Jan L. Top

Ontology of units of measure and related concepts

This paper describes the Ontology of units of Measure and related concepts OM, an OWL ontology of the domain of quantities and units of measure. OM supports making quantitative research data more explicit, so that the data can be integrated, verified and reproduced. The various options for modeling the domain are discussed. For example, physical quantities can be modeled either as classes, instances or properties. The design choices made are based on use cases from our own projects and general experience in the field. The use cases have been implemented as tools and web services. OM is compared with QUDT, another active effort for an OWL model in this domain. We note possibilities for integration of these efforts. We also discuss the role OWL plays in our approach.

How semantics can improve engineering processes: A case of units of measure and quantities

Science and engineering heavily depend on the ability to share data and models. The World Wide Web provides even greater opportunity to reuse such information from disparate sources. Moreover, if the information is digitized it can to a large extent be processed automatically. However, information sharing requires the availability of proper formal standards. Ontologies provide such standards. Creating an ontology of units of measure is a crucial first step in unambiguously exchanging and processing quantitative information. The next step is to make this ontology available for software applications. In this paper we evaluate prevailing ontologies of units by comparing them to a semi-formal description of the domain of units of measure. This description was drafted from textual descriptions of standards in the field. An important result of the analysis is that existing ontologies only define subsets of the necessary concepts and relations identified in our reference description. We therefore propose a new ontology, called OM (Ontology of units of Measure and related concepts). The ontology is based on the description and the corresponding parts of the analyzed ontologies. OM defines the complete set of concepts in the domain as distinguished in the textual standards. As a result the ontology can answer a wider range of competency questions than the existing approaches do. Moreover, to make OM available for arbitrary software systems, we have developed a number of web services that offer a standardized interface. Three applications demonstrate the usefulness of OM and its services. First, a web application checks dimension and unit consistency of formulas. Second, an engineering application for agricultural supply chains computes product respiration quantities and measures. Third, a Microsoft Excel add-in assists in data annotation and unit conversion. Preliminary user evaluations indicate that OM and the associated services provide a useful component for software applications in science and engineering.

Organizing data in arable farming : towards an ontology of processing potato

Arable farmers and their suppliers, consultants and procurers are increasingly dealing with gathering and processing of large amounts of data. Data sources are related to mandatory and voluntary registration (certification, tracing and tracking, quality control). Besides data collected for registration purposes, decision support systems for strategic, tactical and operational tasks yield enormous amounts of mainly digital information. Data of similar nature but with often varying definitions are collected and processed separately for different purposes. This paper describes for an important arable crop – the processing potato – which data requirements and flows exist at present and how they could possibly be described in a unifying ontology. An ontology describes the concepts, attributes and relations in a specific knowledge domain using a standardized representation language. Important concepts in this domain are for example crop, parcel, soil, treatment and farm. The ontology – once elaborated – will reduce the overlap between information models and helps to overcome the problem of data definition and representation. It is a key element for the development of systems that can automatically learn either with the help of expert knowledge or through adequate numerical techniques.

From Relational Data to RDFS Models

A vast amount of information resources is stored as relational-like data and inaccessible to RDFS-based systems. We describe FDR2 – an approach to integration of relational-like information resources with RDFS-aware systems. The proposed solution is purely RDFS-based. We use RDF/S as a mechanism to specify and perform linking of relational data to a predefined domain ontology. The approach is transformation-free, this ensures that all the data is accessible and usable in consistence with the original data model.

Onto-SOA: From Ontology-enabled SOA to Service-enabled Ontologies

The significant potential of the combination of ontologies and SOA has been recognized in the field of Semantic Web Services (SWS). The OWL-S and WSMF approaches provide us with ontology-based frameworks for WSDL web services to enable automation of high-level tasks such as discovery, invocation and composition of web services. We also investigate ontologies and SOA but our initial focus is on the software architectural aspects of ontology-enabled services We set up to define a general Ontology-enabled Service- Oriented Architectural style (Onto-SOA). Onto-SOA is independent from an ontology language and a particular web service technology and, therefore, is applicable within any approach that combines ontologies and SOA. With Onto- SOA we discover that the relation between an ontology and a service is bi-directional: not only an ontology brings shared semantics to services, but also services can be integrated into an ontology (language). The latter provides an ontology with a service-enabled mechanism capable of connecting an arbitrary service to an ontology. In order to illustrate the bi-directionality and to validate Onto-SOA, we further specialize it into MoRe - an RDF/S-enabled SOA with elements of REST web services - and then apply MoRe to the unit conversion problem in the e-Science domain.

