: Background: In responsible research and innovation (RRI), innovation is seen as a way in which humankind finds solutions for societal issues. However, studies on commercial innovation show that firms respond in a different manner and at a different speed to the same societal issue. This study investigates what role organizational motives play in the product innovation processes of firms when aiming for socially responsible outcomes. Methods: This multiple-case study investigates the motives of food firms for healthier product innovation by (...) interviewing firms about the organizational motives behind product reformulation and innovation. Results: This study highlights the importance of having both instrumental and moral motives in the innovation process when aiming for socially responsible outcomes, and how both these motives interact and contribute to responsible innovation in industry. Furthermore, the study results question the nature of relational motives as a separate category from the other two categories of motives, as suggested by corporate social responsibility (CSR) scholars. Conclusions: If commercial innovation needs to contribute to solutions for societal issues, the importance of moral motives has to be stressed without annihilating the instrumental objectives of firms. Both motives contribute to the success factors of responsible product innovation in industry. (shrink)

BACKGROUND -/- The importance of ontologies in the biomedical domain is generally recognized. However, their quality is often too poor for large-scale use in critical applications, at least partially due to insufficient training of ontology developers. -/- OBJECTIVE -/- To show the efficacy of guideline-based ontology development training on the performance of ontology developers. The hypothesis was that students who received training on top-level ontologies and design patterns perform better than those who only received training in the basic principles of (...) formal ontology engineering. -/- METHODS -/- A curriculum was implemented based on a guideline for ontology design. A randomized controlled trial on the efficacy of this curriculum was performed with 24 students from bioinformatics and related fields. After joint training on the fundamentals of ontology development the students were randomly allocated to two groups. During the intervention, each group received training on different topics in ontology development. In the assessment phase, all students were asked to solve modeling problems on topics taught differentially in the intervention phase. Primary outcome was the similarity of the students’ ontology artefacts compared with gold standard ontologies developed by the authors before the experiment; secondary outcome was the intra-group similarity of group members’ ontologies. -/- RESULTS -/- The experiment showed no significant effect of the guideline-based training on the performance of ontology developers (a) the ontologies developed after specific training were only slightly but not significantly closer to the gold standard ontologies than the ontologies developed without prior specific training; (b) although significant differences for certain ontologies were detected, the intra-group similarity was not consistently influenced in one direction by the differential training. -/- CONCLUSION -/- Methodologically limited, this study cannot be interpreted as a general failure of a guideline-based approach to ontology development. Further research is needed to increase insight into whether specific development guidelines and practices in ontology design are effective. (shrink)

In order to strengthen RI in the private sector, it is imperative to understand how companies organise this process, where it takes place, and what considerations and motivations are central in the innovation process. In this chapter, the questions of whether and where normative considerations play a role in the innovation process, and whether dimensions of RI are present in the innovation process, are addressed. In order answer these research questions, a theoretical framework is developed based on Jones’s theory of (...) ethical decision making and Cooper’s stagegate model of innovation management. In order to answer the research questions, a specific case of innovations that contribute to public health is explored, namely, that of food companies that participate in a Front-of-Pack logo for healthier food. As the use of healthy food logos does not necessarily have a positive impact on sales and profits, it is expected that in the decision-making process, as part of their innovation process, companies make several trade-offs between economic, technical and moral factors. As the social-ethical values at stake in corporate innovation processes have remained to a large extent unexplored in research on innovation management, the aim of this chapter is to identify the motivations and barriers for companies embracing and continuing a FoP logo for healthier food, and to assess whether ethical considerations play a role in this innovation process. From the findings in this research, it will become clear that although the studied companies participated in a programme for healthy food and thus are responsive to the needs of society, and although the companies feel responsible for public health, ethical considerations do not play a central role in the operational innovation process. Instead, technical and economic considerations seem to prevail in the operational innovation process. Furthermore, none of the procedural dimensions of RI seems to be present at this level in the innovation trajectory. It is argued that this may be an indication that the ethical decision-making process for RI is not located at the level of the operational innovation process itself, but is something that might be located on a higher strategic level in the company. It is at this level that the moral decision is taken to adopt the FoP logo and to engage in the RI process. The findings cast a new light on the discourse on RI in general, and in the private sector in particular. (shrink)

