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Emerging ontology authoring methods to add knowledge to an ontology focus on ameliorating the validation bottleneck. The verification of the newly added axiom is still one of trying and seeing what the reasoner says, because a systematic testbed for ontology authoring is missing. We sought to address this by introducing the approach of test-driven development for ontology authoring. We specify 36 generic tests, as TBox queries and TBox axioms tested through individuals, and structure their inner workings in an 'open box'-way, which cover the OWL 2 DL language features. This is implemented as a Protege plugin so that one can perform a TDD test as a black box test. We evaluated the two test approaches on their performance. The TBox queries were faster, and that effect is more pronounced the larger the ontology is. We provide a general sequence of a TDD process for ontology engineering as a foundation for a TDD methodology.

Abstract-Multiple different understandings and uses exist of what granularity is and how to implement it, where the former influences success of the latter with regards to storing granular data and using granularity for reasoning over the data or information. We propose a taxonomy of types of granularity and discuss for each leaf type how the entities or instances relate within its granular level. Such unambiguous distinctions can guide a conceptual modeler to better distinguish between the types of granularity and the software developer to improve on implementations of granularity.

Abstract. There is an assumption that ontology developers will use a top-down approach by using a foundational ontology, because it purportedly speeds up ontology development and improves quality and interoperability of the domain ontology. Informal assessment of these assumptions reveals ambiguous results that are not only open to different interpretations but also such that foundational ontology usage is not foreseen in most methodologies. Therefore, we investigated these assumptions in a controlled experiment. After a lecture about DOLCE, BFO, and partwhole relations, one-third chose to start domain ontology development with an OWLized foundational ontology. On average, those who commenced with a foundational ontology added more new classes and class axioms, and significantly less object properties than those who started from scratch. No ontology contained errors regarding part-of vs. is-a. The comprehensive results show that the 'cost' incurred spending time getting acquainted with a foundational ontology compared to starting from scratch was more than made up for in size, understandability, and interoperability already within the limited time frame of the experiment.

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used as a reference by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

Abstract. OWL 2 DL is a very expressive language and has many features for declaring complex object property expressions. Standard reasoning services for OWL ontologies assume the axioms in the 'object property box' to be correct and according to the ontologist's intention. However, the more one can do, the higher the chance modelling flaws are introduced; hence, an unexpected or undesired classification or inconsistency may actually be due to a mistake in the object property box, not the class axioms. We identify the types of flaws that can occur in the object property box and propose corresponding compatibility services, SubProS and ProChainS, that check for meaningful property hierarchies and property chaining and propose how to revise a flaw. SubProS and ProChainS were evaluated with several ontologies, demonstrating they indeed do serve to isolate flaws and can propose useful corrections.

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Abstract. A scenario in ontology development and its use is hypothesis testing, such as finding new subconcepts based on the data linked to the ontology. During such experimentation, knowledge tends to be vague and the associated data is often incomplete, which OWL ontologies normally do not consider explicitly. To fill this gap, we use OWL 2 and their application infrastructures together with rough sets. Although OWL 2 QL is insufficient to represent most of rough set's semantics, the mapping layer of its Ontology-Based Data Access framework that links concepts in the ontology to queries over the data source suffice to ascertain if a concept is rough, which subsequently can be modelled more precisely in an OWL 2 DL ontology. We summarise the trade-offs and validate it with the HGT ontology and its 17GB genomics database and with sepsis, which demonstrates it is an encouraging step toward comprehensive and usable rough ontologies.