Enabling Semantic Web for Precision Agriculture: a Showcase of the Project agriOpenLink Dana Tomic Domagoj Drenjanac Wilifred Wöber FTW FTW Universität für Bodenkultur Donau-City Strasse 1 Donau-City Strasse 1 Gregor-Mendel-Strasse 33 Vienna, Austria Vienna, Austria Vienna, Austria tomic@ieee.org drenjanac@ftw.at wilfried.woeber@boku.ac.at Sandra Hörmann Wolfgang Auer Josephinum Research MKW electronics GmbH Rottenhauser Straße 1 Jutogasse 3 Wieselburg, Austria Weibern, Austria sandra.hoermann@josephinum.at wolfgang.auer@mkwe.com ABSTRACT This paper describes the agriOpenLink approach towards 1. INTRODUCTION triplification of the production data generated by heterogeneous 1.1 Precision Farming agricultural equipment, for their easy integration and querying Precision agriculture stands for management practices and tools within a common information context of various decision support that leverage advanced sensor, actuator and decision support applications. The presented approach has been developed within technology to aid in optimization of the production processes the running project agriOpenLink, which aims at improving [1][2]. agricultural production processes. Triplification is performed by equipment-specific model adapters (plugins), which translate the The market for the precision agriculture equipment, both for the equipment data into RDF triples. The plugins are realized by arable farming and the livestock farming is rapidly growing, and means of semantic REST services, and triplification is a result of a the robotic and sensor systems are already indispensable at larger workflow orchestrated "on-demand" by chaining services of farms. These are expected to be ubiquitously used in the years to appropriate data sources. In the project, we have also come. Today, modern agricultural equipment such as conceptualized, and are currently implementing a platform and autonomously driving tractors with their smart implements, tools that facilitate easy plugin development and deployment. milking and feeding robots or animal monitoring systems, Dealing with the problem of creating a common information aggregate many different types of sensors and collect many context, the project designed a domain ontology taking into different sensor data. In many cases, these data are stored in local account existing work, experimented with triplification of existing databases, e.g., imbedded within robots or accompanying domain knowledge, and proposed an approach for the ontology controllers (PCs) that provide user applications. The raw sensor maintenance. The agriOpenLink platform is being tested and data are often processed locally so as to provide users with a demonstrated in the precision irrigation use case and the precision meaningful aggregated information for the decision support. Such dairy farming use case. interpreted data is often presented to users in a tabular of graphical form. Cloud-based solutions are being increasingly offered, however the issues of data ownership or reuse by 3rd Categories and Subject Descriptors parties are still subject of many controversies hampering the take- H.4 [Information Systems Applications]: Miscellaneous off of these solutions. Nevertheless, the equipment is just one source of the production General Terms data. The farmers need to interact with a multitude of other Design, Experimentation external information systems with the weather data, soil, weed, crops, pest, animal breeds, feed, and other information. Although Keywords the integration of data from different sources is a paramount enabler for the decision support applications and for the Triplification, ontology, semantic services production process optimization, it still remains a grand challenge. Today it is often a farmer who has to collect data from different sources e.g., in form of comma separated value (CSV) files, and link them, and process them together. This is particularly noticeable in the domain of precision livestock farming where proprietary data models are often used, partially because of a low acceptance of existing standards (e.g., ISOagriNET framework, www.isoagrinet.org) and their often criticized inflexibility. As an answer to strong user needs, 26 emerging data integration solutions [3] typically also fall back to promises of the Semantic Web, the project agriOpenLink1 using proprietary data models, without attempting to solve the developed a semantic approach with a goal to establish a common problem of data model interoperability. These approaches focus context for the integration of heterogeneous interfaces and data, more on providing a single integrated user interface for the and to develop tools to facilitate in better decision making and farmers, and less on establishing open interfaces for any new process optimization. This approach integrates three tasks: 1) system that can be meaningfully connected. Therefore they often establishing a platform in which a common ontology-based model lead to non-scalable solutions sensitive to changes in data formats can be created and maintained to reflect the common data that are used on the interfaces of heterogeneous integrated integration possibilities and needs, 2) designing tools to ease the sources. development of the model adapters – the equipment-specific or data-source-specific triplifiers – that translates process data into 1.2 Semantic Web for Precision Farming RDF triples based on the concepts of the common ontology, and The goal of Semantic Web is to use Web as an information 3) designing a run time environment in which data translation management platform [4]. The standards of the Semantic Web (tripligication) is performed on demand as a result of execution of enable unified modelling of data and metadata, so that they could queries. As these RDF data relate to the common ontology (the be incrementally integrated within transparent knowledge