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Agriculture as a whole consists of many sectors reliant on each other for proper functioning. The need for cross sector cooperation is especially important between apiary owners and farmers. Indeed, it provides benefits for both: increasing and diversifying access to appropriate flowering plants on one hand and providing means of increasing pollination of plants and in turn yield from fields on the other. On the contrary, lack of information may result in poisoning beehive families operating near regions where specific plant protection products have been used. However, even although IT and IoT solutions are increasingly prevalent in agri-food sectors, there is still a significant barrier in connecting various systems, exchanging data and helping users in facilitating modern solutions. In this paper we present a viable solution to this problem by leveraging DEMETER platform and show it’s usage to enable interoperability between commercial IoT beekeeper solution and emerging national farm management (eDWIN) and advisory tools.

One of the main goals of DataBio was the provision of solutions for big data management enabling, among others, the harmonisation and integration of a large variety of data generated and collected through various applications, services and devices. The DataBio approach to deliver such capabilities was based on the use of Linked Data as a federated layer to provide an integrated view over (initially) disconnected and heterogeneous datasets. The large amount of data sources, ranging from mostly static to highly dynamic, led to the design and implementation of Linked Data Pipelines. The goal of these pipelines is to automate as much as possible the process to transform and publish different input datasets as Linked Data. In this chapter, we describe these pipelines and how they were applied to support different uses cases in the project, including the tools and methods used to implement them.

There is an increasing tension in agriculture between the requirements to assure full safety on the one hand and keep costs under control on the other hand, both with respect to (inter) national strategies. Farmers need to measure and understand the impact of huge amount and variety of data which drive overall quality and yield in their fields. Among others, those are local weather data, Global Navigation System of Systems data, orthophotos and satellite imagery, data on soil specifics etc. A strong need to secure Big Data arises due to various repositories and heterogeneous sources. Data storage and visualisation requirements are in some cases competing as they are a common interest as well as a threat that helps one part of a value chain to gain a higher profit. As demonstrated in this paper, handling (Big) data is therefore a sensitive topic, where trust of producers on data security is essential.

A research object is a single information unit encapsulating all the knowledge relevant to a particular scientific investigation, their associated metadata and the context where such resources were produced and came into play. Aimed at enhancing the preservation, reuse and scholarly communication of data-intensive science, research objects are both technical and social artifacts that represent a partnership between scientific communities and the computational support required in nowadays science. In this paper, we explore such partnership, identifying the lack of appropriate machine-readable metadata as one of its main inhibitors, and address the semantic enrichment of research objects as one key aspect towards its establishment. Focused on the specific needs of Earth Science communities, we propose extensions to research object representation models and present novel methods and tools to enrich research object metadata through automatic means. Finally, we validate the approach through the implementation of a recommender system that exploits the resulting metadata to facilitate research object discovery and reuse, enabling humans and machines to work together and accelerate the research life cycle.

FOODIE project aims at building an open and interoperable agricultural specialized platform on the cloud for the management, discovery and large-scale integration of data relevant for farming production. In particular, the integration focuses on existing open datasets as well as their publication in Linked data format in order to maximize their reusability and enable the exploitation of the extra knowledge derived from the generated links. Based on such data, for instance, FOODIE platform aims at providing high-value applications and services supporting the planning and decision-making processes of different stakeholders related to the agricultural domain. The keystone for data integration is FOODIE data model, which has been defined by reusing and extending current standards and best practices, including data specifications from the INSPIRE directive which are in turn based on the ISO/OGC standards for geographical information. However, as these data specifications are available as XML documents, the first step to publish Linked Data required transforming or lifting FOODIE data model into semantic format. In this paper, we describe this process, which was conducted semi-automatically by reusing existing tools, and adhering to the mapping rules for transforming geographic information UML models to OWL ontologies defined by the ISO 19150-2 standard. We describe the challenges associated to this transformation, and finally, we describe the generated ontology, providing an INSPIRE-based vocabulary for the publication of Agricultural Linked Data.

