=Paper=
{{Paper
|id=Vol-1545/om2015_TSpaper5
|storemode=property
|title=Ontology matching for big data applications in the smart dairy farming domain
|pdfUrl=https://ceur-ws.org/Vol-1545/om2015_TSpaper5.pdf
|volume=Vol-1545
|dblpUrl=https://dblp.org/rec/conf/semweb/VerhooselBE15
}}
==Ontology matching for big data applications in the smart dairy farming domain==
https://ceur-ws.org/Vol-1545/om2015_TSpaper5.pdf
Ontology Matching for Big Data Applications in the
Smart Dairy Farming Domain
Jack P.C. Verhoosel, Michael van Bekkum and Frits K. van Evert
TNO Connected Business, Soesterberg, The Netherlands
{jack.verhoosel,michael.vanbekkum}@tno.nl
Wageningen UR, Wageningen, The Netherlands
frits.vanevert@wur.nl
Abstract. 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++. Fi-
nally, 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 in-
formation provisioning for decision support is becoming crucial to maintain a compet-
itive 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 pro-
vide 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 in-
formation 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 creat-
ed 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, espe-
cially 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 rela-
tively 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 lan-
guage processing of words.
owl: equivalentProperty
owl: equivalentProperty
rdfs: subClassOf rdfs: subClassOf
Fig. 2. Mapping of classes and properties based on the matching result.
There are numerous tools available that support this specific matching technology,
mostly from academic efforts. Some however are no longer in active use, either being
outdated or not maintained anymore [2].
We have selected several matching systems that support our requirement of lan-
guage-based matching: HerTUDA [3,4], AgreementMaker Light (AML) [5], LogMap
[6], and YAM++ [7]. We have started to investigate the possibilities of these tools to
find alignments of concepts and properties in our ontologies. Initial efforts with the
concepts shown in Fig. 2 have not led to successful matches and alignments yet, how-
ever. The HerTUDA, LogMap and YAM++ tools were difficult to install and execute.
The AML worked fine, but could not entirely find the relation between “parcel”,
“perceel” and “plot”. Further analysis is required to find out whether this is due to
inappropriate matching techniques or to the specific ontologies that we offered to the
tool.
3 SPARQL queries and triple stores
In order to show that the mapping of the common ontology to the specific ontolo-
gies works properly, we generated in Topbraid a few instances of an Akkerweb plot
and a DairyCampus perceel. In addition, we build a simple select query using the
common ontology to retrieve all parcels and for each parcel the properties name, bio-
mass, surface and test.
Fig. 3. Select query on common ontology to retrieve all parcels.
The query and its results are shown in Fig. 3. As can be seen, the query retrieves
both Akkerweb plots and DairyCampus percelen. In addition, Akkerweb contains data
about a plot with name “L188” and DairyCampus contains data on a perceel with an
identifier “L188”. This means that both databases contain the same parcel and the
properties can be combined.
The specific ontologies for DairyCampus and Akkerweb formed the basis to gen-
erate triples from the relational data sources of DairyCampus and Akkerweb. The
triples have been made available via Virtuoso as well as directly from the D2RQ tool
(www.d2rq.org). A system that is based on the common ontology can take the big
data question to create federated SPARQL queries on the DairyCampus and Akker-
web triple stores using the matched ontologies. As a result, farmers can pose ques-
tions in terms of the concepts in the common ontology instead of the detailed and
specific concepts of the DairyCampus and Akkerweb data sources.
The farmer can use such a system for decision support purposes on various daily
operations, such as which amount of feed to provide to which cow in which period,
when to inseminate a specific cow and how to deal with the transition of a cow to-
wards calving.
4 Future work
The approach that is describe in this paper is currently in an experimental phase.
We have reached a set-up by filling the triple stores for 3 farms with cow-data of 1
month which adds up to a total of 7 million triples. This needs to be upgraded to all
farms with all data from 2014. Thereby, we can test the scalability of our system. In
addition, we need to do more detailed analysis of the matching tools that we used and
the reasons for not adequately solving the simple matching problem that we proposed.
References
1. Otero-Cerdeira, L., Rodriguez-Martinez, F.J., Gomez-Rodriguez, A.: Ontology matching:
A literature review. Journal on Expert Systems with Applications, 949-971 (2015)
2. Ontology matchings tool overview: www.mkbergman.com/1769/50-ontology-mapping-
and-alignment-tools/
3. Hertling, S.: Hertuda results for OAEI 2012. In Ontology Matching 2012 workshop pro-
ceedings, 141-144 (2012)
4. HerTUDA download: www.ke.tu-darmstadt.de/resources/ontology-matching/hertuda
5. AgreementMakerLight website: somer.fc.ul.pt/aml.php
6. LogMap website: www.cs.ox.ac.uk/isg/tools/LogMap/
7. YAM++ website: www.lirmm.fr/yam-plus-plus