OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

A core ontological model for semantic sensor web infrastructures

Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects (i.e., vocabulary, syntax, structure, semantics, representation, and context).

The framework for post-accident preparedness in Europe. The IRPA guiding principles for stakeholder engagement

To provide public and private actors at local, regional, national and European levels with methodologies to: – Create the conditions for them to gradually appropriate the problematic of long term rehabilitation following a situation of long‐lasting radioactive contamination; – Develop in their context appropriate means and tools for rehabilitation strategies; – Foster innovation and experimentation at territorial and national levels.

Análisis Post-Hoc de un experimento acerca de TDD

Objetivo: Este Trabajo Fin de Máster (TFM) analiza la importancia de la información poblacional en el análisis de datos de experimentos en IS. Por otro lado se intenta abordar un estudio sobre las posibles metodologías que se pueden usar para realizar dicho análisis. Contexto: El análisis se realiza sobre los resultados de un experimento llevado a cabo por el Grupo de Investigación de IS empírica de la UPM, cuyo objetivo es analizar el impacto que tiene el uso de TDD (Test Driven Development) sobre la calidad y la productividad del desarrollo software en comparación con el desarrollo tradicional TLD. Método de Investigación: Se analizan ocho variables demográficas frente a tres variables respuesta. Las metodologías o técnicas de análisis que se revisan son la Dicotomización, la Correlación de Pearson, la regresión lineal múltiple y la stepwise regression. Resultados: No se encuentran evidencias claras para afirmar que las variables demográficas influyen en los resultados de los experimentos. No obstante los resultados no son del todo concluyentes y queda abierta la investigación a realizarse con una muestra más amplia y representativa. En relación a la metodología de análisis aplicada, la dicotomización y la correlación de Pearson presentan deficiencias que se solventan con la regresión lineal múltiple y la stepwise regression. Conclusión: Resulta de vital importancia encontrar evidencias de la influencia de las características demográficas de los sujetos experimentales en el análisis de los datos experimentos en IS. Se ha encontrado un buen método para analizar esta influencia, pero falta replicar este análisis a más experimentos de IS para obtener resultados mejor fundados.---ABSTRACT---Objective: This Master's Thesis (TFM) discusses the importance of demographic data in the analysis of data from experiments in SE. On the other hand, it attempts to address a study of the possible methodologies that can be used to perform the analysis. Context: The analysis is performed on the results of an experiment conducted by the ESE Research Group of the UPM, aimed at analyzing the impact of the use of TDD (Test Driven Development) on quality and productivity, compared to traditional development TLD (Test Last Development). Research Method: Eight demographic variables were analyzed against three response variables. The methodologies and analysis techniques that are reviewed include dichotomization, Pearson correlation, multiple linear regression and stepwise regression. Results: There is not clear evidence to say that demographic variables influence the results of SE experiments. However the results are not conclusive, and are open to research with a broader and more representative sample. Regarding the applied analysis methodology, dichotomization and Pearson correlation have deficiencies that are solved with multiple linear regression and stepwise regression. Conclusion: It is very important to find evidence on the influence of demographic characteristics of subjects in the data analysis of SE experiments. We found a good way to analyze this influence, but is necessary replicate this analysis on more SE experiments to obtain sound results.

Persuasive system design methods to deliver a cardio-rehabilitation program of post mi outpatients