The construction of complex ontologies can be facilitated by adapting existing vocabularies. There is little clarity and in fact little consensus as to what modifications of vocabularies are necessary in order to re-engineer them into ontologies. In this paper we present a method that provides clear steps to follow when re-engineering a thesaurus. The method makes use of top-level ontologies and was derived from the structural differences between thesauri and ontologies as well as from best practices in modeling, some of (...) which have been advocated in the biomedical domain. We illustrate each step of our method with examples from a re-engineering case study about agricultural fertilizers based on the AGROVOC thesaurus. Our method makes clear that re-engineering thesauri requires far more than just a syntactic conversion into a formal language or other easily automatable steps. The method can not only be used for re-engineering thesauri, but does also summarize steps for building ontologies in general, and can hence be adapted for the re-engineering of other types of vocabularies or terminologies. (shrink)

BACKGROUND -/- The need for detailed description and modeling of cells drives the continuous generation of large and diverse datasets. Unfortunately, there exists no systematic and comprehensive way to organize these datasets and their information. CELDA (Cell: Expression, Localization, Development, Anatomy) is a novel ontology for the association of primary experimental data and derived knowledge to various types of cells of organisms. -/- RESULTS -/- CELDA is a structure that can help to categorize cell types based on species, anatomical localization, (...) subcellular structures, developmental stages and origin. It targets cells in vitro as well as in vivo. Instead of developing a novel ontology from scratch, we carefully designed CELDA in such a way that existing ontologies were integrated as much as possible, and only minimal extensions were performed to cover those classes and areas not present in any existing model. Currently, ten existing ontologies and models are linked to CELDA through the top-level ontology BioTop. Together with 15.439 newly created classes, CELDA contains more than 196.000 classes and 233.670 relationship axioms. CELDA is primarily used as a representational framework for modeling, analyzing and comparing cells within and across species in CellFinder, a web based data repository on cells (http://cellfinder.org). -/- CONCLUSIONS -/- CELDA can semantically link diverse types of information about cell types. It has been integrated within the research platform CellFinder, where it exemplarily relates cell types from liver and kidney during development on the one hand and anatomical locations in humans on the other, integrating information on all spatial and temporal stages. CELDA is available from the CellFinder website: http://cellfinder.org/about/ontology. (shrink)

Background -/- Ontology Design Patterns (ODPs) are representational artifacts devised to offer solutions for recurring ontology design problems. They promise to enhance the ontology building process in terms of flexibility, re-usability and expansion, and to make the result of ontology engineering more predictable. In this paper, we analyze ODP repositories and investigate their relation with upper-level ontologies. In particular, we compare the BioTop upper ontology to the Action ODP from the NeOn an ODP repository. In view of the differences in (...) the respective approaches, we investigate whether the Action ODP can be embedded into BioTop. We demonstrate that this requires re-interpreting the meaning of classes of the NeOn Action ODP in the light of the precepts of realist ontologies. -/- Results -/- As a result, the re-design required clarifying the ontological commitment of the ODP classes by assigning them to top-level categories. Thus, ambiguous definitions are avoided. Classes of real entities are clearly distinguished from classes of information artifacts. The proposed approach avoids the commitment to the existence of unclear future entities which underlies the NeOn Action ODP. Our re-design is parsimonious in the sense that existing BioTop content proved to be largely sufficient to define the different types of actions and plans. -/- Conclusions -/- The proposed model demonstrates that an expressive upper-level ontology provides enough resources and expressivity to represent even complex ODPs, here shown with the different flavors of Action as proposed in the NeOn ODP. The advantage of ODP inclusion into a top-level ontology is the given predetermined dependency of each class, an existing backbone structure and well-defined relations. Our comparison shows that the use of some ODPs is more likely to cause problems for ontology developers, rather than to guide them. Besides the structural properties, the explanation of classification results were particularly hard to grasp for 'self-sufficient' ODPs as compared with implemented and 'embedded' upper-level structures which, for example in the case of BioTop, offer a detailed description of classes and relations in an axiomatic network. This ensures unambiguous interpretation and provides more concise constraints to leverage on in the ontology engineering process. (shrink)

