Dimensions Affecting Representation Styles in Ontologies

There are different ways to formalise roughly the same knowledge, which negatively affects ontology reuse and alignment and other tasks such as formalising competency questions automatically. We aim to shed light on, and make more precise, the intuitive notion of such `representation styles' through characterising their inherent features and the dimensions by which a style may differ. This has led to a total of 28 different traits that are partitioned over 10 dimensions. The operationalisability was assessed through an evaluation of 30 ontologies on those dimensions and applicable values. It showed that it is feasible to use the dimensions and values and resulting in three easily recognisable types of ontologies. Most ontologies had clearly one or the other trait, whereas some were inherently mixed due to inclusion of different and conflicting design decisions

Evidence-based Languages for Conceptual Data Modelling Profiles

To improve database system quality as well as runtime use of conceptual models, many logic-based reconstructions of conceptual data modelling languages have been proposed in a myriad of logics. They each cover their features to a greater or lesser extent and are typically motivated from a logic viewpoint. This raises questions such as what would be an evidence-based common core and what is the optimal language profile for a conceptual modelling language family. Based on a common metamodel of UML Class Diagrams (v2.4.1), ER/EER, and ORM/2's static elements, a set of 101 conceptual models, and availing of computational complexity insights from Description Logics, we specify these profiles. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable $\mathcal{ALNI}$). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

A model for verbalising relations with roles in multiple languages

Natural language renderings of ontologies facilitate communication with domain experts. While for ontologies with terms in English this is fairly straightforward, it is problematic for grammatically richer languages due to conjugation of verbs, an article that may be dependent on the preposition, or a preposition that modifies the noun. There is no systematic way to deal with such `complex' names of OWL object properties, or their verbalisation with existing language models for annotating ontologies. The modifications occur only when the object performs some {\em role} in a relation, so we propose a conceptual model that can handle this. This requires reconciling the standard view with relational expressions to a positionalist view, which is included in the model and in the formalisation of the mapping between the two. This eases verbalisation and it allows for a more precise representation of the knowledge, yet is still compatible with existing technologies. We have implemented it as a Prot\'eg\'e plugin and validated its adequacy with several languages that need it, such as German and isiZulu

The SEWASIE multi-agent system

Data integration, in the context of the web, faces new problems, due in particular to the heterogeneity of sources, to the fragmentation of the information and to the absence of a unique way to structure, and view information. In such areas, the traditional paradigms on which database foundations are based (i.e. client/server architecture, few sources containing large information) have to be overcome by new architectures. In this paper we propose a layered P2P architecture for mediator systems. Peers are information nodes which are coordinated by a multi-agent system in order to allow distributed query processing