Ludger Jansen

Grains, components and mixtures in biomedical ontologies

BackgroundIn biomedical ontologies, mereological relations have always been subject to special interest due to their high relevance in structural descriptions of anatomical entities, cells, and biomolecules. This paper investigates two important subrelations of has_proper_part, viz. the relation has_grain, which relates a collective entity to its multiply occurring uniform parts (e.g., water molecules in a portion of water), and the relation has_component, which relates a compound to its constituents (e.g., molecules to the atoms they consist of).MethodWe distinguish between four kinds of complex entities and characterize them in first order logic. We then discuss whether similar characterizations could be given in description logics, and finally apply the results to mixtures.ResultsAt first sight, collectives and compounds seem to be disjoint categories. Their disjointness, however, relies on agreement about what are uniform entities, and thus on the granularity of description. For instance, the distinction between isomeric subtypes of a molecule can be important in one use case but might be neglected in another one. We demonstrate that, as implemented in the BioTop domain upper level ontology, equivalence or subsumption between different descriptions of same or similar entities cannot be achieved. Using OWL-DL, we propose a new design pattern that avoids primitive subrelations at the expense of more complex descriptions and thus supports the needed inferences.

Molecular interactions: On the ambiguity of ordinary statements in biomedical literature

Statements about the behavior of biochemical entities (e.g., about the interaction between two proteins) abound in the literature on molecular biology and are increasingly becoming the targets of information extraction and text mining techniques. We show that an accurate analysis of the semantics of such statements reveals a number of ambiguities that have to be taken into account in the practice of biomedical ontology engineering: such statements can not only be understood as event reporting statements, but also as ascriptions of dispositions or tendencies that may or may not refer to collectives of interacting molecules or even to collectives of interaction events. Both authors contributed equally to this work.

CELDA – an ontology for the comprehensive representation of cells in complex systems

BackgroundThe 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.ResultsCELDA 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).ConclusionsCELDA 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.

Evaluating the Good Ontology Design Guideline (GoodOD) with the ontology quality requirements and evaluation method and metrics (OQuaRE).

Objective To (1) evaluate the GoodOD guideline for ontology development by applying the OQuaRE evaluation method and metrics to the ontology artefacts that were produced by students in a randomized controlled trial, and (2) informally compare the OQuaRE evaluation method with gold standard and competency questions based evaluation methods, respectively. Background In the last decades many methods for ontology construction and ontology evaluation have been proposed. However, none of them has become a standard and there is no empirical evidence of comparative evaluation of such methods. This paper brings together GoodOD and OQuaRE. GoodOD is a guideline for developing robust ontologies. It was previously evaluated in a randomized controlled trial employing metrics based on gold standard ontologies and competency questions as outcome parameters. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies and has been successfully used for evaluating the quality of ontologies. Methods In this paper, we evaluate the effect of training in ontology construction based on the GoodOD guideline within the OQuaRE quality evaluation framework and compare the results with those obtained for the previous studies based on the same data. Results Our results show a significant effect of the GoodOD training over developed ontologies by topics: (a) a highly significant effect was detected in three topics from the analysis of the ontologies of untrained and trained students; (b) both positive and negative training effects with respect to the gold standard were found for five topics. Conclusion The GoodOD guideline had a significant effect over the quality of the ontologies developed. Our results show that GoodOD ontologies can be effectively evaluated using OQuaRE and that OQuaRE is able to provide additional useful information about the quality of the GoodOD ontologies.

Why functions are not special dispositions: an improved classification of realizables for top-level ontologies

BackgroundThe concept of function is central to both biology and technology, but neither in philosophy nor in formal ontology is there a generally accepted theory of functions. In particular, there is no consensus how to include functions into a top-level ontology or whether to include them at all.MethodsWe first review current conceptions of functions in philosophy and formal ontology and evaluate them against a set of criteria. These evaluation criteria are derived from a synopsis of theoretical and practical requirements that have been suggested for formal accounts of functions. In a second step, we elucidate in particular the relation between functions and dispositions.ResultsWe argue that functions should not be taken as a subtype of dispositions. The strongest reason for this is that any view that identifies functions with certain dispositions cannot account for malfunctioning, which is having a function but lacking the matching disposition. As a result, we suggest a cross-classification of realizables with dispositions supervening on the physical structure of their bearer, whereas both functions and roles also have some external grounding. While bearers can survive the gain, loss and change of roles, functions are rigid properties that are essentially connected to their particular bearers. Therefore, Function should not be regarded as a subtype of Disposition; rather, the classes of functions and dispositions are disjoint siblings of Realizable.