Background: In order to improve ontology quality, tool- and language-related tutorials are not sufficient. Care must be taken to provide optimized curricula for teaching the representational language in the context of a semantically rich upper level ontology. The constraints provided by rigid top and upper level models assure that the ontologies built are not only logically consistent but also adequately represent the domain of discourse and align to explicitly outlined ontological principles. Finally such a curriculum must take into account the (...) pre-existing skills and knowledge of the target audience. -/- Objective: To develop a well-structured curriculum aligned to the particular requirements of life science professionals, in order to enable them to create logically sound, domain adequate and predicable ontologies using the Web Ontology Language (OWL) in Protégè. -/- Methods: Content selection for the curriculum was based on the literature, pre-existing tutorials, and a guideline for good ontology development (i.e ontology design enhancing domain adequacy, sustainability and interoperability) that drew on the authors previous experiences with large ontology development projects. Learning objectives were formulated according to a needs assessment of the targeted learners, who were students trained in life sciences with basic knowledge and practical skills in computer science. As instructional format we choose an approach with a high amount of practical exercises. The curriculum was first implemented with 24 Students and 7 lecturers/ tutors over 5 full days. The curriculum was evaluated by gathering the participants feedback via a questionnaire. -/- Results: Curricular development produced 16 modules of approximately 2 hours each, which covered basic principles of Applied Ontology, description logic syntax and semantics, as well as best design practices outlined in ontology design patterns and variants of the BioTop upper ontology. An opinion survey based on questionnaires indicated that the participants took advantage from the teaching strategies applied, as they indicated good knowledge gain and acknowledged the relevance of the modules. The difficulty was rated slightly lower. -/- Conclusion: The development of teaching material for principled ontology design and best practices is of crucial importance in order to enhance the quality of biomedical ontologies. Here, we present a curriculum for a week long workshop, leveraging on current educational principles, focusing on interactive hands-on exercises, group interactions, and problem-oriented learning. Whereas evaluation clearly showed the success of this approach, in particular regarding student’s satisfaction, the objective measurement of traceable effects on the quality of the generated ontology, although of much higher interest, has just started. (shrink)

Ontology engineering is mainly done by domain experts who are specialists in their domain but have, if at all, limited knowledge in logics, computer science, or analytic philosophy. The literature on formal ontologies and biomedical ontologies is neither suited nor intended to serve as an educational resource that would help domain experts to become good ontologists. Existing educational resources focus rather on ontology tools and languages than on good practice. The purpose of the GoodOD guideline is to pave the road (...) for domain experts who want (or need) to become ontology engineers. It pursues the openly pragmatic approach that ontology engineers only need to know ontology theory to the extent which supports the (measurable) outcome of their work. The GoodOD Guideline builds upon given philosophical and methodological principles as well as preexisting tools and resources. In particular (pragmatic) scientific realism, first order logics and its subset OWL-DL (description logics), set theory, an upper-level ontology with high-level categories and a canonic set of relations, constraints which limit the freedom of choice when building coordinate expressions, the ontology editor Protégé 4, a complete DL reasoner, such as HermIT, ontology design patterns, and naming conventions. -/- Given these precepts the guideline contains all the knowledge to be acquired by a user to be able to solve ontology engineering problems in a foreseeable and non-arbitrary way. The didactic concept is to convey the skills not only by theory but also by practical examples. (shrink)

