Herramienta para el análisis y anotación de conocimiento pragmático para modelos de diálogo

Este proyecto se engloba dentro del marco del modelo de diálogo y tiene como objetivo desarrollar una herramienta que facilite la anotación del conocimiento pragmático de corpus. La finalidad que se persigue es la implementación de una herramienta fácil de manejar e intuitiva que permita a usuarios expertos en pragmática llevar a cabo todas las fases de las que consta el análisis individual de una muestra de diálogo. La herramienta estará orientada a la anotación de muestras de diálogo de modo independiente, si bien parte del conocimiento anotado puede ser reutilizado en sucesivas anotaciones. Esto quiere decir que la herramienta Cognos.Dial se encarga de facilitar el análisis individual de cada muestra de diálogo y de almacenar la información recogida para cada uno de los análisis en una base de conocimiento. Sin embargo, este conocimiento no puede ser utilizado directamente por el modelo de diálogo, sino que se requiere realizar una unificación del conocimiento individual. Para que el conocimiento anotado pueda ser utilizado por el modelo de diálogo, es necesario que sobre las muestras individuales analizadas se apliquen algoritmos de unificación (tanto de tareas como de segmentos) y un algoritmo de aprendizaje para el compromiso. El conocimiento unificado se almacenará en una base de conocimiento global con el propósito de ser utilizada en el modelo de diálogo. Este conjunto de funciones que operan globalmente sobre la totalidad de las muestras de un corpus quedan fuera del ámbito del proyecto pero serán implementadas en un futuro próximoIngeniería en Informátic

An ontology for human-like interaction systems

This report proposes and describes the development of a Ph.D. Thesis aimed at building an ontological knowledge model supporting Human-Like Interaction systems. The main function of such knowledge model in a human-like interaction system is to unify the representation of each concept, relating it to the appropriate terms, as well as to other concepts with which it shares semantic relations. When developing human-like interactive systems, the inclusion of an ontological module can be valuable for both supporting interaction between participants and enabling accurate cooperation of the diverse components of such an interaction system. On one hand, during human communication, the relation between cognition and messages relies in formalization of concepts, linked to terms (or words) in a language that will enable its utterance (at the expressive layer). Moreover, each participant has a unique conceptualization (ontology), different from other individual’s. Through interaction, is the intersection of both part’s conceptualization what enables communication. Therefore, for human-like interaction is crucial to have a strong conceptualization, backed by a vast net of terms linked to its concepts, and the ability of mapping it with any interlocutor’s ontology to support denotation. On the other hand, the diverse knowledge models comprising a human-like interaction system (situation model, user model, dialogue model, etc.) and its interface components (natural language processor, voice recognizer, gesture processor, etc.) will be continuously exchanging information during their operation. It is also required for them to share a solid base of references to concepts, providing consistency, completeness and quality to their processing. Besides, humans usually handle a certain range of similar concepts they can use when building messages. The subject of similarity has been and continues to be widely studied in the fields and literature of computer science, psychology and sociolinguistics. Good similarity measures are necessary for several techniques from these fields such as information retrieval, clustering, data-mining, sense disambiguation, ontology translation and automatic schema matching. Furthermore, the ontological component should also be able to perform certain inferential processes, such as the calculation of semantic similarity between concepts. The principal benefit gained from this procedure is the ability to substitute one concept for another based on a calculation of the similarity of the two, given specific circumstances. From the human’s perspective, the procedure enables referring to a given concept in cases where the interlocutor either does not know the term(s) initially applied to refer that concept, or does not know the concept itself. In the first case, the use of synonyms can do, while in the second one it will be necessary to refer the concept from some other similar (semantically-related) concepts...Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaSecretario: Inés María Galván León.- Secretario: José María Cavero Barca.- Vocal: Yolanda García Rui

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies

A Scalable Machine Learning Online Service for Big Data Real-Time Analysis

Proceedings of: IEEE Symposium Series on Computational Intelligence (SSCI 2014). Orlando, FL, USA, December 09-12, 2014.This work describes a proposal for developing and testing a scalable machine learning architecture able to provide real-time predictions or analytics as a service over domain-independent big data, working on top of the Hadoop ecosystem and providing real-time analytics as a service through a RESTful API. Systems implementing this architecture could provide companies with on-demand tools facilitating the tasks of storing, analyzing, understanding and reacting to their data, either in batch or stream fashion; and could turn into a valuable asset for improving the business performance and be a key market differentiator in this fast pace environment. In order to validate the proposed architecture, two systems are developed, each one providing classical machine-learning services in different domains: the first one involves a recommender system for web advertising, while the second consists in a prediction system which learns from gamers' behavior and tries to predict future events such as purchases or churning. An evaluation is carried out on these systems, and results show how both services are able to provide fast responses even when a number of concurrent requests are made, and in the particular case of the second system, results clearly prove that computed predictions significantly outperform those obtained if random guess was used.This research work is part of Memento Data Analysis project, co-funded by the Spanish Ministry of Industry, Energy and Tourism with identifier TSI-020601-2012-99.Publicad