This paper describes our methodological and technological approach for collaborative ontology development in inter-organizational settings. It is based on the formalization of the collaborative ontology development process by means of an explicit editorial workflow, which coordinates proposals for changes among ontology editors in a flexible manner. This approach is supported by new models, methods and strategies for ontology change management in distributed environments: we propose a new form of ontology change representation, organized in layers so as to provide as much independence as possible from the underlying ontology languages, together with methods and strategies for their manipulation, version management, capture, storage and maintenance, some of which are based on existing proposals in the state of the art. Moreover, we propose a set of change propagation strategies that allow keeping distributed copies of the same ontology synchronized. Finally, we illustrate and evaluate our approach with a test case in the fishery domain from the United Nations Food and Agriculture Organisation (FAO). The preliminary results obtained from our evaluation suggest positive indication on the practical value and usability of the work here presented.

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

New digital artifacts are emerging in data-intensive science. For example, scientific workflows are executable descriptions of scientific procedures that define the sequence of computational steps in an automated data analysis, supporting reproducible research and the sharing and replication of best-practice and know-how through reuse. Workflows are specified at design time and interpreted through their execution in a variety of situations, environments, and domains. Hence it is essential to preserve both their static and dynamic aspects, along with the research context in which they are used. To achieve this, we propose the use of multidimensional digital objects (Research Objects) that aggregate the resources used and/or produced in scientific investigations, including workflow models, provenance of their executions, and links to the relevant associated resources, along with the provision of technological support for their preservation and efficient retrieval and reuse. In this direction, we specified a software architecture for the design and implementation of a Research Object preservation system, and realized this architecture with a set of services and clients, drawing together practices in digital libraries, preservation systems, workflow management, social networking and Semantic Web technologies. In this paper, we describe the backbone system of this realization, a digital library system built on top of dLibra.

Interoperability on multiple levels, concerning both the ontologies themselves and their engineering activities, is a key requirement for ontology networks to be efficient, with minimal redundancy and high reuse. This requirement has a strict binding for software tools that can support some interoperability levels, yet they can be hindered by a lack of shared models and vocabularies describing the resources to be handled, as well as the ways of handling them. Here, three examples of metalevel vocabularies are proposed, each covering at least one peculiar interoperability aspect: OMV for modeling the artifacts themselves, LIR for managing a multilingual layer on top of them, and C-ODO Light for modeling collaboration-supportive life cycle management tasks and processes. All of these models lend themselves to handling by dedicated software tools and are all being employed within NeOn products.

The widespread use of ontologies in the last years has raised new challenges for their development and maintenance. Ontology development has transformed from a process normally performed by one ontology engineer into a process performed collaboratively by a team of ontology engineers, who may be geographically distributed and play different roles. For example, editors may propose changes, while authoritative users approve or reject them following a well defined process. This process, however, has only been partially addressed by existing ontology development methods, methodologies, and tool support. Furthermore, in a distributed environment where ontology editors may be working on local copies of the same ontology, strategies should be in place to ensure that changes in one copy are reflected in all of them. In this paper, we propose a workflow-based model for the collaborative development of ontologies in distributed environments and describe the components required to support them. We illustrate our model with a test case in the fishery domain from the United Nations Food and Agriculture Organisation (FAO).

In this paper, we present Oyster, a Peer-to-Peer system for exchanging ontology metadata among communities in the Semantic Web. Oyster exploits semantic web techniques in data representation, query formulation and query result presentation to provide an online solution for sharing ontologies, thus assisting researchers in re-using existing ontologies.

The agriculture sector is a unique sector due to its strategic importance for both citizens (consumers) and economy (regional and global), which ideally should make the whole sector a network of interacting organizations. Rural areas are of particular importance with respect to the agro-food sector and should be specifically addressed within this scope. The FOODIE project aims at building an open and interoperable agricultural specialized platform hub on the cloud for the management of spatial and non-spatial data relevant for farming production; for discovery of spatial and non-spatial agriculture related data from heterogeneous sources; integration of existing and valuable European open datasets related to agriculture; data publication and data linking of external agriculture data sources contributed by different public and private stakeholders allowing to provide specific and high-value applications and services for the support in the planning and decision-making processes of different stakeholders groups related to the agricultural and environmental domains.