Las enfermedades no transmisibles provocan cada ano 38 millones de fallecimientos en el mundo. Entre ellas, tan solo cuatro enfermedades son responsables del 82% de estas muertes: las enfermedades cardiovasculares, las enfermedades crónicas respiratorias, la diabetes, y el cáncer. Se prevé que estas cifras aumenten en los próximos anos, ya que las tendencias indican que en el año 2030 las muertes por esta causa ascenderán a 53 millones de personas. La Organización Mundial de la Salud (OMS) considera importante buscar soluciones para afrontar esta situación y ha solicitado a los gobiernos del mundo la implementación de intervenciones para mejorar los hábitos de vida de las personas y reducir así el riesgo de desarrollo de enfermedades no trasmisibles. Cada año se producen 32 millones de infartos de miocardio y derrames celebrales, de los cuales 12.5 son mortales. En el mundo entre el 40% y 75% de la víctimas de un infarto de miocardio mueren antes de su ingreso en el hospital. En los casos que sobreviven, la adopción de un estilo de vida saludable puede evitar infartos sucesivo, y supone un ahorro potencial de 6 billones de euros al año. La rehabilitación cardiaca es un programa individualizado que aplica un método multidisciplinar para ayudar al paciente a recuperar su condición física, a gestionar la enfermedad cardiovascular y sus comorbilidades, a adoptar hábitos de vida saludables, y a promover su salud mental. La rehabilitación cardiaca requiere la total involucración y motivación del paciente, solo de esta manera se podrán promover hábitos saludables y mejorar la gestión y prevención de su enfermedad. Aunque la participación en los programas de rehabilitación cardiaca es baja, hoy en día existen programas de rehabilitación cardiaca que el paciente puede realizar en su casa. Estos suponen una solución prometedora para aumentar la participación. La rehabilitación cardiaca se considera una intervención integral donde los modelos de psicología de la salud son aplicados para promover un cambio en el estilo de vida de las personas así como para ayudarles a afrontar su propia enfermedad. Existen métodos para implementar cambios de hábitos y de aptitud, y también se considera muy relevante promover no solo el bienestar físico sino también el mental. Existen tecnologías que promueven los cambios de comportamientos en los seres humanos. En concreto, las tecnologías persuasivas y los sistemas de apoyo al cambio de comportamientos modelan las características, las estrategias y los métodos de diseño para promover cambios usando la tecnología. Pero estos modelos tienen algunas limitaciones: todavía no se ha definido que rol tienen las emociones en el cambio de comportamientos y como traducir los métodos de la psicología de la salud en la tecnología. Esta tesis se centra en tres elementos que tienen un rol clave en los cambios de hábitos y actitud: el estado físico, el estado mental, y la tecnología. -Estado de salud: un estado de salud critico puede modificar la actitud del ser humano respecto al cambio. A la vez un buen estado de salud hace que la necesidad del cambio sea menos percibida. -Estado emocional: la actitud tiene un componente afectivo. Los estados emocionales negativos pueden reducir la habilidad de una persona para adoptar nuevos comportamientos. La salud mental es la situación ideal donde los individuos tienen predisposición a los cambios. La tecnología puede ayudar a las personas a adoptar nuevos hábitos, así como a mantener una salud física y mental. Este trabajo de investigación se centra en el diseño de tecnologías para la mejora del estado físico y emocional de las personas. Se ha propuesto un marco de diseño llamado “Well.Be.Sign”. El marco se basa en tres aspectos: El marco teórico: representa los elementos que se tienen que definir para diseñar tecnologías para promover el bienestar de las personas. -El diagrama de influencia: presenta las fuerzas de ‘persuasión’ en el contexto de la salud. El rol de las tecnologías persuasivas ha sido contextualizado en una dimensión donde otros elementos influencian el usuario.  El proceso de diseño: describe el proceso de diseño utilizando una metodología iterativa e incremental que aplica una combinación de métodos de diseño existentes (Diseño Orientado a Objetivos, Diseño de Sistemas Persuasivos) así como elementos originales de este trabajo de investigación. Los métodos se han aplicados para diseñar un sistema que ofrezca un programa de tele-rehabilitación cardiaca. Inicialmente se ha diseñado un prototipo de acuerdo con las necesidades del usuario. En segundo lugar, el prototipo se ha extendido especificando la intervención requerida para al programa de rehabilitación cardiaca. Finalmente el sistema se ha desarrollado y validado en un ensayo clínico con grupo control, donde se observaron las variaciones del estado cardiovascular, el nivel de conocimiento acerca de la enfermedad, la percepción de la enfermedad, la persistencia de hábitos saludables, y la aceptabilidad del sistema. Los resultados muestran que el grupo de intervención tiene una superior capacidad cardiovascular, mejor conocimiento acerca de la enfermedad, y más percepción de control de la enfermedad. Asimismo, en algunos casos se ha registrado persistencia de los hábitos de ejercicios 6 meses después del uso del sistema. Otros dos estudios se han presentado para demonstrar la relevancia del estado emocional del usuario en el diseño de aplicaciones para la promoción del bienestar.  