Competency questions for ontologies are used in a number of ontology development tasks. The questions’ sentences structure have been analysed to inform ontology authoring and validation. One of the problems to make this a seamless process is the hurdle of writing good CQs manually or offering automated assistance in writing CQs. In this paper, we propose an enhanced and automated pipeline where one can trace meticulously through each step, using a mini-corpus, T5, and the SQuAD dataset to generate questions, and the CLaRO controlled language, semantic similarity, and other steps for filtering. This was evaluated with two corpora of different genre in the same broad domain and evaluated with domain experts. The final output questions across the experiments were around 25% for scope and relevance and 45% of unproblematic quality. Technically, it provided ample insight into trade-offs in generation and filtering, where relaxing filtering increased sentence structure diversity but also led to more spurious sentences that required additional processing

Parthood is used widely in ontologies across subject domains. Some modelling guidance can be gleaned from Ontology, yet it offers multiple mereological theories, and even more when combined with topology, i.e., mereotopology. To complicate the landscape, decidable languages put restrictions on the language features, so that only fragments of the mereo(topo)logical theories can be represented, yet during modelling, those full features may be needed to check correctness. We address these issues by specifying a structured network of theories formulated in multiple logics that are glued together by the various linking constructs of the Distributed Ontology Language, DOL. For the KGEMT mereotopological theory and five sub-theories, together with the DL-based OWL species and first- and second-order logic, this network in DOL orchestrates 28 ontologies. Further, we propose automated steps toward resolution of language feature conflicts when combining modules, availing of the new 'OWL classifier' tool that pinpoints profile violations.

Generic, reusable ontology elements, such as a foundational ontology's categories and part-whole relations, are essential for good and interoperable knowledge representation. Ontology developers, which include domain experts and novices, face the challenge to figure out which category or relationship to choose for their ontology authoring task. To reduce this bottleneck, there is a need to have guidance to handle these Ontology-laden entities. We solve this with a generic approach and realize it with the Foundational Ontology and Reasoner-enhanced axiomatiZAtion (FORZA) method, containing DOLCE, a decision diagram for DOLCE categories, part-whole relations, and an automated reasoner that is used during the authoring process to propose feasible axioms. This fusion has been integrated in the MoKi ontology development tool to validate its implementability.

An approach in achieving semantic interoperability among heterogeneous systems is to offer infrastructure to assist with linking and integration using a foundational ontology. Due to the creation of multiple foundational ontologies, this also means linking and integrating those ones. In order to achieve this, we have selected the widely used foundational ontologies DOLCE, BFO, and GFO, and their related modules, on which to perform ontology mediation (alignment, mapping, and merging). The foundational ontologies were aligned by identifying correspondences between ontology entities using seven tools, documentation, and our manual alignments, and comparing their effectiveness. Thereafter, based on the alignments, we created correspondences in the ontology files resulting in entity mappings and merged ontologies. However, during the mapping process, it was found that differences in foundational ontologies, such as their hierarchical structure, conflicting axioms due to complement and disjointness, and incompatible domain and range restriction, cause logical inconsistencies in foundational ontology alignments, thereby greatly reducing the number of mappings. We analyse and present these logical inconsistencies with possible solutions to some of them. 1

A growing number of ontologies are already available thanks to development initiatives in many different fields. In such ontology developments, developers must tackle a wide range of difficulties and handicaps, which can result in the appearance of anomalies in the resulting ontologies. Therefore, ontology evaluation plays a key role in ontology development projects. OOPS! is an on-line tool that automatically detects pitfalls, considered as potential errors or problems, and thus may help ontology developers to improve their ontologies. To gain insight in the existence of pitfalls and to assess whether there are differences among ontologies developed by novices, a random set of already scanned ontologies, and existing well-known ones, data of 406 OWL ontologies were analysed on OOPS!’s 21 pitfalls, of which 24 ontologies were also examined manually on the detected pitfalls. The various analyses performed show only minor differences between the three sets of ontologies, therewith providing a general landscape of pitfalls in ontologies.

Generic, reusable ontology elements, such as a foundational ontology?s categories and part-whole relations, are essential for good and interoperable knowledge representation. Ontology developers face the challenge to figure out which category their class belongs to and which relationship to choose for their ontology authoring tasks. To reduce this bottleneck, there is a need to have guidance to handle these Ontology-laden entities. We solve this with the generic approach GENERATOR: Guided ENtity reuse and class Expression geneRATOR that enables easy reuse of already represented knowledge such that it guides the modeller toward the comparatively best options of possible axioms to add. This has been realised with DOLCE, part-whole relations, and an automated reasoner that is used during the authoring process to propose feasible axioms, and the software has been integrated in the MoKi ontology development tool.

A purpose of a foundational ontology is to solve interoperability issues among ontologies. Many foundational ontologies have been developed, re-introducing the ontology interoperability problem. We address this with the new online foundational ontology repository ROMULUS, in which DOLCE, BFO and GFO have been aligned. We summarise the alignments, mappings, and logical inconsistencies of the foundational ontologies, and ROMULUS?s features.