RESUMO
Ensuring the sustainability and circularity of mixed crop-ruminant livestock systems is essential if they are to deliver on the enhancement of long-term productivity and profitability with a smaller footprint. The objectives of this study were to select indicators in the environmental, economic and social dimensions of sustainability of crop-livestock systems, to assess if these indicators are relevant in the operational schedule of farmers, and to score the indicators in these farm systems. The scoring system was based on relevance to farmers, data availability, frequency of use, and policy. The study was successful in the assemblage of a suite of indicators comprising three dimensions of sustainability and the development of criteria to assess the usefulness of these indicators in crop-ruminant livestock systems in distinct agro-climatic regions across the globe. Except for ammonia emissions, indicators within the Emissions to air theme obtained high scores, as expected from mixed crop-ruminant systems in countries transitioning towards low emission production systems. Despite the inherent association between nutrient losses and water quality, the sum of scores was numerically greater for the former, attributed to a mix of economic and policy incentives. The sum of indicator scores within the Profitability theme (farm net income, expenditure and revenue) received the highest scores in the economic dimension. The Workforce theme (diversity, education, succession) stood out within the social dimension, reflecting the need for an engaged labor force that requires knowledge and skills in both crop and livestock husbandry. The development of surveys with farmers/stakeholders to assess the relevance of farm-scale indicators and tools is important to support direct actions and policies in support of sustainable mixed crop-ruminant livestock farm systems.
Assuntos
Agricultura , Criação de Animais Domésticos , Fazendeiros , Gado , Animais , Criação de Animais Domésticos/métodos , Agricultura/métodos , Produtos Agrícolas , Fazendas , Conservação dos Recursos Naturais/métodos , Monitoramento Ambiental/métodosRESUMO
BACKGROUND: Biomedical computational systems benefit from ontologies and their associated mappings. Indeed, aligned ontologies in life sciences play a central role in several semantic-enabled tasks, especially in data exchange. It is crucial to maintain up-to-date alignments according to new knowledge inserted in novel ontology releases. Refining ontology mappings in place, based on adding concepts, demands further research. RESULTS: This article studies the mapping refinement phenomenon by proposing techniques to refine a set of established mappings based on the evolution of biomedical ontologies. In our first analysis, we investigate ways of suggesting correspondences with the new ontology version without applying a matching operation to the whole set of ontology entities. In the second analysis, the refinement technique enables deriving new mappings and updating the semantic type of the mapping beyond equivalence. Our study explores the neighborhood of concepts in the alignment process to refine mapping sets. CONCLUSION: Experimental evaluations with several versions of aligned biomedical ontologies were conducted. Those experiments demonstrated the usefulness of ontology evolution changes to support the process of mapping refinement. Furthermore, using context in ontological concepts was effective in our techniques.
Assuntos
Ontologias Biológicas , Disciplinas das Ciências Biológicas , SemânticaRESUMO
BACKGROUND: Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer's Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. METHODS: In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer's Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. RESULTS: The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. CONCLUSIONS: We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.