A Double Classification of Common Pitfalls in Ontologies

The application of methodologies for building ontologies has improved the ontology quality. However, such a quality is not totally guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or worst practices in the modelling. In this context, our aim in this paper is twofold: (1) to provide a catalogue of common worst practices, which we call pitfalls, and (2) to present a double classification of such pitfalls. These two products will serve in the ontology development in two ways: (a) to avoid the appearance of pitfalls in the ontology modelling, and (b) to evaluate and correct ontologies to improve their quality

Breaking down brick walls: Design, construction, and prototype fabrication knowledge in architecture

Architectural designs are not just collections of 3D objects. Architects have both high-level aesthetic design intent, and intent for the functionality of the building; these must eventually translate into real-world construction materials and processes. Physical prototypes are still essential for the architect and their clients to get a feel for whether designs "work". An exciting recent development in architecture is the use of industrial robots to automatically construct 3D prototype architectural models. But programming the robots requires tedious procedures of low-level commands, far removed from the designer's intent. Adeon is a system that integrates high-level architectural design knowledge, including aesthetic and stylistic intent, with knowledge about materials and construction processes, and robot programming code for constructing prototype 3D physical models. It centers around collecting and associating "common sense" knowledge, expressed in English and converted to a knowledge representation about the various levels. It provides a graphic editor that allows architects to draw high-level aesthetic designs, perhaps referencing known styles or historical examples, and retrieving relevant construction, materials, and cost information. It automatically produces a robot program for constructing the prototype. We present examples detailing the design of various styles of brick walls. Adeon is an interesting example of how to provide an interface for creative work that spans both high-level and low-level concerns

Reusing Ontology Design Patterns in a Context Ontology Network

Reusing knowledge resources, specifically Ontology Design Patterns (ODPs), has became a popular technique within the ontology engineering field. Such a reuse allows speeding up the ontology development process, saving time and money, and promoting the application of good practices. Recently methods and tools to support the reuse of ODPs have emerged. In addition, the existence of detailed examples of real use cases that reuse ODPs favours the adoption and application of such methods. Thus, our objective in this paper is to show an example of how to apply a method for reusing ODPs during the development of a context ontology network to model context-related knowledge that allows adapting applications based on user context. Besides, in this paper we present the main drawbacks found during the application of the reuse method as well as some proposals to overcome them

Malas prácticas en ontologías

Los denominados patrones de diseño ontológico (Ontology Design Patterns u ODPs), definidos como soluciones a problemas de diseño, suponen una ayuda para los desarrolladores durante la modelización de ontologías, proporcionan una guía en el desarrollo y en la evaluación, y mejoran la calidad de las ontologías resultantes. Sin embargo, se ha demostrado que los desarrolladores de ontologías tienen dificultades para reutilizar los patrones de diseño correctos, incluyendo en estos casos errores en la modelización. Para evitar la aparición de errores de modelado en ontologías, en este artículo se propone una clasificación de los mismos en dos tipos: (1) errores de modelado relacionados con ODPs existentes, llamados antipatrones; y (2) errores de modelado no relacionados con ODPs existentes, denominados malas prácticas. Esta clasificación ha surgido fruto del análisis de un conjunto de ontologías. Este artículo se centra en las malas prácticas encontradas durante dicho análisis, presentando una clasificación de las mismas y una serie de ejemplos

Publishing Linked Data - There is no One-Size-Fits-All Formula

Publishing Linked Data is a process that involves several design decisions and technologies. Although some initial guidelines have been already provided by Linked Data publishers, these are still far from covering all the steps that are necessary (from data source selection to publication) or giving enough details about all these steps, technologies, intermediate products, etc. Furthermore, given the variety of data sources from which Linked Data can be generated, we believe that it is possible to have a single and uni�ed method for publishing Linked Data, but we should rely on di�erent techniques, technologies and tools for particular datasets of a given domain. In this paper we present a general method for publishing Linked Data and the application of the method to cover di�erent sources from di�erent domains

Ontology Analysis Based on Ontology Design Patterns

The so-called Ontology Design Patterns (ODPs), which have been defined as solutions to ontological design problems, are of great help to developers when modelling ontologies since these patterns provide a development guide and improve the quality of the resulting ontologies. However, it has been demonstrated that, in many cases, developers encounter difficulties when they have to reuse ontology design patterns and include errors in the modelling. Thus, to avoid errors in ontology modelling, this paper proposes classifying errors into two types: (1) errors related to existing ODPs, called anti-patterns, and (2) errors not related to existing ODPs, called worst practices. This classification is the result of analysing a set of ontologies which come from an academic experiment. In addition, the paper presents a general classification of the worst practices found and a set of worst practice examples. Finally, the paper shows an example of how the aforementioned worst practices could be related among them

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