Combining data-driven and domain knowledge components in an intelligent assistant to build personalized menus

In this paper, some new components that have been integrated in the Diet4You system for the generation of nutritional plans are introduced. Negative user preferences have been modelled and introduced in the system. Furthermore, the cultural eating styles originated from the location where the user lives have been taken into account dividing the original menu plan in sub-plans. Each sub-plan is in charge to optimize one of the meals of one day in the personal menu of the user. The main latent reasoning mechanism used is case-based reasoning, which reuses previous menu configurations according to the nutritional plan and the corresponding hard constraints and the user preferences to meet a personalized recommendation menu for a given user. It uses the cognitive analogical reasoning technique in addition to ontologies, nutritional databases and expert knowledge. The preliminary results with some examples of application to test the new contextual components have been very satisfactory according to the evaluation of the experts.This work has been partially supported by the project Diet4You (TIN2014-60557-R), the Spanish Thematic Network MAPAS [TIN2017-90567-REDT (MINECO/FEDER EU)], and the Consolidated Research Group Grant from AGAUR (Generalitat de Catalunya) IDEAI-UPC (AGAUR SGR2017-574).Peer ReviewedPostprint (author's final draft

Development of a Framework for Ontology Population Using Web Scraping in Mechatronics

One of the major challenges in engineering contexts is the efficient collection, management, and sharing of data. To address this problem, semantic technologies and ontologies are potent assets, although some tasks, such as ontology population, usually demand high maintenance effort. This thesis proposes a framework to automate data collection from sparse web resources and insert it into an ontology. In the first place, a product ontology is created based on the combination of several reference vocabularies, namely GoodRelations, the Basic Formal Ontology, ECLASS stan- dard, and an information model. Then, this study introduces a general procedure for developing a web scraping agent to collect data from the web. Subsequently, an algorithm based on lexical similarity measures is presented to map the collected data to the concepts of the ontology. Lastly, the collected data is inserted into the ontology. To validate the proposed solution, this thesis implements the previous steps to collect information about microcontrollers from three differ- ent websites. Finally, the thesis evaluates the use case results, draws conclusions, and suggests promising directions for future research

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Supporting Automatic Interoperability in Model-Driven Development Processes

By analyzing the last years of software development evolution, it is possible to observe that the involved technologies are increasingly focused on the definition of models for the specification of the intended software products. This model-centric development schema is the main ingredient for the Model-Driven Development (MDD) paradigm. In general terms, the MDD approaches propose the automatic generation of software products by means of the transformation of the defined models into the final program code. This transformation process is also known as model compilation process. Thus, MDD is oriented to reduce (or even eliminate) the hand-made programming, which is an error-prone and time-consuming task. Hence, models become the main actors of the MDD processes: the models are the new programming code. In this context, the interoperability can be considered a natural trend for the future of model-driven technologies, where different modeling approaches, tools, and standards can be integrated and coordinated to reduce the implementation and learning time of MDD solutions as well as to improve the quality of the final software products. However, there is a lack of approaches that provide a suitable solution to support the interoperability in MDD processes. Moreover, the proposals that define an interoperability framework for MDD processes are still in a theoretical space and are not aligned with current standards, interoperability approaches, and technologies. Thus, the main objective of this doctoral thesis is to develop an approach to achieve the interoperability in MDD processes. This interoperability approach is based on current metamodeling standards, modeling language customization mechanisms, and model-to-model transformation technologies. To achieve this objective, novel approaches have been defined to improve the integration of modeling languages, to obtain a suitable interchange of modeling information, and to perform automatic interoperability verification.Giachetti Herrera, GA. (2011). Supporting Automatic Interoperability in Model-Driven Development Processes [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11108Palanci

A Global Data Ecosystem for Agriculture and Food

Agriculture would benefit hugely from a common data ecosystem. Produced and used by diverse stakeholders, from smallholders to multinational conglomerates, a shared global data space would help build the infrastructures that will propel the industry forward. In light of growing concern that there was no single entity that could make the industry-wide change needed to acquire and manage the necessary data, this paper was commissioned by Syngenta with GODAN’s assistance to catalyse consensus around what form a global data ecosystem might take, how it could bring value to key players, what cultural changes might be needed to make it a reality and finally what technology might be needed to support it. This paper looks at the challenges and principles that must be addressed in in building a global data ecosystem for agriculture. These begin with building incentives and trust: amongst both data providers and consumers: in sharing, opening and using data. Key to achieving this will be developing a broad awareness of, and making efforts to improve, data quality, provenance, timeliness and accessibility. We set out the key global standards and data publishing principles that can be followed in supporting this, including the ‘Five stars of open data’ and the ‘FAIR principles’ and offer several recommendations for stakeholders in the industry to follow

Machine learning techniques implementation in power optimization, data processing, and bio-medical applications

The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for demand side management of electric water heaters using Q-learning and action-dependent heuristic dynamic programming. The implemented approaches provide an efficient load management mechanism that reduces the overall power cost and smooths grid load profile. The second paper implements an ensemble statistical and subspace-clustering model for analyzing the heterogeneous data of the autism spectrum disorder. The paper implements a novel k-dimensional algorithm that shows efficiency in handling heterogeneous dataset. The third paper provides a unified learning model for clustering neuroimaging data to identify the potential risk factors for suboptimal brain aging. In the last paper, clustering and clustering validation indices are utilized to identify the groups of compounds that are responsible for plant uptake and contaminant transportation from roots to plants edible parts --Abstract, page iv

Conceptual modelling for integrated decision-making in process systems

This Thesis addresses the systematic construction of Decision Making Models (DMMs) from the conceptualization stage to its application in specific situations, with special emphasis on !he treatment of scenarios where there is a hierarchy of decision levels, common in the Process Systems (PS). Although the methodologies developed are generic, the scope of this Thesis is limited to the perspective of Process Engineering. The central component required to construct a DMM is the conceptual description of the reality, which supports the system alisation of management procedures . During this description, two different dom ains can be identified: the PS Domain, useful to describe the structure of the process as such (physical reality and the way in which its elements are related), and the Management Domain, identified in this Thesis as associated with the Conceptual Constraints (CC) that describe the restrictions associated with the management of the process . In this way, the PS Domain includes concepts and relationships that appear in the control standards of the process followed by the company: the description of the process to be developed, the description of the physical equipment in which it is developed , and that of its interactions, giving rise to the control of the execution of the procedures; this domain should allow managing the construction, design, operation and control of any manufacturing system. On the other hand, the CC Domain contains the information associated with the concepts and relationships that m ust be fulfilled to ensure a coherent set of decisions, with the purpose of identifying and representing the systematics to follow during the decision-making process, giving rise to the conceptual representation of this system and, finally, the construction of the corresponding DMM. The first challenge addressed in this thesis is associated with the systematisation of conceptual modelling from semantic information, for the construction ofontologies from textual sources and a procedure to verify the interna! coherence of lhese sources. The application of this methodology has been used for the identification of the essential concepts and relationships in the PS Domain, allowing creating a generic, common and shared model, unlike the existing models. In the next step, this PS Domain has been used to solve management problems in systems that comprise multi-level hierarchies. The resulting decision-making process allows integrating the decisions made al each level, ensuring their consistency from an approach that simultaneously considers the management of all available information (data and knowledge). On the other hand, the introduction of the necessary concepts and relationships to ensure the feasibility of the process management decisions, through the CC Domain, allows the development of systematic DMM creation procedures: this domain classifies the constrains (balances, sequence, etc.), adds abstrae! elements to them (e.g.: produced and consumed amounts) and allows to generalize the relation of its compone nis with the information associated to the PS Domain. The last part of this Thesis deals with the integration of the PS and CC Domains, and their application for the generation of new decision-making systems . For this, algorithms have been designed that, starting from the previously identified and classified restrictions, and patterns of DMMs also previously identified from existing cases, exploit the information available through the instances in the PS Domain, to generate new DMMs according to the user's specifications. lts use is illustrated through cases from different environments, demonstrating the generalisation capacity of the created systematics.Esta Tesis aborda la construcción sistemática de Modelos para la toma de Decisiones (DMMs) desde la etapa de conceptualización hasta su aplicación en situaciones concretas, con especial énfasis en el tratamiento de escenarios en los que existe una jerarquía de niveles de decisión, habitual en la Industria de Proceso (PS). Aunque las metodologías desarrolladas son genéricas, el alcance de esta Tesis se limita a la perspectiva de la Ingeniería de Procesos. El componente central requerido para construir un DMMs es la descripción conceptual de la realidad a la que se orienta, que a su vez respalda la sistematización de los procedimientos de gestión. Durante esta descripción, se pueden identificar planteamientos asociados a dos dominios diferentes: el Dominio del Proceso (PS), útil para describir la estructura del proceso como tal (realidad física y forma en la que se relacionan sus elementos), y el Dominio de Gestión, asociado a las Restricciones Conceptuales (CC) que describen las restricciones asociadas a la gestión del proceso. El Dominio PS incluye conceptos y relaciones que aparecen en los estándares de control del proceso que sigue la empresa: la descripción del proceso a desarrollar, la descripción de los equipos físicos en los que se desarrolla, y la de sus interacciones, que dan lugar al control de ejecución de los procedimientos; este dominio debe permitir la construcción, el diseño, la operación y el control de cualquier sistema de fabricación. Por su parte, el Dominio CC contiene la información asociada a los conceptos y las relaciones que deben cumplirse para asegurar un conjunto coherente de decisiones, con el propósito de identificar y representar la sistemática a seguir durante el proceso de toma de decisiones, dando lugar a la representación conceptual de esta sistemática y, finalmente, a la construcción del correspondiente DMM. El primer reto abordado en esta Tesis está asociado a la sistematización del modelado conceptual a partir de información semántica, para construcción de ontologías a partir de fuentes textuales y de un procedimiento para verificar la coherencia interna de dichas fuentes. La aplicación de esta metodología se ha utilizado para la identificación de los conceptos y las relaciones esenciales en el Dominio PS, permitiendo crear un modelo genérico, común y compartido, a diferencia de los modelos existentes. En el siguiente paso, este Dominio PS se ha utilizado para la resolución de problemas de gestión en sistemas que comprenden múltiples niveles de jerarquías funcionales. El proceso de toma de decisiones resultante permite integrar las decisiones tomadas en cada nivel, asegurando su coherencia a partir de un enfoque que contempla simultáneamente la gestión de toda la información disponible (datos y conocimiento). Por su parte, la introducción de los conceptos y relaciones necesarios para asegurar la factibilidad de las decisiones de gestión del proceso, a través del Dominio CC, permite el desarrollo de procedimientos sistemáticos de creación de DMMs: este Dominio clasifica las restricciones (balances, secuencia, etc.), agrega elementos abstractos a dichas restricciones (p.e.: cantidad producida y consumida) y permite generalizar la relación de sus componentes con la información asociada al Dominio PS. En la última parte de esta Tesis se aborda la integración de los Dominios PS y CC, y su aplicación para la generación de nuevos sistemas de toma de decisiones. Para ello, se han diseñado algoritmos que, partiendo de las restricciones anteriormente identificadas y clasificadas, y patrones de DMMs también previamente identificados a partir de casos ya existentes, explotan la información disponible a través de las instancias del Dominio PS, para generar de nuevos modelos de toma de decisión de acuerdo con las especificaciones del usuario. Su utilización se ilustra a través de casos procedentes de diferentes entornos, demostrando la capacidad de generalización de la sistemática creada.Postprint (published version

ERAWATCH Country Reports 2013: Spain

The Analytical Country Reports analyse and assess in a structured manner the evolution of the national policy research and innovation in the perspective of the wider EU strategy and goals, with a particular focus on the performance of the national research and innovation (R&I) system, their broader policy mix and governance. The 2013 edition of the Country Reports highlight national policy and system developments occurring since late 2012 and assess, through dedicated sections: -National progress in addressing Research and Innovation system challenges; -National progress in addressing the 5 ERA priorities; -The progress at Member State level towards achieving the Innovation Union; -The status and relevant features of Regional and/or National Research and Innovation Strategies on Smart Specialisation (RIS3); -As far relevant, country Specific Research and Innovation (R&I) Recommendations. Detailed annexes in tabular form provide access to country information in a concise and synthetic manner. The reports were originally produced in December 2013, focusing on policy developments occurring over the preceding twelve months.JRC.J.2-Knowledge for Growt

Knowledge Accumulation of Microbial Data Aiming at a Dynamic Taxonomic Framework

Deze thesis is een poging om precies dit onderzoeksgebied te overbruggen dat ligt tussen ruw gegeven en abstract concept, tussen praktijk en theorie, binnen het kader van de hedendaagse bacteriële taxonomie. Als gevolg hiervan is het een kruisbestuiving geworden tussen microbiologie, wiskunde en computerwetenschappen. De kunst om het landschap van de bacteriële diversiteit uit te tekenen, gebruikt als een metafoor voor het modelleren van de taxonomie, vereist het bepalen van een representatieve waaier aan reproduceerbare en vergelijkbare experimentele kenmerken van een verzameling bacteriën (microbiologie/taxonomie), het ontwerpen en implementeren van objectieve classificatiemethodes voor het groeperen van gegevens op een niet gecoördineerde manier (wiskunde/classificatie) en het consolideren van experimentele gegevens en hun verschillende onderverdelingen via een uniforme en weldoordachte aanpak (computerwetenschappen/kennisbeheer). Men kan zich gemakkelijk een globaal kennissysteem voor de geest halen dat de vellen vol experimentele gegevens die voortspruiten uit de microbiologische onderzoeksverrichtingen op een gestructureerde en geüniformiseerde manier kan absorberen. Een dergelijk kennisbeheersysteem zou een ongelofelijke vooruitgang betekenen voor de mogelijke toepassing van intelligente en goed gefundeerde methodes voor het ontginnen van de gegevens, ingezet als hulpmiddel om het afbakenen van objectieve en universele taxonomische consensusmodellen op een betere manier te stroomlijnen en te automatiseren. Bovendien kunnen dergelijke inferentiesystemen in staat worden geacht om ogenblikkelijk te reageren op een toevloed van nieuwe gegevens en interactief te communiceren met de buitenwereld indien noodzakelijke stukken voor het vervolledigen van de taxonomische puzzel zouden ontbreken. De geldigheid van nieuwe inzichten of hypothesen omtrent het leven en de evolutie van bacteriën zou onmiddellijk kunnen getoetst worden aan deze vergaarbakken vol kennis, mogelijks met een directe aanpassing van bestaande taxonomische modellen tot gevolg. Vooraleer de betrachtingen van een autodidactisch inferentiesysteem voor het uittekenen van het landschap van de bacteriële diversiteit kunnen gerealiseerd worden, moeten belangrijke technische en organisatorische hindernissen overwonnen worden. Dit vraagt het verleggen van de grenzen van een mondiale uitwisseling van gegevens, het nasporen en invullen van de hiaten in de waarnemingen, en het verkennen van de mogelijkheden van nieuwe technieken voor het ontginnen van gegevens, ten voordele van een beter inzicht in het leven en de evolutie van bacteriën. Spijts de nog vele onopgeloste kwesties, kunnen de ideeën die worden aangebracht in deze verhandeling als stimulans en leidraad dienen bij het integreren en exploiteren van microbiële gegevens, in plaats van het blijvend koesteren van een ijdele hoo