UrbanAgriKG: A knowledge graph on urban agriculture and its embeddings

Abstract

This research article outlines a study that examines the creation of a comprehensive knowledge graph specifically designed for the domain of urban agriculture. The research centers on the acquisition, synthesis, and arrangement of pertinent information from various origins in order to establish a specialized knowledge graph tailored for urban agricultural systems. The graph depicts the interrelationships and attributes of various entities, including urban farms, crops, farming methods, environmental factors, and economic elements. Moreover, this study investigates the efficacy of different graph embedding methodologies in the domain of urban agriculture. The aforementioned techniques are utilized in the context of the urban agriculture knowledge graph in order to extract significant representations of entities and their relationships. The primary objective of the experimental study is to investigate and reveal semantic relationships, patterns, and predictions that have the potential to improve decision-making processes and optimize practices in the field of urban agriculture. The results of this study make a significant contribution to the existing body of knowledge in the area of urban agriculture. Additionally, they offer valuable insights into the potential uses of graph embedding techniques within this field.

Keywords

• urban agriculture
• knowledge graph
• graph embedding methods
• link prediction
• graph representation learning
• sustainability