Effects of guideline-based training on the quality of formal ontologies: a randomized controlled trial.

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.

A content-focused method for re-engineering thesauri into semantically adequate ontologies using OWL

The re-engineering of vocabularies into ontologies can save considerable time in the development of ontologies. Current methods that guide the re-engineering of thesauri into ontologies often convert vocabularies syntactically only and ignore the problems that stems from interpreting vocabularies as statements of truth (ontologies). Current reengineering methods also do not make use of the semantic capabilities of formal languages like OWL in order to detect logical mistakes and to improve vocabularies. In this paper, we introduce a content-focused method for building domain-specific ontologies based on a thesaurus, a popular type of vocabulary. The method results in a semantically adequate ontology that does not only contain a semantically rich description of the entities to be modeled, but also enables non-trivial consistency checks and classifications based on automated reasoning, and can be integrated with other ontologies following the same development principles. The identification of membership conditions, the alignment to a top-level ontology and formal relations, and the consistency check and inference using a reasoner are the central steps in our method. We explain the motivation and sub-activities for each of these steps and illustrate their application through a case study in the domain of agricultural fertilizers based on the ACROVOC Thesaurus. Foremost, our method shows that simple syntactic conversions are insufficient to derive an ontology from a thesaurus. Instead, considerable structural changes are required to derive an ontology that corresponds to the reality it represents. Our method relies on a manual development effort and is particularly useful where a highly reliable is-a hierarchy is crucial.

Crisp Islands in Vague Seas: Cases of Determinate Parthood Relations in Biological Objects

Biological entities are embedded in cascades of mereological systems, both on the level of individuals and on the level of classes. But there are, in fact, numerous borderline cases, in which biological parthood is vague or otherwise indeterminate. In this paper, we distinguish between two kinds of criteria for biological parthood: We argue that criteria based on spatial inclusion, function, origin and genetic identity are useful rules of thumb, but do allow for exceptions, even when taken in combination. Hard criteria, which do not admit exceptions, can be found when we refer to linguistic or ontological necessities. We find such necessities in collections, compounds, fiat partitions, and immaterial entities, which allow formulating some general axioms for biological parthood. To be sure, these are islands of crispness in seas of vagueness.

Ontological interpretation of biomedical database content

BackgroundBiological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework.MethodsBy using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs).ResultsIND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements.ConclusionAmbiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models.

Using ontologies to study cell transitions.

BackgroundUnderstanding, modelling and influencing the transition between different states of cells, be it reprogramming of somatic cells to pluripotency or trans-differentiation between cells, is a hot topic in current biomedical and cell-biological research. Nevertheless, the large body of published knowledge in this area is underused, as most results are only represented in natural language, impeding their finding, comparison, aggregation, and usage. Scientific understanding of the complex molecular mechanisms underlying cell transitions could be improved by making essential pieces of knowledge available in a formal (and thus computable) manner.ResultsWe describe the outline of two ontologies for cell phenotypes and for cellular mechanisms which together enable the representation of data curated from the literature or obtained by bioinformatics analyses and thus for building a knowledge base on mechanisms involved in cellular reprogramming. In particular, we discuss how comprehensive ontologies of cell phenotypes and of changes in mechanisms can be designed using the entity-quality (EQ) model.ConclusionsWe show that the principles for building cellular ontologies published in this work allow deeper insights into the relations between the continuants (cell phenotypes) and the occurrents (cell mechanism changes) involved in cellular reprogramming, although implementation remains for future work. Further, our design principles lead to ontologies that allow the meaningful application of similarity searches in the spaces of cell phenotypes and of mechanisms, and, especially, of changes of mechanisms during cellular transitions.