Agricultural Knowledge Extraction from Text Sources using a distributed MapReduce Cluster

Extracting and accessing knowledge in a Knowledge Base is a crucial task. Documents must be computationally understood and transformed into accessible knowledge. Specifically, the farming industry has a notable importance due to the wide variety of information from text documents that need to be interpreted, often by a human. These documents, often relates to regulations, chemicals, seeds and fertilizers among others. Moreover, automatize document processing increases its importance in areas like the European Union with its language and regulation differences which increases the complexity of farming in general. Our approach aims to help users by means of providing a scalable system using a distributed MapReduce document cluster to process all this information to provide an accessible way to this knowledge thereafter.