Ontology matching for big data applications in the smart dairy farming domain

This paper addresses the use of ontologies for combining different sensor data sources to enable big data analysis in the dairy farming domain. We have made existing data sources accessible via linked data RDF mechanisms using OWL ontologies on Virtuoso and D2RQ triple stores. In addition, we have created a common ontology for the domain and mapped it to the existing ontologies of the different data sources. Furthermore, we verified this mapping using the ontology matching tools HerTUDA, AML, LogMap and YAM++. Finally, we have enabled the querying of the combined set of data sources using SPARQL on the common ontology. 1 Background and context Dairy farmers are currently in an era of precision livestock farming in which information provisioning for decision support is becoming crucial to maintain a competitive advantage. Therefore, getting access to a variety of data sources on and off the farm that contain static and dynamic individual cow data is necessary in order to provide improved answers on daily questions around feeding, insemination, calving and milk production processes. In our SmartDairyFarming project, we have installed sensor equipment to monitor around 300 cows each at 7 dairy farms in The Netherlands. These cows have been monitored during the year 2014 which has generated a huge amount of sensor data on grazing activity, feed intake, weight, temperature and milk production of individual cows stored in databases at each of the dairy farms. The amount of data recorded per cow is at least 1MB of sensor values per month, which adds up to 3.6GB of data per dairy farm per year. In addition, static cow data is available in a data warehouse at the national milk registration organization, including date of birth, ancestors and current farm. Finally, another existing data source contains satellite information on the amount of biomass in grasslands in the country that is important for measuring the feed intake of cows during grazing. We focused on decision support for the dairy farmer on feed efficiency in relation to milk production. Thus, the big data analysis question is: “How much feed did an individual cow consume in a certain time period at a specific grassland parcel and how does this relate to the milk production in that period?”. 2 Ontology matching approach We selected one of the dairy farms (DairyCampus) and created with TopBraid composer a small ontology with 12 concepts that covers among others the grasslands of a farm and grazing periods of cows. This ontology contains the concept “perceel” which is Dutch for parcel. In addition, we selected the data source with satellite information about biomass in grasslands (AkkerWeb, www.akkerweb.nl). This data source already had an ontology defined with 15 concepts that contains the concept “plot” which is similar to parcel but with different properties. Furthermore, we created with TopBraid composer a common ontology for the domain with 28 concepts on feed efficiency (see Fig. 1). Fig. 1. Common ontology excerpt for feed efficiency in dairy farming. The challenge was to find a match between the concepts and properties in the common ontology and both specific DairyCampus and Akkerweb ontologies, especially regarding the concepts “parcel”, “perceel” and “plot”. We have initially created manual mappings between classes and properties in TopBraid using rdfs:subClassOf and owl:equivalentProperty relations. Based on relatively few and simple matches we created initial alignments between properties and classes (see Fig. 2). Use of a matching tool or system however, provides us with opportunities to verify our current findings and better support our efforts in finding alignments between the other concepts in our ontologies. We used a literature survey of matching techniques and supporting matching systems in [1] to identify both a suitable matching technique and find tools supporting that technique. We consider language-based matching as the appropriate type of matching since it focuses on syntactic element-level natural language processing of words. rdfs: subClassOf owl: equivalentProperty owl: equivalentProperty