Applying ontologies in the dairy farming domain for big data analysis

In the Dutch SmartDairyFarming project, main dairy industry organizations like FrieslandCampina, AgriFirm and CRV work together on better decision support for the dairy farmer on daily questions around feeding, insemination, calving and milk production processes. This paper is concerned with the inherent semantic interoperability problem in decision support information in a variety of big data sources containing static and dynamic sensor data of individual cows. Semantic alignment is achieved using ontologies and linked data mechanisms on a large amount of sensor data, such as grazing activity, feed intake, weight, temperature and milk production of individual cows at 7 dairy farms in The Netherlands. A Common Dairy Ontology (CDO) and a specific measurement ontology have been developed and used to transform 12GB of yearly sensor data into 350GB of RDF triples, made accessible via a SPARQL interface on the Apache Jena Fuseki triplestore. A few example applications have been developed to show how the CDO can be used for decision support and historic analysis. The performance of our linked data semantic solution is acceptable for analysis queries on large sets of data. Without optimization of queries the time for answering queries ranged from a few seconds to a couple of minutes. © 2016, CEUR-WS. All rights reserved