An Efficient and Scalable Recommender System for the Smart Web

This proceeding at: 11th International Conference on Innovations in Information Technology (IIT) Innovations 2015. Special Theme: Smart Cities, Big Data, Sustainable Development. Took place at 2015, November, 01 - 03, in Dubai, United Arab Emirates (IEEE IIT 2015).This work describes the development of a web recommender system implementing both collaborative filtering and content-based filtering. Moreover, it supports two different working modes, either sponsored or related, depending on whether websites are to be recommended based on a list of ongoing ad campaigns or in the user preferences. Novel recommendation algorithms are proposed and implemented, which fully rely on set operations such as union and intersection in order to compute the set of recommendations to be provided to end users. The recommender system is deployed over a real-time big data architecture designed to work with Apache Hadoop ecosystem, thus supporting horizontal scalability, and is able to provide recommendations as a service by means of a RESTful API. The performance of the recommender is measured, resulting in the system being able to provide dozens of recommendations in few milliseconds in a single-node cluster setup.This research work is part of Memento Data Analysis project, co-funded by the Spanish Ministry of Industry, Energy and Tourism with no. TSI-020601-2012-99 and TSI-020110-2009-137.Publicad

Semantic Similarity Measures Applied to an Ontology for Human-Like Interaction

The focus of this paper is the calculation of similarity between two concepts from an ontology for a Human-Like Interaction system. In order to facilitate this calculation, a similarity function is proposed based on five dimensions (sort, compositional, essential, restrictive and descriptive) constituting the structure of ontological knowledge. The paper includes a proposal for computing a similarity function for each dimension of knowledge. Later on, the similarity values obtained are weighted and aggregated to obtain a global similarity measure. In order to calculate those weights associated to each dimension, four training methods have been proposed. The training methods differ in the element to fit: the user, concepts or pairs of concepts, and a hybrid approach. For evaluating the proposal, the knowledge base was fed from WordNet and extended by using a knowledge editing toolkit (Cognos). The evaluation of the proposal is carried out through the comparison of system responses with those given by human test subjects, both providing a measure of the soundness of the procedure and revealing ways in which the proposal may be improved.The development of this approach and its construction as part of the LaBDA-Interactor Human-Like Interaction System, part of the research projects SemAnts (TSI-020110-2009-419) and THUBAN (TIN2008-02711) and CADOOH (TSI-020302-2011-21), is supported by the Spanish Ministry of Industry, Tourism and Commerce and the Spanish Ministry of Education, respectively. Besides, the knowledge bases were populated using the COGNOS toolkit developed through the research project MA2VICMR (S2009/TIC-1542) supported by the Regional Government of Madrid.Publicad

ECMO for COVID-19 patients in Europe and Israel

Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies

Cognos: una herramienta para la anotación de corpus orientada al conocimiento pragmático

En este trabajo se describen algunas herramientas para la anotación de corpus orientadas al conocimiento pragmático. Entre estas herramientas se encuentra Cognos Toolkit: un conjunto de herramientas y aplicaciones que facilitan el análisis lingüístico, la anotación, formalización y gestión del corpus de interacción humana adquirido. Las herramientas de anotación pueden abordar distintas capas del análisis, centrándose Cognos Toolkit en el lenguaje natural, actos comunicativos, segmentos, intenciones, contexto y ejecución de tareas durante la ejecución. Cognos Toolkit es independiente de la plataforma y cuenta con una base conocimientos y una interfaz gráfica e intuitiva. Además, la exportación e importación de muestras individuales en ficheros XML permite reutilizar y compartir el conocimiento pragmático anotado.This paper describes some corpus annotation tools focused in pragmatic knowledge. They are part of the Cognos Toolkit: a set of tools and applications for assisting the analysis, annotation, formalization and management of interactive corpus acquired from human interactions. Annotation takes place on multiple layers, yet the presented here is focused on natural language, communicative acts, segments, intentions, context and tasks developed through the interaction. Cognos Toolkit is platform-independent, database supported, and endowed with an intuitive graphical user interface. It also enables the exportation of pragmatically annotated dialogues to XML files which extends the annotations reusability and sharing