Did you evaluate your ontology? OOPS! (OntOlogy Pitfall Scanner!)

The application of methodologies for building ontologies can improve ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. In this context, our aim is to describe OOPS!(OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.

OOPS! (OntOlogy Pitfall Scanner!): a web-based tool for ontology evaluation

Presentación en Workshop EUON 2014

Ontology Evaluation: a pitfall-based approach to ontology diagnosis

La evaluación de ontologías, incluyendo diagnóstico y reparación de las mismas, es una compleja actividad que debe llevarse a cabo en cualquier proyecto de desarrollo ontológico para comprobar la calidad técnica de las ontologías. Sin embargo, existe una gran brecha entre los enfoques metodológicos sobre la evaluación de ontologías y las herramientas que le dan soporte. En particular, no existen enfoques que proporcionen guías concretas sobre cómo diagnosticar y, en consecuencia, reparar ontologías. Esta tesis pretende avanzar en el área de la evaluación de ontologías, concretamente en la actividad de diagnóstico. Los principales objetivos de esta tesis son (a) ayudar a los desarrolladores en el diagnóstico de ontologías para encontrar errores comunes y (b) facilitar dicho diagnóstico reduciendo el esfuerzo empleado proporcionando el soporte tecnológico adecuado. Esta tesis presenta las siguientes contribuciones: • Catálogo de 41 errores comunes que los ingenieros ontológicos pueden cometer durante el desarrollo de ontologías. • Modelo de calidad para el diagnóstico de ontologías alineando el catálogo de errores comunes con modelos de calidad existentes. • Diseño e implementación de 48 métodos para detectar 33 de los 41 errores comunes en el catálogo. • Soporte tecnológico OOPS!, que permite el diagnstico de ontologías de forma (semi)automática. De acuerdo con los comentarios recibidos y los resultados de los test de satisfacción realizados, se puede afirmar que el enfoque desarrollado y presentado en esta tesis ayuda de forma efectiva a los usuarios a mejorar la calidad de sus ontologías. OOPS! ha sido ampliamente aceptado por un gran número de usuarios de formal global y ha sido utilizado alrededor de 3000 veces desde 60 países diferentes. OOPS! se ha integrado en software desarrollado por terceros y ha sido instalado en empresas para ser utilizado tanto durante el desarrollo de ontologías como en actividades de formación. Abstract Ontology evaluation, which includes ontology diagnosis and repair, is a complex activity that should be carried out in every ontology development project, because it checks for the technical quality of the ontology. However, there is an important gap between the methodological work about ontology evaluation and the tools that support such an activity. More precisely, not many approaches provide clear guidance about how to diagnose ontologies and how to repair them accordingly. This thesis aims to advance the current state of the art of ontology evaluation, specifically in the ontology diagnosis activity. The main goals of this thesis are (a) to help ontology engineers to diagnose their ontologies in order to find common pitfalls and (b) to lessen the effort required from them by providing the suitable technological support. This thesis presents the following main contributions: • A catalogue that describes 41 pitfalls that ontology developers might include in their ontologies. • A quality model for ontology diagnose that aligns the pitfall catalogue to existing quality models for semantic technologies. • The design and implementation of 48 methods for detecting 33 out of the 41 pitfalls defined in the catalogue. • A system called OOPS! (OntOlogy Pitfall Scanner!) that allows ontology engineers to (semi)automatically diagnose their ontologies. According to the feedback gathered and satisfaction tests carried out, the approach developed and presented in this thesis effectively helps users to increase the quality of their ontologies. At the time of writing this thesis, OOPS! has been broadly accepted by a high number of users worldwide and has been used around 3000 times from 60 different countries. OOPS! is integrated with third-party software and is locally installed in private enterprises being used both for ontology development activities and training courses.

Revisiting the Classics to recover the Physical Sense in electrical noise

This paper shows a physically cogent model for electrical noise in resistors that has been obtained from Thermodynamical reasons. This new model derived from the works of Johnson and Nyquist also agrees with the Quantum model for noisy systems handled by Callen and Welton in 1951, thus unifying these two Physical viewpoints. This new model is a Complex or 2-D noise model based on an Admittance that considers both Fluctuation and Dissipation of electrical energy to excel the Real or 1-D model in use that only considers Dissipation. By the two orthogonal currents linked with a common voltage noise by an Admittance function, the new model is shown in frequency domain. Its use in time domain allows to see the pitfall behind a paradox of Statistical Mechanics about systems considered as energy-conserving and deterministic on the microscale that are dissipative and unpredictable on the macroscale and also shows how to use properly the Fluctuation-Dissipation Theorem.

Did you validate your ontology? OOPS!

The application of methodologies for building ontologies can im-prove ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. Sev-eral authors have provided lists of typical anomalies detected in ontologies dur-ing the last decade. In this context, our aim in this paper is to describe OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.