Agriculture is both highly dependent on water resources, and impacting on these resources. Regardless of advances in the area, the impacts of water scarcity and climatic changes on agriculture, as well as the impacts of agriculture on water resources, remain uncertain. Potentially, collaborative systems can support the management and information sharing of multifaceted and large scale data sources, providing valuable and indispensable information for research. However, these solutions rely on semantic interoperability, the construction of complex knowledge representation models, as well as information recovery. This work describes interoperability issues in the engineering process of the OntoAgroHidro, an ontology that represents knowledge about impacts of agricultural activities and climatic changes on water resources. The paper presents representative scenarios and questions, and discusses the reuse and integration of concepts using knowledge visualization techniques. Experiments on the information recovery scenario point out the potential and limitations of the OntoAgroHidro.201

Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.