A framework for interoperability between models with hybrid tools.

Complex system development and maintenance face the challenge of dealing with different types of models due to language affordances, preferences, sizes, and so forth that involve interaction between users with different levels of proficiency. Current conceptual data modelling tools do not fully support these modes of working. It requires that the interaction between multiple models in multiple languages is clearly specified to ensure they keep their intended semantics, which is lacking in extant tools. The key objective is to devise a mechanism to support semantic interoperability in hybrid tools for multi-modal modelling in a plurality of paradigms, all within one system. We propose FaCIL, a framework for such hybrid modelling tools. We design and realise the framework FaCIL, which maps UML, ER and ORM2 into a common metamodel with rules that provide the central point for management among the models and that links to the formalisation and logic-based automated reasoning. FaCIL supports the ability to represent models in different formats while preserving their semantics, and several editing workflows are supported within the framework. It has a clear separation of concerns for typical conceptual modelling activities in an interoperable and extensible way. FaCIL structures and facilitates the interaction between visual and textual conceptual models, their formal specifications, and abstractions as well as tracking and propagating updates across all the representations. FaCIL is compared against the requirements, implemented in crowd 2.0, and assessed with a use case. The proof-of-concept implementation in the web-based modelling tool crowd 2.0 demonstrates its viability. The framework also meets the requirements and fully supports the use case.

Toward a systematic conflict resolution framework for ontologies.

BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to limitations in the logic or the subject domain semantics. Examples are dealing with different foundational ontologies in ontology alignment and OWL 2 DL's transitive object property versus a qualified cardinality constraint. Such conflicts have to be resolved somehow. However, only isolated and fragmented guidance for doing so is available, which therefore results in ad hoc decision-making that may not be the best choice or forgotten about later. RESULTS: This work aims to address this by taking steps towards a framework to deal with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. It proposes an initial library of common conflicts, a conflict set, typical steps toward resolution, and the software availability and requirements needed for it. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak, being avian influenza, and running examples with COVID-19 ontologies. CONCLUSIONS: The evaluation demonstrated the potential and feasibility of a conflict resolution framework for ontologies.

The African wildlife ontology tutorial ontologies.

BACKGROUND: Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercise they carry out. Not providing good examples holistically causes the propagation of sub-optimal ontology development, which may negatively affect the quality of a real domain ontology. RESULTS: We identified 22 requirements that a good tutorial ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an ontology engineering course or tutorial since 2010 and is included in a recent ontology engineering textbook with relevant examples and exercises. CONCLUSION: The African Wildlife Ontology provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating just language features and automated reasoning. It assists in demonstrating tasks concerning ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies.

Dataset of ontology competency questions to SPARQL-OWL queries translations.

This data article reports on a new set of 234 competency questions for ontology development and their formalisation into a set of 131 SPARQL-OWL queries. This is the largest set of competency questions with their linked queries to date, covering several ontologies of different type in different subject domains developed by different groups of question authors and ontology developers. The dataset is focused specifically on the ontology TBox (terminological part). The dataset may serve as a manually created gold standard for testing and benchmarking, research into competency questions and querying ontologies, and tool development. The data is available in Mendeley Data. Its analysis is presented in "Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL" [15].

Potential mechanisms for the association between fall birth and food allergy.

BACKGROUND: Season of birth has been reported as a risk factor for food allergy, but the mechanisms by which it acts are unknown. METHODS: Two populations were studied: 5862 children from the National Health and Nutrition Examination Survey (NHANES) III and 1514 well-characterized food allergic children from the Johns Hopkins Pediatric Allergy Clinic (JHPAC). Food allergy was defined as self-report of an acute reaction to a food (NHANES), or as milk, egg, and peanut allergy. Logistic regression compared fall or nonfall birth between (i) food allergic and nonallergic subjects in NHANES, adjusted for ethnicity, age, income, and sex, and (ii) JHPAC subjects and the general Maryland population. For NHANES, stratification by ethnicity and for JHPAC, eczema were examined. RESULTS: Fall birth was more common among food allergic subjects in both NHANES (OR, 1.91; 95% CI, 1.31-2.77) and JHPAC/Maryland (OR, 1.31; 95% CI, 1.18-1.47). Ethnicity interacted with season (OR, 2.34; 95% CI, 1.43-3.82 for Caucasians; OR, 1.19; 95% CI, 0.77-1.86 for non-Caucasians; P = 0.04 for interaction), as did eczema (OR, 1.47; 95% CI, 1.29-1.67 with eczema; OR, 1.00; 95% CI, 0.80-1.23 without eczema; P = 0.002 for interaction). CONCLUSIONS: Fall birth is associated with increased risk of food allergy, and this risk is greatest among those most likely to have seasonal variation in vitamin D during infancy (Caucasians) and those at risk for skin barrier dysfunction (subjects with a history of eczema), suggesting that vitamin D and the skin barrier may be implicated in seasonal associations with food allergy.

Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn.

Bio-ontology development is a resource-consuming task despite the many open source ontologies available for reuse. Various strategies and tools for bottom-up ontology development have been proposed from a computing angle, yet the most obvious one from a domain expert perspective is unexplored: the abundant diagrams in the sciences. To speed up and simplify bio-ontology development, we propose a detailed, micro-level, procedure, DiDOn, to formalise such semi-structured biological diagrams availing also of a foundational ontology for more precise and interoperable subject domain semantics. The approach is illustrated using Pathway Studio as case study.