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September 2011 - present
Publications
Publications (197)
Bayesian Networks (BNs) are probabilistic graphical models used to represent variables and their conditional dependencies, making them highly valuable in a wide range of fields, such as radiology, agriculture, neuroscience, construction management, medicine, and engineering systems, among many others. Despite their widespread application, the reusa...
In this paper, we propose a model for building natural language explanations for Bayesian Network Reasoning in terms of factor arguments, which are argumentation graphs of flowing evidence, relating the observed evidence to a target variable we want to learn about. We introduce the notion of factor argument independence to address the outstanding q...
In this work, we propose a novel method for Bayesian Networks (BNs) structure elicitation that is based on the initialization of several LLMs with different experiences, independently querying them to create a structure of the BN, and further obtaining the final structure by majority voting. We compare the method with one alternative method on vari...
Despite the efforts of the Process Mining community, the understanding of process mining results and reports by non-technical users still remains an open challenge. For this reason, an increasing need for endowing, integrating and enriching process mining tools and pipelines with mechanisms able to convey the most salient aspects of a process in an...
The article emphasizes the critical importance of language generation today, particularly focusing on three key aspects: Multitasking, Multilinguality, and Multimodality, which are pivotal for the Natural Language Generation community. It delves into the activities conducted within the Multi3Generation COST Action (CA18231) and discusses current tr...
Process mining techniques extract knowledge from event logs within organizations to understand and improve the behavior of their business processes. These techniques utilize a wide range of methods to automatically generate process models from event log data, simplify these models, calculate various indicators to optimize performance, and visualize...
Plain language summary
The Multi3Generation COST Action is a collaborative project that brings together researchers from various fields, all centered around Natural Language Generation. Natural Language Generation involves using computers to generate human-like language for tasks such as translation, summarization, question-answering, and dialogue...
We present a summary of the work A framework for the automatic description of healthcare processes in natural language: Application in an aortic stenosis care process originally published in the Journal of Biomedical Informatics. We present a framework for the automatic generation of natural language descriptions of healthcare processes, with a spe...
The effective presentation of process models to non-expert users in a way that allows them to understand and query these models is a well-known research challenge. Conversational interfaces, with their low expertise requirements, offer a potential solution. While procedural models like Petri nets are not ideal for linguistic presentation, declarati...
In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes. P2T integrates three AI paradigms: process mining for extracting temporal and structural information from a process, fuzzy linguistic protoforms for modelling uncertain terms, and natural language generation fo...
In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes. The model includes temporal and causal information from processes and attributes, quantifies attributes in time during the process life-span and recalls causal relat...
Medical applications of Artificial Intelligence (AI) have consistently shown remarkable performance in providing medical professionals and patients with support for complex tasks. Nevertheless, the use of these applications in sensitive clinical domains where high-stakes decisions are involved could be much more extensive if patients, medical profe...
Proxecto Nós is an initiative aimed at providing the Galician language with openly licensed resources, tools, and demonstrators in the area of intelligent technologies. The Project has two main scientific and technological objectives: (i) to integrate the Galician language into cutting-edge AI and language technologies, thus enabling the natural us...
Presentamos un resumen del trabajo titulado A framework for the automatic description of healthcare processes in natural language: Application in an aortic stenosis care process publicado originalmente en el Journal of Biomedical Informatics [1]. En él, presentamos un framework para la generación automática de descripciones en lenguaje natural de p...
Hallucinations and omissions need to be carefully handled when using neural models for performing Natural Language Generation tasks. In the particular case of data to text applications, neural models are usually trained on large-scale datasets and sometimes generate text with divergences in respect to the data input. In this paper, we show the impa...
The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powerful languages, as they are a...
In this paper we experimentally assess, from both algorithmic and pragmatic perspectives, the adequacy of linguistic descriptions of real data generated by two metaheuristics: simulated annealing and genetic algorithm meta-heuristics. The type of descriptions we consider are fuzzy quantified statements (both Zadeh's type-1 and type-2) involving thr...
In this paper, we propose a framework for the automatic generation of natural language descriptions of healthcare processes using quantitative and qualitative data and medical expert knowledge. Inspired by the demand of novel ways of conveying process mining analysis results of healthcare processes [1], our framework is based on the most widely use...
In this paper we present a model based on computational intelligence and natural language generation for the automatic generation of textual summaries from numerical data series, aiming to provide insights which help users to understand the relevant information hidden in the data. Our model includes a fuzzy temporal ontology with temporal reference...
In this paper, we deal with one of the challenges in process mining enhancement: prediction of remaining times in a business process, which is a critical task for many organisations. Our approach consists of (i) defining a number of attributes on the business logs that capture structural information from the traces, (ii) extending the well-known an...
In this work, we present a complete system to produce an automatic linguistic reporting about the customer activity patterns inside open malls, a mixed distribution of classical malls joined with the shops on the street. These reports can assist to design marketing campaigns by means of identifying the best places to catch the attention of customer...
In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes. The model includes temporal and causal information from processes and attributes, quantifies attributes in time during the process life-span and recalls causal relat...
We have defined an interdisciplinary program for training a new generation of researchers who will be ready to leverage the use of Artificial Intelligence (AI)-based models and techniques even by non-expert users. The final goal is to make AI self-explaining and thus contribute to translating knowledge into products and services for economic and so...
In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes. P2T integrates three AI paradigms: process mining for extracting temporal and structural information from a process, fuzzy linguistic protoforms for modelling uncertain terms, and natural language generation fo...
The evaluation of Natural Language Generation (NLG) systems has recently aroused much interest in the research community, since it should address several challenging aspects, such as readability of the generated texts, adequacy to the user within a particular context and moment and linguistic quality-related issues (e.g., correctness, coherence, un...
