Structuring Abstraction to Achieve Ontology Modularisation

Large and complex ontologies lead to usage difficulties, thereby hampering the ontology developers’ tasks. Ontology modules have been proposed as a possible solution, which is supported by some algorithms and tools. However, the majority of types of modules, including those based on abstraction, still rely on manual methods for modularisation. Toward filling this gap in modularisation techniques, we systematised abstractions and selected five types of abstractions relevant for modularisation for which we created novel algorithms, implemented them, and wrapped it in a GUI, called NOMSA, to facilitate their use by ontology developers. The algorithms were evaluated quantitatively by assessing the quality of the generated modules. The quality of a module is measured by comparing it to the benchmark metrics from an existing framework for ontology modularisation. The results show that module’s quality ranges between average to good, whilst also eliminating manual intervention

The Current Landscape of Pitfalls in Ontologies

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

Evidence-based lean conceptual data modelling languages

Multiple logic-based reconstructions of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exist. 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, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith 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 DL ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

Conceptual Model Interoperability: a Metamodel-driven Approach

Linking, integrating, or converting conceptual data models represented in different modelling languages is a common aspect in the design and maintenance of complex information systems. While such languages seem similar, they are known to be distinct and no unifying framework exists that respects all of their language features in either model transformations or inter-model assertions to relate them. We aim to address this issue using an approach where the rules are enhanced with a logic-based metamodel. We present the main approach and some essential metamodel-driven rules for the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2. The transformations for model elements and patterns are used with the metamodel to verify correctness of inter-model assertions across models in different languages

Abstracting modelling languages: A reutilization approach

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-31095-9_9Proceedings of 24th International Conference, CAiSE 2012, Gdansk, Poland, June 25-29, 2012Model-Driven Engineering automates the development of information systems. This approach is based on the use of Domain-Specific Modelling Languages (DSMLs) for the description of the relevant aspects of the systems to be built. The increasing complexity of the target systems has raised the need for abstraction techniques able to produce simpler versions of the models, but retaining certain properties of interest. However, developing such abstractions for each DSML from scratch is a time and resource consuming activity. Our solution to this situation is a number of techniques to build reusable abstractions that are defined once and can be reused over families of modelling languages sharing certain requirements. As a proof of concept, we present a catalogue of reusable abstractions, together with an implementation in the MetaDepth multi-level meta-modelling tool.Work funded by the Spanish Ministry of Economy and Competitivity (TIN2011-24139), and the R&D programme of Madrid Region (S2009/TIC-1650)

A core ontology of macroscopic stuff

Domain ontologies contain representations of types of stuff (matter, mass, or substance), such as milk, alcohol, and mud, which are represented in a myriad of ways that are neither compatible with each other nor do they follow a structured approach within the domain ontology. Foundational ontologies and Ontology distinguish between pure stuff and mixtures only, if it contains stuff. We aim to fill this gap between foundational and domain ontologies by applying the notion of a `bridging' core ontology, being an ontology of categories of stuff that is formalised in OWL. This core ontology both refines the DOLCE and BFO foundational ontologies and resolves the main type of interoperability issues with stuffs in domain ontologies, thereby also contributing to better ontology quality. Modelling guidelines are provided to facilitate the Stuff Ontology's use

ONSET: Automated foundational ontology selection and explanation

Abstract. It has been shown that using a foundational ontology for domain ontology development is beneficial in theory and practice. However, developers have difficulty with choosing the appropriate foundational ontology, and why. In order to solve this problem, a comprehensive set of criteria that influence foundational ontology selection has been compiled and the values for each parameter determined for DOLCE, BFO, GFO, and SUMO. This paper-based analysis is transformed into an easily extensible algorithm and implemented in the novel tool ONSET, which helps a domain ontology developer to choose a foundational ontology through interactive selection of preferences and scaling of importance so that it computes the most suitable foundational ontology for the domain ontology and explains why this selection was made. This has been evaluated in an experiment with novice modellers, which showed that ONSET greatly assists in foundational ontology selection.

Detecting and revising flaws in OWL object property expressions

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.