Ontology Cleaning by Mereotopological Reasoning

A mereotopological semantics to manage ontologies is presented. The aim is to provide a formal basis for ontology cleaning. It allows us to arrange, in a consistent manner, the concepts in early steps of the building of an ontology as well as to repair anomalies. The semantics supports cleaning cycle that combines several AI techniques as closed world assumption, default reasoning on taxonomies and knowledge acquisition.Junta de Andalucía TIC-13

Mereotopological Patterns for Ontology Evolution and Debugging

In this paper the foundational principles and the application of a mereotopological theory, the Region Connection Calculus, for controlling the revision of formal ontologies by means of visual arrangements is presented. The visual representation of logical relationships between concepts of an ontology is defined, and it is computed by means of an automated theorem prover. The user can recognize mereotopological patterns in the visual representation, particularly those representing anomalies in the ontology. An intelligent tool called Paella is designed and implemented for this task. Also, the extension to this formalism for managing uncertainty in concept reasoning is described

Visual Ontology Cleaning: Cognitive Principles and Applicability

In this paper we connect two research areas, the Qualitative Spatial Reasoning and visual reasoning on ontologies. We discuss the logical limitations of the mereotopological approach to the visual ontology cleaning, from the point of view of its formal support. The analysis is based on three different spatial interpretations wich are based in turn on three different spatial interpretations of the concepts of an ontology.Ministerio de Educación y Ciencia TIN2004-0388

Extending Qualitative Spatial Theories with Emergent Spatial Concepts: An Automated Reasoning Approach

Qualitative Spatial Reasoning is an exciting research field of the Knowledge Representation and Reasoning paradigm whose application often requires the extension, refinement or combination of existent theories (as well as the associated calculus). This paper addresses the issue of the sound spatial interpretation of formal extensions of such theories; particularly the interpretation of the extension and the desired representational features. The paper shows how to interpret certain kinds of extensions of Region Connection Calculus (RCC) theory. We also show how to rebuild the qualitative calculus of these extensions.Junta de Andalucía TIC-606

Extension of Ontologies Assisted by Automated Reasoning Systems

A method to extend ontologies with the assistance of automated reasoning systems and preserving a kind of completeness with respect to their associate conceptualizations is presented. The use of such systems makes feasible the ontological insertion of new concepts, but it is necessary to re-interpret the older ones with respect to new ontological commitments.We illustrate the method extending a well-known ontology about spatial relationships, the called Region Connection Calculus.Ministerio de Educación y Ciencia TIN2004-0388

Mereotopological Analysis of Formal Concepts in Security Ontologies

In this paper an analysis of security ontologies, using an mereotopological interpretation of the relationship amongst their classes, based on the entailment in the ontology, is presented. The analysis is carried out by means of a graphical tool (called Paella) that implements such an interpretation and it can suggest the potential debugging of anomalies. The analysis also suggests how to interpret the representational anomalies.Ministerio de Ciencia e Innovación TIN2009-0949

A Formal Foundation for Knowledge Integration of Defficent Information in the Semantic Web

Maintenance of logical robustness in Information Integration represents a major challenge in the envisioned Semantic Web. In this framework, it is previsible unprecise information (with respect to an ontology) is retrieved from some resources. The sound integration of such information is crucial to achieve logical soundness. We present a data-driven approach to classify that knowledge by means of the cognitive entropy of the possible robust ontology extensions and data.Ministerio de Educación y Ciencia TIN2004-0388

On the Use of Automated Reasoning Systems in Ontology Integration.

Ontology Integration is a challenge in the field of Knowledge Engineering, whose solution is indispensable for the envisioned Semantic Web. Some approximations suffer from logical confidence, and others are hard to mechanize. In this paper a method – assisted by Automated Reasoning Systems – to solve a subproblem, the merging of ontologies, is presented. A case study of application is drawn from the field of Qualitative Spatial Reasoning.Junta de Andalucía Minerva Services in Mobility Platform Project WeTeVe (2C/040

Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies

A logical formalism to support the insertion of uncertain concepts in formal ontologies is presented. It is based on the search of extensions by means of two automated reasoning systems (ARS), and it is driven by what we call cognitive entropy.Ministerio de Educación y Ciencia TIN2004-0388

Semantics for incident identification and resolution reports

In order to achieve a safe and systematic treatment of security protocols, organizations release a number of technical briefings describing how to detect and manage security incidents. A critical issue is that this document set may suffer from semantic deficiencies, mainly due to ambiguity or different granularity levels of description and analysis. An approach to face this problem is the use of semantic methodologies in order to provide better Knowledge Externalization from incident protocols management. In this article, we propose a method based on semantic techniques for both, analyzing and specifying (meta)security requirements on protocols used for solving security incidents. This would allow specialist getting better documentation on their intangible knowledge about them.Ministerio de Economía y Competitividad TIN2013-41086-