OBJECTIVE: (a) To measure the effect of a guideline-based training on the performance of ontology developers compared with the performance after unspecific training by a competency question based evaluation; and (b) to provide empirical evidence for the applicability of competency questions in formal ontology evaluation in general. BACKGROUND: A close connection between ontology development and ontology evaluation as quality management procedure can been attained with the use of competency questions. Competency questions are often used as a semi-formal specification of requirements (...) for an ontology under development. Hence they can also be used as evaluation instruments, in order to check how far an ontology fulfills these requirements. METHODS: A randomized controlled trial was conducted with two groups of 12 students each. The intervention consisted in a differential guideline-based training on ontology development vs. unspecific training. After a group-specific training focusing on three topics per group, performance of students was assessed with 12 exercises (2 exercises per topic), in which the students had to apply their skills. Different types of competency questions were elaborated for the analysis of the students' ontologies. We used the proportion of correct answers as a measure of ontology quality. RESULTS: On single topic level, the performance of ontology developers increased after guideline-based training for two out of the six topics: it increased from a proportion of 0.46 to 0.63 for the topic Process \& Participation and from 0.44 to 0.53 for the topic Collective Material Entity. In regression analysis, a positive correlation was shown between the performance of students on untrained topics and the performance after specific guideline-based training. Moreover, in multiple regression analysis an overall effect of specific training of 0.09 was calculated (p < 0.1). CONCLUSION: The results show an effect of a specific guideline-based training on the performance of ontology developers compared to the performance after unspecific training by an increase of about 10 \% on the rate of correct competency questions. In addition, this study has shown the general applicability of competency questions in a formal ontology evaluation scenario. However, the study also shows that the training of ontology developers and their performance evaluation is a tedious task. The resulting performance of ontology developers is more dependent on the a priori individual competencies than on the specific acquired skills after training. (shrink)

While genomic and proteomic information describe the overall cellular machinery available to an organism, the metabolic profile of an individual at a given time provides a canvas as to the current physiological state. Concentration levels of relevant metabolites vary under different conditions, in particular, in the presence or absence of different disorders. Metabolite concentrations thus mediate an important link between chemistry and biology, contributing to a systems-wide understanding of biological processes and pathways. However, there are a number of challenges in (...) the ontological representation of such information. Firstly, concentration information is numeric and ranges over continuous values, while ontologies consist of discrete classes. Secondly, ontologies usually model only what is certain, and their logical formalisms are adapted to reasoning from certain axioms to logical deductions, however, the link between chemicals and diseases via concentration levels, like many threshold phenomena, is both uncertain and vague. In this paper we evaluate the representation of this knowledge using a combination of concrete domains and probabilistic reasoning. We parse concentration values from HMDB and create an ontology able to distinguish normal from abnormal concentrations and able to evaluate a probabilistic risk category for the presence of an associated disorder. (shrink)

Ontologies aim to represent what is general, by means of universal statements. In contrast, dispositional predications capture knowledge about what is likely to happen if a certain set of circumstances obtain, which is crucial in investigative research such as in drug discovery and systems biology, where entities which are constitutionally dissimilar can nevertheless have similar behavior in a biological context. While such dispositional properties are increasingly included in biomedical ontologies, the circumstances under which the dispositions are realized are seldom explicitly (...) modeled, and doing so is problematic due to the necessary restriction to binary relations in OWL ontologies. In this paper we address this shortcoming, focusing on the bioactivity of small molecules at varying levels of concentration within a living organism as our problem domain, although our approach is generalizable to other problems. We discuss the ontological nature and representation of dispositions and their realization; consider the nature of concentrations and their representation; and finally we detail an approach to linking dispositions to the conditions for their realization which regards conditions as triggers for the process in which the disposition is realized. (shrink)

Ontological realism is a philosophical stance that provides a definitional framework for ontologies and is referred to by various applied ontologists. From a computer science perspective, ontologies are often associated with formal languages for the representation of ontologies like the Web Ontology Language (OWL). It has, however, not been made explicit how the realist framework is related to the representation formalism. We analyse how the representational units of OWL can be used for modelling realist ontologies. While OWL is sufficiently close (...) to the realist framework of ontologies to describe them, there are categories in OWL that are alien to realist ontologies or duplicate categories in realist ontologies. This clarification allows separating talk about ontologies in general from talk about their specific representation. (shrink)

Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted (...) to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems. (shrink)