A note on the compatibility of part-whole relations with foundational ontologies

Parthood in mereology is one relation, and typically is included in foundational ontologies. Some of these foundational ontologies and many domain ontologies use a plethora of parthood and part-whole relations, such as `sub process' and `portion'. This poses requirements on the foundational ontologies and, perhaps, Ontology, on what to do with these two different approaches to part-whole relations. We present an analysis of DOLCE, BFO, GFO, SUMO, GIST, and YAMATO on their inclusion and use of part-whole relations. It demonstrates there is no perfect fit with either for various reasons. We then aim to bridge this gap with an orchestration of ontologies of part-whole relations that are aligned to several foundational ontologies and such that they can be imported into other ontologies

Preventing, Detecting, and Revising Flaws in Object Property Expressions

The OWL 2 DL ontology language is very expressive and has many features for declaring complex object property expressions. Standard reasoning services for OWL ontologies take these expressions as 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 in the class hierarchy may actually be due to a mistake in the 'object property box', not the class axioms. We analyse the principles of subsumption in object property hierarchies, and use it to identify the types of flaws that can occur in object property expressions. We propose the compatibility services SubProS and ProChainS that check for meaningful property hierarchies and property chaining and propose how to revise a flaw. These insights can also be used to prevent flaws and to choose the best option, which we demonstrate with the chain pattern for upward and downward distributivity over parthood relations. SubProS and ProChainS were evaluated with several ontologies, which demonstrates that such flaws do exist, that they can be isolated effectively, and useful suggestions for revisions can be proposed

Orchestrating a Network of Mereo(topo)logical Theories

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

Pitfalls in Ontologies and TIPS to Prevent Them

Abstract. 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. 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. We also propose guidelines to avoid the inclusion of such common pitfalls in new ontologies, the Typical pItfalls Prevention Scheme (TIPS), so as to increase the baseline quality of OWL ontologies

ROMULUS: a Repository of Ontologies for MULtiple USes populated with foundational ontologies

A foundational ontology contributes to ontology-driven conceptual data modelling and is used to solve interoperability issues among domain ontologies. Multiple foundational ontologies have been developed in recent years, and most of them are available in several versions. This has re-introduced the interoperability problem, increased the need for a coordinated and structured comparison and elucidation of modelling decisions, and raised the requirement for software infrastructure to address this. We present here a basic step in that direction with the Repository of Ontologies for MULtiple USes, ROMULUS, which is the first online library of machine-processable, modularised, aligned, and logic-based merged foundational ontologies. In addition to the typical features of a model repository, it has a foundational ontology recommender covering features of six foundational ontologies, tailor-made modules for easier reuse, and a catalogue of mappable and non-mappable elements among the BFO, GFO and DOLCE foundational ontologies

Evidence-based lean logic profiles for conceptual data modelling languages

Multiple logic-based reconstruction of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models