knowledge context) they can be easily integrated and queried structures within which concepts can be disambiguated and together. interrelated, and data easily linked to their defining schemas. The While agreeing on a common model (a defacto standard) is a standards, tools and practices of Semantic Web have reached prominent requirements in the agricultural sector, it is not considerable maturity, and are used for the management of both possible to aspire model completeness. Therefore, the model open data (freely accessible) and enterprise data (accessible to extension or change has to be technologically easy to implement registered users) in many different sectors and applications [5][6]. and propagate. This requirement makes semantic technology the These tools support the whole lifecycle of creating and technology of choice for creating and maintaining a standard maintaining linked data, which includes 1) extraction of model. Ontology-based domain knowledge is easy to extend and structured data from other sources, 2) data authoring or creation interlink with already established concepts. That is why via “triplification”, enrichment, interlinking and fusing and 3) agriOpenLink focus on tools for the ontology-based lifecycle data maintenance in a repository. As a result of the Linked Data management of the schema and data. Accordingly adding a new community effort the Open Linked Data Cloud today include data source or changing formats does not result in a detrimental huge number of data sets (ontologies, vocabularies, … and real complexity or a high implementation cost. data) with geographic information, publications, user generated content, government, life sciences, and cross-domain. The The agriOpenLink approach also aims to account for specificities enterprise systems can re-use established semantic descriptions of data sources in agricultural production environment and special and uniquely defined resources and benefit from the linked data requirements on structuring them. While the applicability of the concepts and the shared semantics. relational database schema translation approaches is high for the owners of large databases, this does not apply for the equipment The languages and tools for translation of data from different as a source of production data, because data stored within the formats into RDF are important components of existing Semantic equipment database first needs to be processed (e.g., averaged Web tool chains. Several approaches have already been proposed, within a specific interval, etc.) to be useful for sharing. Therefore, demonstrated and offered as standards. For the translation of agriOpenLink uses plugin approach and application specific relational databases the W3C defined R2RML [7]. The RML translation of data into RDF by means of semantic REST services. approach [8] defines a language that extends the R2RML standard particularly addressing concurrent translation of many related sources. These approaches are very well suited to be used by 2.2 The Realization owners of large databases that have already accumulated huge 2.2.1 Plugins as Triplifiers amounts of data. The agriOpenLink platform offers tools and procedures to Within the agricultural domain the Semantic Web approach translate the agricultural data from the precision agriculture already inspired a number of solutions in the research spectrum, equipment or other agricultural information sources into their e.g., [9-13] and is also embraced by the Food and Agriculture semantic RDF-based form, in which they can be interlinked and Organization of the United Nations (FAO; http://aims.fao.org) in jointly queried. We adopted a plugin-based approach where each their global initiatives for agricultural information management source of a relevant information is wrapped within a so called systems (AIMS), AGROVOC vocabulary, and agricultural plugin acting as a model adapter - a triplefier. The triplification is ontology service [14]. Also in the agricultural domain there are realized as service-based business logic. More specifically a many different sources of information, and actors who currently plugin is a software component which publishes semantic REST maintain these data for the farmers and are opening them via services, similar to the SADI Web Services [17] that consume and APIs, e.g. as described in [15], or can be found on [16]. produce RDF triples. A plugin acts as a model mediator – it translates data from the internal model of the device into instances of particular class of the ontology decorated with specific 2. THE AGRIOPENLINK APPROACH properties. Accordingly its function is to open and provide for 2.1 The Motivation further linking RDF data from the devices. An important Motivated with the shortcomings of the existing data integration component of our plugin approach is the development approaches in commercial precision farming solutions and the 1 www.agriopenlink.com 27 environment which offers plugin skeleton code and basic We experimented with the ways of encoding the expert functionality to aid in the plugin implementation. knowledge in form of defined classes, SPARQL queries and semantic services. To this aim the Ontology Editor offers a user The decision to create a specific plugin platform and not use interface to create defined classes with restrictions on properties existing Internet of things (IOT) frameworks such as [18], and to as a basis for classification. An example is a class that restricts the encapsulate translation of the data into RDF within semantic activity property to define an animal which may be potentially services is based on specific requirements and current constraints lame. The Query Editor offers a user interface for the creation of existing within the agriculture sector. First of all, we aimed at a SPARQL queries. We are currently implementing a solution with compact solution with