En personas con una grave enfermedad crónica como la insuficiencia cardiaca, donde se ha presentado las conexiones entre estado de salud y estado emocional. En el estudio se ensena la relaciones que tienen los síntomas y las emociones negativas y como un estado negativo emocional puede empeorar la condición física del paciente. -Personas con trastornos del humor: el estudio muestra como las emociones pueden tener un impacto en la percepción de la tecnología por parte del usuario. ABSTRACT Noncommunicable diseases (NCDs) cause the death of 38 million people every year. Four major NCDs are responsible for 82% of these deaths: cardio vascular disease, chronic respiratory disease, diabetes and cancer. These pandemic numbers are projected to raise to 53 million deaths in 2030, and for this reason the assembly of the World Health Organization (WHO) considers communicable diseases as an urgent need to be addressed. It is also a trend to advocate the adoption of mobile technology to deliver health services and to promote healthy behaviours among citizens, but adopting healthS promoting lifestyle is still a difficult task facing human tendencies. Within this context, there is a promising opportunity: persuasive technologies. These technologies are intentionally designed to change a person’s attitudes or behaviours; when applied in this context, than can be used to change health-related attitudes, beliefs, and behaviours. Each year there are 32 million heart attacks and strokes globally, of which about 12.5 million are fatal. Worldwide between 40 and 75% of all heart-attack victims die before reaching hospital. Avoiding a second heart attack by improving adherence to lifestyle and medication regimens has a cost saving potential of around €6 billion per year. In most of the cases the cardiovascular event has been provoked by unhealthy lifestyle. Furthermore, after an MI event the patient's decision to adopt or not healthier behaviour will influence the progress of the disease. Cardio-rehabilitation is an individualized program that follows a multidisciplinary approach to support the user to recover from the Myocardial Infarction, manage the Cardio Vascular Disease and the comorbidities, adopt healthy habits, and cope with any emotional distress. Cardio- rehabilitation requires patient participation and willingness to perform behavioral modifications and change the attitude toward the management and prevention of the disease. Participation in the Cardio Rehabilitation program is not high; the home-based rehabilitation program is a promising solution to increase participation. Nowadays cardio rehabilitation is considered a comprehensive intervention in which models of health psychology are applied to promote the behaviour change of the individuals. Relevant methods that have been successfully applied to foster healthy habits include the Health Belief Model and the Trans Theoretical Model. Studies also demonstrate the importance to promote not only the physical but also the mental well being of the individuals. The idea of also promoting behaviour change using technologies has been defined by the literature as persuasive technologies or behaviour change support systems, in which the features, the strategies and the design method have been modelled to foster the behaviour change using technology. Limitations have been found in this model: there is still research to be done on the role of the emotions and how psychological health intervention can be translated into computer methods. This research focuses on three elements that could foster behaviour change in individuals: the physical and emotional status of the person, and the technology. Every component can influence the user's attitude and behaviour in the following ways: ' Physical status: bad physical status could change human attitude toward the necessity to adopt health behaviours; at the same time, good health status reduces the need to adopt healthy habits. ' Emotional status: the attitude has an affective component, negative emotional state can reduce the ability of a person to adopt new behaviours, and mental well being is the ideal situation in which individuals have a predisposition to adopt healthy behaviours. ' Technology: it can help users to adopt new behaviours and can also be support to promote physical and emotional status. Following this approach the idea driven in this research is that technology that is designed to improve the physical status and the emotional status of the individual could better foster behaviour change. According to this principle, the Well.Be.Sign framework has been proposed. The framework is based on three views: ' The theoretical framework: it represents the patterns that have to be defined to design the technologies to promote well being. ' The influence diagram: it shows the persuasive forces in the context of health care. The role of the persuasive technologies is contextualized in a wider universe where other factors and persuasive forces influence a patient. ' The design process: it shows the process of design using an iterative, incremental methodology that applies a combination of existing methodologies (Goal Directed Design and Persuasive System Design) and others that are original to this research. The methods have been applied to design a system to deliver cardio rehabilitation at home: first a prototype has been defined according to the user’s needs, then it has been extended with the specific intervention required for the cardio–rehabilitation, finally the system has been developed and validated in a controlled clinical study in which the cardiovascular fitness, the level of knowledge, the perception of the illness, the persistence of healthy habits and the system acceptance (only the intervention group) were measured. The results show that the intervention group increased cardiovascular capacity, knowledge, feeling of control of illness and perceived benefits of exercise at the end of the study. After six months of the study, a followSup of the exercise habits was performed. Some individuals of the intervention group continued to be engaged in the running exercise sessions promoted in the designed system. Two other cases have been presented to demonstrate the foundations of the Well.Be.Sign’s approach to promote both physical and emotional status: ' People affected by Heart Failure, in which a bidirectional connection between health status and emotions has been discussed with patients. Two correlations were demonstrated: the relationship between symptoms and negative emotional response, and that negative emotional status is correlated with worsening of chronic conditions. ' People with mood disorders: the study shows that emotions could also impact how the user perceives the technology.