Agricultural Knowledge Extraction from Text Sources Using a Distributed MapReduce Cluster

Extracting and accessing knowledge in a KnowledgeBase is a crucial task. Documents must be computationallyunderstood and transformed into accessible knowledge. Specifically, the farming industry has a notable importance due tothe wide variety of information from text documents that needto be interpreted, often by a human. These documents, oftenrelates to regulations, chemicals, seeds and fertilizers amongothers. Moreover, automatize document processing increases itsimportance in areas like the European Union with its languageand regulation differences which increases the complexity offarming in general. Our approach aims to help users by meansof providing a scalable system using a distributed MapReducedocument cluster to process all this information to provide anaccessible way to this knowledge thereafter.