Legimatics and AI tools for the monitoring of EU Legislation in agrifood and SDGs

This work presents a comprehensive mechanism with algorithms for annotating legal norms, classifying EU legislation, and linking them to SDGs objectives. The dataset comprised 15082 EU legislative documents in AKN file format from 1962 to 2021. Complete work is divided into three tasks: Detection and annotation of legal Definitions, Model design for classification of EU legislative documents to Goals and Targets of SDGs, and Linking EU legislative documents to Goals and Targets of SDGs. The first task is performed using Symbolic AI supported by LegalXML annotation. The annotation of only Delimiting Definitions is the target of this task. For the purpose two independent Artificial Intelligence-based algorithms are designed for two different scenarios. These algorithms are implemented in Python using the ElementTree library and rule-based mining to annotate targeted text. The annotation is validated through indentation checks in the AKN format. The first algorithm annotates 899, while the second algorithm annotates 1,272 documents. A total of 11,705 Definitions are successfully annotated. For the second task, a new ML-based multilabel class model is designed to link EU legislative texts to the Goals and Targets of SDGs. Based upon the literature review, two algorithms, SVM and KNN, were tried. SVM outperforms KNN with an accuracy of 53.34%, a weighted F-score of 70.04% and a macro F-score of 57.94% on the SDGs classification at the Goals level. At the Target level, SVM achieved 46.56% accuracy, 56.60% weighted F-score and 30.61% macro F-score. In the third task, legislative text and annotated Delimiting Definitions are successfully linked with the Goals and Targets of SDGs using model designed in second task. By integrating annotation, classification, and linking of EU legislation with SDGs, this research provides a robust mechanism for policymakers and researchers to monitor legislative alignment with SDGs objectives, enabling informed decision-making and effective policy formulation.