Ontology-Based Transformation and Verification of UML/OCL Constraints

In Software Engineering (SE), the graphical models specify the system's architecture, connection, and characteristics. New SE methods such as MDA utilize graphical models as a nucleus of all development activities. This paper presents the transformation and verification of class diagram and Object Constraint Language (OCL) and transformation algorithm from Class model to ontology in the continuity of our research on UML and ontology integration. The class diagram is transformed into ontology, and constraints specified through OCL are transformed into SPARQL.

Standpoint Logic: Multi-Perspective Knowledge Representation

Ontologies and knowledge bases encode, to a certain extent, the standpoints or perspectives of their creators. As differences and conflicts between standpoints should be expected in multi-agent scenarios, this will pose challenges for shared creation and usage of knowledge sources. Our work pursues the idea that, in some cases, a framework that can handle diverse and possibly conflicting standpoints is more useful and versatile than forcing their unification, and avoids common compromises required for their merge. Moreover, in analogy to the notion of family resemblance concepts, we propose that a collection of standpoints can provide a simpler yet more faithful and nuanced representation of some domains. To this end, we present standpoint logic, a multi-modal framework that is suitable for expressing information with semantically heterogeneous vocabularies, where a standpoint is a partial and acceptable interpretation of the domain. Standpoints can be organised hierarchically and combined, and complex correspondences can be established between them. We provide a formal syntax and semantics, outline the complexity for the propositional case, and explore the representational capacities of the framework in relation to standard techniques in ontology integration, with some examples in the Bio-Ontology domain.

Semantic Enrichment of 3D Models Based on Ontology Integration

The rise of new technologies has led to a growth in the number of 3D models. They can come from various source, hence they are heterogeneous and complex. The level of 3D data access is often a function of the user’s expertise since the 3D data are often registered to different file formats. Some file formats do not show the data tree, as IGES. For using information inside a 3D model, that does not show a data tree, each company adopts his own system that will allow him to access easily to 3D model in order to exploit the hidden knowledge within the models. In this article, we are going to speak about technologies that helps user to exploit and knowledge coming from different file formats. In addition, we are going to present a system named VAQUERO that uses ontology to access, store and share knowledge coming from 3D models.

A Method of Ontology Integration for Designing Intelligent Problem Solvers

Nowadays, designing knowledge-based systems which involve knowledge from different domains requires deep research of methods and techniques for knowledge integration, and ontology integration has become the foundation for many recent knowledge integration methods. To meet the requirements of real-world applications, methods of ontology integration need to be studied and developed. In this paper, an ontology model used as the knowledge kernel is presented, consisting of concepts, relationships between concepts, and inference rules. Additionally, this kernel is also added to other knowledge, such as knowledge of operators and functions, to form an integrated knowledge-based system. The mechanism of this integration method works upon the integration of the knowledge components in the ontology structure. Besides this, problems and the reasoning method to solve them on the integrated knowledge domain are also studied. Many related problems in the integrated knowledge domain and the reasoning method for solving them are also studied. Such an integrated model can represent the real-world knowledge domain about operators and functions with high accuracy and effectiveness. The ontology model can also be applied to build knowledge bases for intelligent problem solvers (IPS) in many mathematical courses in college, such as linear algebra and graph theory. These IPSs have great potential in helping students perform better in those college courses.