Evolución en el uso de las TICs: pasado, presente y futuro. Una experiencia en Programación Lógica

En este artículo se presenta una experiencia en el uso de diversas TICs en dos asignaturas relacionadas con programación lógica que se imparten en la Ingeniería en Informática (Facultad de Informática, UPM). El uso de TICs en dichas asignaturas se centró en el pasado en tecnologías propias de la Web 1.0 (p.ej., las listas de correos y las páginas Web). En la actualidad se están utilizando dichas tecnologías de la Web 1.0 en combinación con un entorno virtual de enseñanza-aprendizaje (concretamente Moodle). En el futuro cercano se plantea el uso integrado de tecnologías de la Web 2.0 (p.ej, el wiki) en un entorno virtual de enseñanza-aprendizaje

OOPS! – OntOlogy Pitfalls Scanner!

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. Several authors have provided lists of typical anomalies detected in ontologies during the last decade. In this context, our aim in this technical report is to describe OOPS! (OntOlogy Pitfalls Scanner!), a tool for pitfalls detection in ontology developments

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

Pattern for Re-engineering a Classification Scheme, which Follows the Adjacency List Data Model, to a Taxonomy

This pattern for re-engineering non-ontological resources (pr-nor) fits in the schema re-engineering category proposed by [3]. The pattern defines a procedure that transforms the classification scheme components into ontology representational primitives. This pattern comes from the experience of ontology engineers in developing ontologies using classification schemes in several projects (seemp 1 , neon 2 , and knowledge web 3 ). The pattern is included in a pool of patterns, which is a key element of our method for re-engineering non-ontological resources into ontologies [2]. The patterns generate the ontologies at a conceptualization level, independent of the ontology implementation language

NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse

A new ontology development paradigm has started; its emphasis lies on the reuse and possible subsequent reengineering of knowledge resources, on the collaborative and argumentative ontology development, and on the building of ontology networks; this new trend is the opposite of building new ontologies from scratch. To help ontology developers in this new paradigm, it is important to provide strong methodological support. This thesis presents some contributions to the methodological area of the Ontology Engineering field that we are sure will improve the development and building of ontologies networks, and thus, - It proposes the NeOn Glossary of Processes and Activities, which identifies and defines the processes and activities potentially involved when ontology networks are collaboratively built. - It defines a set of two ontology network life cycle models. - It identifies and describes a collection of nine scenarios for building ontology networks. - It provides some methodological guidelines for performing the ontology requirements specification activity, to obtain the requirements that the ontology should fulfil. - It offers some methodological guidelines for obtaining the ontology network life cycle for a concrete ontology network, as part of scheduling ontology projects. Additionally, the thesis provides the technological support to these guidelines: a tool called gOntt. - It also proposes some methodological guidelines for the reuse of ontological resources at two different levels of granularity: as a whole (general ontologies and domain ontologies) and using ontology statements

SimpleOrAggregated

In this paper, we describe a content ontology design pattern to represent objects that can be simple or aggregated. The aggregation relation refers to several objects gathered in another object acting as a whole; all these objects should belong to the same concept in the model

Results of Taxonomic Evaluation of RDF(S) and DAML+OIL Ontologies using RDF(S) and DAML+OIL Validation Tools and Ontology Platforms Import Services

Before using RDF(S) and DAML+OIL ontologies in Semantic Web applications, its content should be evaluated from a knowledge representation point of view. In recent years, some RDF(S) and DAML+OIL ‘checkers’, ‘validators’, and ‘parsers’ have been created and several ontology platforms are able to import RDF(S) and DAML+OIL ontologies. Two are the experiments presented in this paper. The first one reveals that the majority of RDF(S) and DAML+OIL parsers (Validating RDF Parser, RDF Validation Service, DAML Validator, and DAML+OIL Ontology Checker) do not detect taxonomic mistakes in ontologies implemented in such languages. So, if such ontologies are imported by ontology platforms, are they able to detect such problems? The second experiment presented in this paper reveals that the majority of the ontology platforms (OilEd, OntoEdit, Protégé-2000, and WebODE) only detect a few of mistakes in concept taxonomies before importing them

Ontology Evaluation Functionalities of RDF(S), DAML+OIL, and OWL Parsers and Ontology Platforms

Before using ontologies in Semantic Web applications, ontology content and ontology tools (parsers, platforms, etc.) should be evaluated. In this paper we evaluate ontology evaluation functionalities of RDF(S), DAML+OIL, and OWL parsers and import services for such languages within ontology platforms. In recent years, some RDF(S), DAML+OIL, and OWL parsers have been created and several ontology platforms are able to import ontologies implemented in such languages. In this paper we present two experiments. The first one reveals that most RDF(S), DAML+OIL, and OWL parsers studied do not detect taxonomic problems, from a knowledge representation point of view, in ontologies implemented in such languages. So, if such ontologies are imported by ontology platforms, the question is: are they able to detect such problems? The second experiment presented in this paper reveals that most ontology platforms analyzed only detect a few of problems in concept taxonomies during ontology import

Building Ontology Networks: How to Obtain a Particular Ontology Network Life Cycle?

To build an ontology, ontology developers should devise first a concrete plan for the ontology development, that is, they should establish the ontology life cycle. To do this, ontology developers should answer two key questions: a) which ontology life cycle model is the most appropriate for their ontology project? and b) which particular activities should be carried out in their ontology life cycle? In this paper we present a set of guidelines to help ontology developers and also naïve users answer such questions

Summarization of an Inverse n-ary Relation

In this paper, we describe a logical ontology design pattern that summarizes a relationship and its inverse between two distinguished members of an n-ary relationship