a minimum set of functionality. The goal is which any query can be also translated into 1) a dynamically to bring the benefits of the semantics and ontology to the involved deployed service (which controls invocation of other services) and actors (equipment vendors/ 3rd party application providers), but 2) an API which can be programmatically included in any 3 rd hide as much as possible the complexity of the semantic party application that wants to start such query. representation. To this aim the ontology is maintained via the platform, and the plugin development environment automates 2.2.2 The Domain Ontology some of the complexity of semantic data processing and semantic Particularly the creation of the domain ontology for the dairy REST service creation, for the plugin developers. Accordingly, farming scenario was challenging, due to not much of existing agriOpenLink aims at scalable integration of any data interface work in this domain. In the process of DFO engineering for the and data format within a common information and knowledge domains of milk quality, feeding, breeding, fertility, we analyzed context in which data can easily be related to and combined with both the ISOagriNET standard dictionaries [16] and the schemas other data. of the milking robots, concentrate feeder, and heat and activity While many agricultural equipment use relational databases to monitoring equipment. We created the first version of the organize its internal data, our triplification approach is not based ontology in the OWL ontology editor Protégé. It addresses the on the approaches and tools for data model translation such as scenario in which a linked data set is created for a herd at the farm [7][8]. The reasons for this are twofold. Firstly, the raw data as a basis for herd management applications. The herd linked data generated by the equipment is very often quite sensitive and must set includes the farm core data, i.e., the animal registration first be processed into interpreted data that can be offered to information, and is enriched with the data continuously coming farmers. The raw data is often considered as being owned by the from different farm equipment and external sources, including the equipment vendors. Accordingly, the amount of interpreted milk quality, feed, activity, fertility, and health information. We information is lower as compared to the raw data. The information started with a small set of OWL classes including: Animal, Farm, offered to the farmer is sometimes not in the database, but is Farmer, Equipment, Organization and the object properties to calculated and only presented on the user interface. Secondly, the reflect the relationships between instances of these classes such as engineers implementing the internal logic of this equipment, and parent-child relationship between Animals, similar to the accordingly the plugins, do not have to learn semantic-heavy tools ontologies [11][21]. While these existing ontologies provided an but can program the business logic of translation completing the important input, only the latter one has been recently published. plugin skeleton. During the ontology creation it became clear that standard The project agriOpenLink is demonstrating the use of the platform ISOagriNET dictionaries need to be available in the linked data in two scenarios: (1) in the precision irrigation scenario we focus format in order to reference them in a formal way. Consequently, on triplifying weather, soil and sensor data and expressing the we defined their “triplification” as a goal for the 2nd phase of our transpiration model knowledge in form of semantic services [19]. DFO engineering task. In finalizing the 1st phase we revisited the In the precision dairy farming scenario we triplify data from the requirements of our initial herd linked data scenario and re- farm robots and farm systems and experiment with expert focused the DFO engineering on the animal state diagnoses knowledge for animal state diagnosis expressed as SPARQL scenario, in which WC3 SPARQL queries coming from Decision queries [20]. Figure 1 illustrates the platform architecture in a Support Systems trigger search, access, interlinking and filtering precision dairy farming. A plugin server that implements HTTP of semantic data available via Plugin Services. We reduced our REST interfaces is a central component of the local system that DFO to the properties that belong to a core data set, and that can initiates and run different plugins on the farm. The agriOpenLink be extracted from equipment and external sources. DFO further platform also includes a plugin development environment in includes defined classes that are used by Plugin Services. They which plugins can be designed and built into dynamically specify what properties can be extracted from each specific loadable components. Plugin, i.e., specific type of the equipment. 2.2.3 Translating ISOagriNET Data Dictionaries To obtain an RDF/OWL model of the standard dictionaries for livestock farming we developed a triplifyer tool that follows the best practices for creating the linked data [6]. Our triplifier tool can also transform standard exchange data files into linked data. The triplification of the standard schema was described in [22]. To test the procedure we triplified all dictionaries available via http://ian.lkv-nrw.de as well as exemplary data files. The translated dictionary for the year 2003 is available at www.agriopenlink.com/ADR2003. We are currently working towards completing and opening the ontology platform with ontology browsing and editing GUI and a SPARQL interface. Figure 1. The agriOpenLink Platform (dairy farming 28 scenario) . 3. CONCLUSIONS [8] Dimou, A., Vander Sande, M, Colpaert, P, Verborgh, R, The presented agriOpenLink platform is a work-in-progress Mannens, E. and R. 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