Supporting knowledge-intensive inspection tasks with application ontologies

One of the major challenges in computer vision is to create automated systems that perform tasks with at least the same competences as human experts. In particular for automated inspection of natural objects this is not easy to achieve. The task is hampered by large in-class variations and complex 3D-morphology of the objects and subtle argumentations of experts. For example, in our horticultural case we deal with quality assessment of young tomato plants, which requires experienced specialists. We submit that automation of such a task employing an explicit model of the objects and their assessment is preferred over a black-box model obtained from modelling input-output relations only. We propose to employ ontologies for representing the geometrical shapes, object parts and quality classes associated with the explicit models. Our main contribution is the description of a method to develop a white-box computer vision application in which the needed expert knowledge is defined by: (i) decomposing the task of the inspection system into subtasks and (ii) identifying the algorithms that execute the subtasks. This method describes the interaction between the task decomposition and the needed task-specific knowledge, and studies the delicate balance between general domain knowledge and task-specific details. As a proof of principle of this methodology, we work through a horticultural case study and argue that the method leads to a robust, well-performing, and extendable computer vision system.

The Potato Ontology: Delimitation of the Domain, Modelling Concepts, and Prospects of Performance

The ever increasing amount of data gathered by more growers in more years offers possibilities to add value. Therefore—for interested parties and stakeholders—a common and controlled vocabulary of the potato domain that describes concepts, attributes, and the relations between them in a formal way using a standardised knowledge representation language is being developed: a potato ontology. The advantage is that all possible stakeholders will be able to understand the data expressed by this ontology and that software applications can process them automatically. It will also allow the application of advanced numerical techniques that may help to uncover previously unknown correlations. This version of the potato ontology aims at the domain of processing potatoes in a setting of mechanised potato production where growers have access to automated decision support systems and exchange data electronically. This paper describes the procedures to establish such an ontology where competency questions formulated by stakeholders and potential users take a central position. The potato ontology formally describes “Concepts” or “Classes”. The three main classes are those used in crop ecology: Crop, Environment and Management. Classes, e.g., biocides are a subclass of agro-chemicals, and in turn have a subclass Fungicides. The ontology also describes the “Properties” of classes, e.g., agrochemicals are produced synthetically in a factory; biocides are used to protect crops and fungicides to control fungi. The ontology also describes the “Attributes” (properties) of the concepts, e.g., all agrochemicals have attributes such as dose and time of application and mode of application. “Restrictions” may be that a particular chemical can only be applied with a certain type of equipment, or its application is restricted to a certain period or dose. The ontology also features “Instances” which are the individual data such as a particular herbicide treatment with values for field, time, dose, active ingredient, trademark, mode of application, which equipment operated by whom. The standardisation language used is the “Ontology Web Language”.

Expert-Based Ontology Construction: A Case-Study in Horticulture

Experts are capable of performing complex tasks in their specific field of expertise. To do this, they use a vast amount of explicit and tacit domain knowledge. For various applications it may be interesting to represent such detailed domain knowledge in a formal way. We show here the process of elicitating expert knowledge and constructing a domain ontology for a case-study in which experts assess the quality of young greenhouse plants. We have interviewed sorting experts from different plant breeders, created individual ontologies, merged these ontologies, added relevant relations from an observers point of view and checked the results in a teach-back session. We draw two main conclusions from this work. The first conclusion is that the tacit part of an experts knowledge is often explicit knowledge for another expert. The resulting merged ontology is richer than the individual ontologies. The second conclusion is that it is essential to involve an objective observer in the creation of the ontology for adding relations to the ontology that are relevant for the final purpose of the ontology, but that are part of the tacit knowledge of the experts

Semantic Support for Quantitative Research Processes

Collaboration in science requires a shared model of underlying workflows and concepts. In addition to leveraging information exchange between scientists, the shared model should enable automated invocation of computational (numerical) methods from a conceptual level. In this way, the model fills the gap between humans interpreting textual information and computers processing the underlying data and mathematical models. To this end, the authors propose an ontology of quantitative research (OQR). The OQR is based on established tenets of the philosophy of science. Scientific quantities expressed in OQR can be used directly as input to computational methods. The authors demonstrate the OQRs quality by applying it to a case of quantitative food research. Finally, they describe an application in Quest, a prototype quantitative e-science tool.

A methodology for constructing the calculation model of scientific spreadsheets

Spreadsheets models are frequently used by scientists to analyze research data. These models are typically described in a paper or a report, which serves as single source of information on the underlying research project. As the calculation workflow in these models is not made explicit, readers are not able to fully understand how the research results are calculated, and trace them back to the underlying spreadsheets. This paper proposes a methodology for semi-automatically deriving the calculation workflow underlying a set of spreadsheets. The starting point of our methodology is the cell dependency graph, representing all spreadsheet cells and connections. We automatically aggregate all cells in the graph that represent instances and duplicates of the same quantities, based on analysis of the formula syntax. Subsequently, we use a set of heuristics, incorporating knowledge on spreadsheet design, computational procedures and domain knowledge, to select those quantities, that are relevant for understanding the calculation workflow. We explain and illustrate our methodology by actually applying it on three sets of spreadsheets from existing research projects in the domains of environmental and life science. Results from these case studies show that our constructed calculation models approximate the ground truth calculation workflows, both in terms of content and size, but are not a perfect match.