Antimicrobial resistance is a major worldwide public health problem. The misuse of antimicrobial agents and the delay in spotting emerging and outbreak resistances in current biosurveillance and monitoring systems are regarded by health bodies as underlying causes of increasing resistance. In this thesis, we explore novel methods to monitor and analyze antimicrobial resistance trends to improve existing biosurveillance systems. More specifically, we investigate the use of semantic technologies to foster integration and interoperability of interinstitutional and cross-border microbiology laboratory databases. Additionally, we research an original, fully data-driven trend analysis method based on trend extraction and machine learning forecasting to enhance antimicrobial resistance analyses.