Collaborating Authors

Merging of Ontologies Through Merging of Their Rules Artificial Intelligence

Ontology merging is important, but not always effective. The main reason, why ontology merging is not effective, is that ontology merging is perform ed without considering goals. Goals define the way, in which ontologies to be merg ed more effectively. The paper illustrates ontology m erging by means of rules, which are generate d from these ontologies. This is necessary for further use in expert systems.

Ontology-Based Monitoring of Dynamic Systems

AAAI Conferences

Our understanding of the notion "dynamic system" is a rather broad one: such a system has states, which can change over time. Ontologies are used to describe the states of the system, possibly in an incomplete way. Monitoring is then concerned with deciding whether some run of the system or all of its runs satisfy a certain property, which can be expressed by a formula of an appropriate temporal logic. We consider different instances of this broad framework, which can roughly be classified into two cases. In one instance, the system is assumed to be a black box, whose inner working is not known, but whose states can be (partially) observed during a run of the system. In the second instance, one has (partial) knowledge about the inner working of the system, which provides information on which runs of the system are possible. In this paper, we will review some of our recent work that can be seen as instances of this general framework of ontology-based monitoring of dynamic systems. We will also mention possible extensions towards probabilistic reasoning and the integration of mathematical modeling of dynamical systems.

Testing the AgreementMaker System in the Anatomy Task of OAEI 2012 Artificial Intelligence

The AgreementMaker system was the leading system in the anatomy task of the Ontology Alignment Evaluation Initiative (OAEI) competition in 2011. While AgreementMaker did not compete in OAEI 2012, here we report on its performance in the 2012 anatomy task, using the same configurations of AgreementMaker submitted to OAEI 2011. Additionally, we also test AgreementMaker using an updated version of the UBERON ontology as a mediating ontology, and otherwise identical configurations. AgreementMaker achieved an F-measure of 91.8% with the 2011 configurations, and an F-measure of 92.2% with the updated UBERON ontology. Thus, AgreementMaker would have been the second best system had it competed in the anatomy task of OAEI 2012, and only 0.1% below the F-measure of the best system.

Multidimensional Ontology Model to Support Context-aware Systems

AAAI Conferences

Mobile computing is rapidly gaining importance because there is an incremental daily demand for information access from anywhere and at any time with multiple purposes. This situation gives rise to the new era of computing called Ubiquitous Computing, where it is necessary to develop new and improved structures for knowledge and information representation and exchange, in order to support the implementation of intelligent and context-aware systems. Thus search results will be fully based on contextual information and user profiles. This paper describes an architecture based on a multi-dimensional ontology model to represent mobile user contexts, Web services and application domains.

The African Wildlife Ontology tutorial ontologies: requirements, design, and content Artificial Intelligence

Background. Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercises they carry out. Not providing good examples holistically causes the propagation of sub-optimal ontology development, which may negatively affect the quality of a real domain ontology. Results. We identified 22 requirements that a good tutorial ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an ontology engineering course or tutorial since 2010 and is included in a recent ontology engineering textbook with relevant examples and exercises. Conclusion. The African Wildlife Ontology provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating only language features and automated reasoning. It assists in demonstrating tasks about ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies.