| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| DM_HugoSilva_MEI_2024 | 2.75 MB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
In recent years, growing concerns about deforestation have driven the need to monitor
the origin and history of the wood arriving at factories. This has led to the adoption
of traceability systems in the forestry sector. However, many of these systems are still
manual and paper-based, which makes them susceptible to errors and falsification.
With the advancement of Industry 4.0 and the digitalisation of forestry operations, there
is an opportunity for the digital transformation of traceability. However, the digitalisation
process faces challenges at various levels, one of the main issues being that the information
sources are dispersed among the various stakeholders in the forest supply chain, resulting
in inaccurate and hard-to-access data, which hinders a comprehensive analysis of the
wood’s journey.
In this context, the present work proposes a traceability system that integrates data from
the various stages encompassing forestry exploitation, from the forest to the factory, ensuring
a continuous flow of information. The system is based on an ontology that, in
addition to formalising the knowledge necessary for traceability, allows for the identification
of errors and inconsistencies through reasoning mechanisms, thereby ensuring the
transparency and reliability of the collected records.
Furthermore, based on the instantiated ontology, Graph Machine Learning techniques are
used to train a model capable of predicting missing data and identifying implicit semantic
relations.
The approach was evaluated in the context of the Floresta 4.0 project and showed promising
results in terms of its effectiveness. In addition to addressing the needs of traceability,
it detected inconsistencies that had not previously been identified by domain experts.
Description
Keywords
Ontologia Rastreabilidade Graph Machine Learning Cadeia de Abastecimento Florestal Transparência