila do Conde/Póvoa de Varzim, Portugal.



DAOLOT: A Semantic Browser

Silva, JB; Santos, A; Leal, JP;

9th Symposium on Languages, Applications and Technologies, SLATE 2020, July 13-14, 2020, School of Technology, Polytechnic Institute of Cávado and Ave, Portugal (Virtual Conference).

The goal of the Semantic Web is to allow the software agents around us and AIs to extract information from the Internet as easily as humans do. This semantic web is a network of connected graphs, where relations between concepts and entities make up a layout that is very easy for machines to navigate. At the moment, there are only a few tools that enable humans to navigate this new layer of the Internet, and those that exist are for the most part very specialized tools that require from the user a lot of pre-existing knowledge about the technologies behind this structure. In this article we report on the development of DAOLOT, a search engine that allows users with no previous knowledge of the semantic web to take full advantage of its information network. This paper presents its design, the algorithm behind it and the results of the validation testing conducted with users. The results of our validation testing show that DAOLOT is useful and intuitive to users, even those without any previous knowledge of the field, and provides curated information from multiple sources instantly about any topic.


Applying a text mining framework to the extraction of numerical parameters from scientific literature in the biotechnology domain

Santos, A; Nogueira, R; Lourenço, A;

ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents.