Natural Language Interfaces for Querying and Retrieving Information from Ontology-based Knowledge Bases

Tesis doctoral titulada “Interfaces de lenguaje natural para la consulta y recuperación de información de bases de conocimiento basadas en ontologías ", defendida por Mario Andrés Paredes Valverde en la Universidad de Murcia y elaborada bajo la dirección de los doctores Rafael Valencia García (Universidad de Murcia) y Miguel Ángel Rodríguez García (King Abdullah University of Science & Technology). La defensa tuvo lugar el 23 de mayo de 2017 ante el tribunal formado por los doctores Juan Miguel Gómez Berbís (Presidente, Universidad Carlos III de Madrid), Francisco García Sánchez (Secretario, Universidad de Murcia) y la doctora Catalina Martínez Costa (Vocal, Medical University of Graz) y la tesis obtuvo la mención Cum Laude y Doctor Internacional.Ph.D. thesis entitled “Natural language interfaces for querying and retrieving information from ontology-based knowledge bases” written by Mario Andrés Paredes Valverde at the University of Murcia under the supervision of the Ph.D. Rafael Valencia García (University of Murcia) and Ph.D. Miguel Ángel Rodríguez García (King Abdullah University of Science & Technology). The viva voice was held on the 23rd May 2017 and the members of the commission were the Ph.D. Juan Miguel Gómez Berbís (President, University Carlos III of Madrid), Ph.D. Francisco García Sánchez (Secretary, University of Murcia) and Ph.D. Catalina Martínez Costa (Vocal, University of Graz) and the thesis obtained the mention Cum Laude and International Doctor

An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico

This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitanThis paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabita

Instant messaging-based dialog system for device control in the Internet of things

La finalidad del proyecto im4Things es el desarrollo de una herramienta que proporcione una interfaz de comunicación entre humanos y dispositivos en la Internet de las cosas mediante diálogo en lenguaje natural escrito a través de servicios de mensajería instantánea. Esta comunicación puede ser de distintos tipos tales como el envío de órdenes, la consulta del estado e incluso se permite que sean los mismos dispositivos los encargados de alertar al usuario, si se ha producido un cambio del estado en los sensores de los dispositivos. Este proyecto está siendo desarrollado conjuntamente por la empresa Proasistech y el grupo TECNOMOD de la Universidad de Murcia y ha sido financiado por los fondos propios de la empresa Proasistech y con un contrato de I+D+i de asesoría tecnológica con el citado grupo de la Universidad de Murcia.The im4Things project aims to develop a communication interface to devices on the Internet of the Things (IoT) through intelligent dialogue based on written natural language over instant messaging services. This communication can be established in different ways such as order sending, status querying and even the devices themselves are responsible for alert users when a change has been produced in the devices sensors. This project is being developed by Proasistech company in cooperation with the TECNOMOD research group of the University of Murcia and it has been funded by equity capital of Proasistech company and by an R&D&i technology consulting contract with the aforementioned University of Murcia research group.Este trabajo ha sido financiado por la empresa Proasistech (http://www.proasistech.com/) a través de sus fondos propios

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an -measure of 88.7% considering the complete dataset.publishedVersio

KBS4FIA: Sistema inteligente basado en conocimiento para análisis de información financiera

Decision making takes place in an environment of uncertainty. Therefore, it is necessary to have information which is as accurate and complete as possible in order to minimize the risk that is inherent to the decision-making process. In the financial domain, the situation becomes even more critical due to the intrinsic complexity of the analytical tasks within this field. The main aim of the KBS4FIA project is to automate the processes associated with financial analysis by leveraging the technological advances in natural language processing, ontology learning and population, ontology evolution, opinion mining, the Semantic Web and Linked Data. This project is being developed by the TECNOMOD research group at the University of Murcia and has been funded by the Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF) through the Spanish National Plan for Scientific and Technical Research and Innovation Aimed at the Challenges of Society.La toma de decisiones tiene lugar en un ambiente de incertidumbre, por lo tanto es necesario disponer de información lo más exacta y completa posible para minimizar el riesgo inherente al proceso de toma de decisiones. En el dominio de las finanzas la situación se hace, si cabe, aún más crítica debido a la complejidad intrínseca de las tareas analíticas dentro de este campo. La finalidad del proyecto KBS4FIA es la automatización de los procesos ligados al análisis financiero, utilizando para ello tecnologías asociadas con el procesamiento del lenguaje natural, el aprendizaje, la instanciación y la evolución de ontologías, la minería de opiniones, la Web Semántica y el Linked Data. Este proyecto está siendo desarrollado por el grupo TECNOMOD de la Universidad de Murcia y ha sido financiado por el Ministerio de Economía y Competitividad y el Fondo Europeo de Desarrollo Regional (FEDER) a través del Programa Estatal de I+D+i Orientada a los Retos de la Sociedad.This project has been funded by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER / ERDF) through project KBS4FIA (TIN2016-76323-R)

IXHEALTH: An advanced speech recognition system to interact with healthcare information systems

El objetivo del proyecto IXHEALTH es desarrollar una plataforma multilingüe basada en reconocimiento del habla que permita a profesionales de la salud llevar a cabo tareas tales como la redacción de informes médicos, así como interactuar con sistemas de información sanitarios mediante comandos de voz. Todo ello, bajo un mecanismo de seguridad basado en biometría de voz que evite que personas no autorizadas editen información sensible gestionada por este tipo de sistemas. Este proyecto ha sido desarrollado por la empresa VOCALI en conjunto con el grupo de investigación TECNOMOD de la Universidad de Murcia, y financiado por el Instituto de Fomento de la Región de Murcia.The IXHEALTH project aims to develop a multilingual platform based on speech recognition that allows healthcare professionals to perform transcription and dictation activities for the generation of medical reports, as well as to interact with healthcare information systems by means of voice commands. These tasks are performed through a biometric voice-based security mechanism that avoids non-allowed users to edit sensitive data managed by this kind of systems. This project has been developed by the VOCALI enterprise in conjunction with the TECNOMOD research group from the University of Murcia, and it has been founded by the Institute of Promotion from the Region of Murcia.Este trabajo ha sido financiado por el Instituto de fomento de la Región de Murcia (Ref. 2015.08.ID+I.0011

Interfaces del lenguaje natural para la consulta y recuperación de información de bases de conocimiento basadas en ontologías

Objetivos. Existe un exponencial crecimiento de información disponible en bases de conocimiento semánticas, sin embargo, el acceso a este tipo de información requiere experiencia en el uso de lenguajes formales de consulta y el conocimiento de la estructura de datos de la base de conocimiento subyacente. El objetivo principal de esta tesis es la investigación y desarrollo de soluciones basadas en tecnologías de procesamiento de lenguaje natural y Web Semántica que permitan reducir la brecha existente entre el usuario y las bases de conocimiento a través de lenguaje natural. Metodología. La metodología seguida durante el desarrollo de esta tesis se divide en cuatro partes principales. (1) Estudio del arte que permita conocer todos aquellos desarrollos de última tecnología realizados en los contextos de Web Semántica, procesamiento de lenguaje natural e interfaces de lenguaje natural. (2) La propuesta de solución se formaliza en tres elementos: (a) una ontología para la descripción de la estructura sintáctica de la pregunta, así como de su contexto en términos de la base de conocimiento del dominio; (b) Proceso de análisis de la pregunta basado en análisis de dependencias sintáctica; y (c) Proceso de generación de consultas SPARQL a partir de la representación semántica de la pregunta. (3) Implementación de la solución propuesta por medio de herramientas de procesamiento de lenguaje natural y Web Semántica. (4) Validación de las soluciones propuestas utilizando bases de conocimiento basadas en Linked Data. En concreto, Dbpedia, un esfuerzo comunitario por extraer conocimiento estructurado de Wikipedia; y MusicBrainz, una fuente de información ampliamente utilizadas en el dominio de la música. Resultados. Como resultados se obtiene: (a) Una ontología que permite describir semánticamente la estructura sintáctica de la pregunta y el contexto de ella en términos de la base de conocimiento del dominio. (b) Una clasificación de preguntas y respuestas adaptada al contexto de bases de conocimiento. (c) Un método para la representación semántica de la pregunta en lenguaje natural basada en el análisis sintáctico de dependencias. (d) Un conjunto de plantillas RDF que permiten la generación de consultas SPARQL a partir de la representación semántica de la pregunta. (e) Una interfaz de lenguaje natural que permite la obtención de respuesta de bases de conocimiento semánticas a partir de preguntas expresadas en lenguaje natural. Esta interfaz implementa cada uno de los resultados antes mencionados. Conclusiones. Las bases de conocimiento basadas en ontologías han sido adoptadas por individuos y organizaciones de múltiples dominios debido a que, entre otras cosas, permiten asignar a la información un significado bien definido que puede ser entendido tanto por humanos como por computadoras. Sin embargo, el acceso a estas bases de conocimiento sigue siendo un reto para una gran parte de los usuarios pues demanda el conocimiento de tecnologías de la Web Semántica, lenguajes formales de consulta, así como la estructura de datos de dicha fuente de información. Utilizar un enfoque basado en lenguaje natural permite al usuario utilizar el lenguaje que ya tiene en vez de verse forzado a utilizar un lenguaje poco natural o limitado. Las herramientas desarrolladas han demostrado ser efectivas en la obtención de información de bases de conocimiento semánticas a partir de preguntas expresadas en lenguaje natural, contribuyendo así, a reducir la brecha existente entre el usuario y este tipo de bases de conocimiento. Aims of the thesis. A lot of individuals and organizations from different domains have adopted the ontology-based approach to publish their information. This has led to an exponential growth of information available on the Web and intranets represented by RDF. Nowadays, the access to this kind of information is performed by using formal query language such as SPARQL. However, this approach is complicated for casual users because of the necessity of learning formal query language of the functioning of graphical interfaces, and even, of knowing the underlying knowledge base structure. This thesis aims to provide natural language-based solutions for reducing the gap between ontology-based knowledge bases and users. Methodology. The methodology followed in this research is decomposed on next main tasks. (1) Analysis of the state of art, which involves studying the technologies used in this research, namely, Semantic We, Natural Language Processing, and Natural Language Interfaces. (2) Formalization of three elements: (a) an ontology for representing the syntactic structure and context of the natural language question; (b) Question’s analysis process based on the dependence analysis; and (c) Generation of SPARQL-based queries based on the semantic representation of the question. (3) Implementation of the proposal by means of natural language processing and Semantic Web technologies. (4) Validation of the proposal on Linked Data based knowledge bases, namely, DBpedia and MusicBrainz. Results. The main contributions of this work are: (a) An ontology for representing the syntactic structure and context of the natural language question. (b) Question and answer classification adapted to the context of ontology-based knowledge bases. (c) A method for the representing the natural language question based on the dependence analysis. (d) A set of RDF-based templates that are the basis for the generation of graph pattern that constitute the SPARQL-based query. (e) A natural language interface that allows obtaining information from semantic knowledge bases. This interface implements all contributions above. Conclusions. The semantic knowledge bases have been adopted by individuals and organizations of different domains. However, current mechanisms for accessing this type of knowledge bases are intended to be used by users with knowledge and expertise on Semantic Web technologies. Natural language solutions allow users to use all communicative power of language that they already possess instead of being forced to use an unnatural and limited mode of communication. Furthermore, NLIs hide from users the formality of a knowledge base as well as the formal query language. The developed tools have demonstrated their effectiveness in providing information (domain knowledge base resources) that answers the user’s question. Therefore, we have presented our effort for reducing the gap between ontology-based knowledge bases and users

Developing Social Networks Mashups: An Overview of REST-Based APIs

AbstractThe social networks have become in a powerful diffusion media in several fields such as communication, e-commerce and entertainment. However, the development of new applications that combine the functionality of different social networks with the purpose of providing added-value to users is not very common. In this context, a new kind of applications called mashups has emerged. A mashup is a web application that integrates data from multiple web sources in order to provide a unique service. Internal data sources, RSS/Atom feeds, Screen-Scraping and Web Services are some resources used by mashups. Nowadays, most of Web Services provided by social networks use the REST-based architectural style because it offers significant advantages in comparison with other technologies. The contribution of this paper is a review of REST-based APIs for the development of mashups that integrate well known social networks such as Youtube©, Picasa©, and Flickr©, among others. In addition, a set of 4 mashups were developed combining the APIs discussed. Also, this work provides a development guide to perform tasks such as extraction and combination from different data sources, as well as leads to the emergence of new ideas for developing web applications

Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain

Internet of Things (IoT) technologies can greatly benefit from machine-learning techniques and artificial neural networks for data mining and vice versa. In the agricultural field, this convergence could result in the development of smart farming systems suitable for use as decision support systems by peasant farmers. This work presents the design of a smart farming system for crop production, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the cloud. Moreover, a new data-mining method exploiting climate data along with crop-production data is proposed for the prediction of production volume from heterogeneous data sources. This method was initially validated using traditional machine-learning techniques and open historical data of the northeast region of the state of Puebla, Mexico, which were collected from data sources from the National Water Commission and the Agri-food Information Service of the Mexican Government

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management