The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to (...) existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource providing details on the people, policies, and issues being addressed in association with OBI. (shrink)

Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from data sets (...) them. However, such ‘minimum information’ MI checklists are usually developed independently by groups working within representatives of particular biologically- or technologically-delineated domains. Consequently, an overview of the full range of checklists can be difficult to establish without intensive searching, and even tracking thetheir individual evolution of single checklists may be a non-trivial exercise. Checklists are also inevitably partially redundant when measured one against another, and where they overlap is far from straightforward. Furthermore, conflicts in scope and arbitrary decisions on wording and sub-structuring make integration difficult. This presents inhibit their use in combination. Overall, these issues present significant difficulties for the users of checklists, especially those in areas such as systems biology, who routinely combine information from multiple biological domains and technology platforms. To address all of the above, we present MIBBI (Minimum Information for Biological and Biomedical Investigations); a web-based communal resource for such checklists, designed to act as a ‘one-stop shop’ for those exploring the range of extant checklist projects, and to foster collaborative, integrative development and ultimately promote gradual integration of checklists. (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)