C. Maria Keet

Detecting and revising flaws in OWL object property expressions

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 ontologists 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.

Representing mereotopological relations in OWL ontologies with ONTOPARTS

Representing and reasoning over mereotopological relations (parthood and location) in an ontology is a well-known challenge, because there are many relations to choose from and OWL has limited expressiveness in this regard. To address these issues, we structure mereotopological relations based on the KGEMT mereotopological theory. A correctly chosen relation counterbalances some weaknesses in OWLs representation and reasoning services. To achieve effortless selection of the appropriate relation, we hide the complexities of the underlying theory through automation of modelling guidelines in the new tool OntoPartS. It uses, mainly, the categories from DOLCE [12], which avoids lengthy question sessions, and it includes examples and verbalizations. OntoPartS was experimentally evaluated, which demonstrated that selecting and representing the desired relation was done efficiently and more accurately with OntoPartS.

ONSET: automated foundational ontology selection and explanation

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.

The Foundational Ontology Library ROMULUS

A purpose of a foundational ontology is to solve interoperability issues among domain ontologies and they are used for ontology-driven conceptual data modelling. 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 interesting mappable and non-mappable elements among the BFO, GFO and DOLCE foundational ontologies.

Toward an Ontology-Driven Unifying Metamodel for UML Class Diagrams, EER, and ORM2

Software compatibility and application integration can be achieved using their respective conceptual data models. However, each model may be represented in a different language. While such languages seem similar yet known to be distinct, no unifying framework exists that respects all of their language features. Aiming toward filling this gap, we designed a common, ontology-driven, metamodel of the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2, such that each is a fragment of the encompassing consistent metamodel. This paper presents and overview and notable insights obtained on the real common core entities and constraints, roles and relationships, and attributes and value types that we refine with the notion of dimensional attribute.

Ontology authoring with FORZA

Generic, reusable ontology elements, such as a foundational ontologys 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.

Enhancing identification mechanisms in UML class diagrams with meaningful keys

Unlike identification with keys and reference schemes in ER and ORM, UML uses internal, system-generated, identifiers, with a little-known underspecified option for user-defined identifiers. To increase the ontological foundations of UML, we propose two language enhancements for UML, being formally defined simple and compound identifiers and the notion of defined class, which also have a corresponding extension of UMLs metamodel.

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.

Rough subsumption reasoning with rOWL

There are various recent efforts to broaden applications of ontologies with vague knowledge, motivated in particular by applications of bio(medical)-ontologies, as well as to enhance rough set information systems with a knowledge representation layer by giving more attention to the intension of a rough set. This requires not only representation of vague knowledge but, moreover, reasoning over it to make it interesting for both ontology engineering and rough set information systems. We propose a minor extension to OWL 2 DL, called rOWL, and define the novel notions of rough subsumption reasoning and classification for rough concepts and their approximations.

Structural Entities of an Ontology-Driven Unifying Metamodel for UML, EER, and ORM2

Software interoperability may be achieved by using their respective conceptual data models. However, each model may be represented in a different conceptual data modelling language for the tools purpose or due to legacy issues. Several translations between small subsets of language features are known, but no unified model exists that includes all their language features. Aiming toward filling this gap, we designed a common and unified, ontology-driven, metamodel covering and unifying EER, UML Class Diagrams v2.4.1, and ORM2. This paper presents the static, structural, components of the metamodel, highlighting the common entities and summarizing some modelling motivations.