In this work we present a method to estimate the activity patterns made by shoppers in open malls based on localization information and process mining techniques. We present our smart phone application for logging information from sensors and a process mining system to discover what kind of activity pattern is made by the shoppers based in the key...
We have defined an interdisciplinary program for training a new generation of researchers who will be ready to leverage the use of Artificial Intelligence (AI)-based models and techniques even by non-expert users. The final goal is to make AI self-explaining and thus contribute to translating knowledge into products and services for economic and so...
Artificial Intelligence (AI) has become a first class citizen in the cities of the 21st century. New applications are including features based on opportunities that AI brings, like medical diagnostic support systems, recommendation systems or intelligent assistance systems that we use every day. Also, each day, people are more concerned regarding t...
We present a motion planner for the autonomous navigation of UAVs that manages motion and sensing uncertainty at planning time. By doing so, optimal paths in terms of probability of collision, traversal time and uncertainty are obtained. Moreover, our approach takes into account the real dimensions of the UAV in order to reliably estimate the proba...
In this paper, we deal with one of the current challenges in process mining enhancement: the prediction of remaining times in business processes. Accurate predictions of the remaining time, defined as the required time for an instance process to finish, are critical in many systems for organisations being able to establish a priori requirements, fo...
The dramatic success of Artificial Intelligence applications has been accompanied by more complexity, which makes its comprehension for final users more difficult and damages trustworthiness as a result. Within this context, the emergence of Explainable AI aims to make intelligent systems decisions and internal processes more comprehensible to huma...
In this paper, we deal with one of the challenges in process mining enhancement: prediction of remaining times in a business process, which is a critical task for many organisations. Our approach consists of i) defining a number of attributes on the business logs that capture structural information from the traces, ii) extending the well-known anno...
Since buildings are one of the largest sources of energy consumption in most cities of the world, energy management is one of the major concerns in their design. To ameliorate this problem, buildings are becoming smarter by the incorporation of intelligent supervision and control systems. Data captured by the sensors can be interpreted and processe...
We present a novel approach for motion planning in mobile robotics under sensing and motion uncertainty based on state lattices with graduated fidelity. The probability of collision is reliably estimated considering the robot shape, and the fidelity adapts to the complexity of the environment, improving the planning efficiency while maintaining the...
In this work we present a state lattice based approach for motion planning in mobile robotics. Sensing and motion uncertainty are managed at planning time to obtain safe and optimal paths. To do this reliably, our approach estimates the probability of collision taking into account the robot shape and the uncertainty in heading. We also introduce a...
We present a new approach to motion planning in mobile robotics under sensing and motion uncertainty based on state lattices with graduated fidelity. Uncertainty is predicted at planning time and used to estimate the safety of the paths. Our approach takes into account the real shape of the robot, introducing a deterministic sampling based method t...
This paper describes the problem of keyword-based search over environmental data sources. Based on a number of assumptions that simplify this general problem, a prototype of a search engine for environmental data was designed, implemented and evaluated. This first solution serves as a proof of concept that illustrates its applicability in different...
We describe the use of fuzzy sets within MonitorSI-Text. It is a real and operative data-to-text system that generates textual information about the operational state of Information Technology services, monitored by the commercial software platform Obsidian. Until now, Obsidian provided several dashboards that allowed to monitor in real time the st...
In this chapter we summarize the fruitful relationship between Prof. Claudio Moraga and the University of Santiago de Compostela. For almost two decades Prof. Moraga was a regular visitor at our departments as well as a lecturer at our doctoral and summer courses. A milestone in this long term relationship was his key role in the hosting and organi...
Welcome to the Proceedings of the 10th International Natural Language Generation Conference (INLG 2017)! INLG is the annual meeting of the ACL Special Interest Group on Natural Language Generation (SIGGEN). The INLG conference provides the premier forum for the discussion, dissemination, and archiving of research and results in the field of Natural...
We describe SimpleNLG-ES, an adaptation of the SimpleNLG realization library for the Spanish language. Our implementation is based on the bilingual English-French SimpleNLG-EnFr adaptation. The library has been tested using a battery of examples that ensure that the most common syntax, morphology and orthography rules for Spanish are met. The libra...
We describe the use of fuzzy sets within MonitorSI-Text. It is a real and operative data-to-text system that generates textual information about the operational state of Information Technology services, monitored by the commercial software platform Obsidian. Until now, Obsidian provided several dashboards that allowed to monitor in real time the st...
The articles in this special section focus on using natural language generation techniques (NLG) and natural language processing (NLP) to build computational systems that generate reports and other kinds of text in human languages. NLG uses analytics, AI, and NLP to obtain relevant information about non-linguistic data and to generate textual summa...
The SoftLearn Activity Reporter (SLAR) is a data-to-text service which automatically generates textual reports about the activity developed by students within the SoftLearn virtual learning environment. In this paper we describe the conception of the service, its architecture and its subsequent evaluation by an expert pedagogue, where 20 full repor...
IEEE CIM is the magazine of the IEEE Computational Intelligence Society. With an Impact Factor of 3.647 (by June 2016), it is ranked as Q1 by the ISI-JCR. You have additional info at http://citius.usc.es/NLGCI
In genetic fuzzy systems (GFS) the size of the problem has a huge influence in the performance of the obtained models, since i) the fuzzy rule bases learned suffer from exponential rule explosion when the number of variables increases, and ii) the convergence time increments with the number of examples. In this paper we present S-FRULER, a scalable...
We explore the implications of using fuzzy techniques (mainly those commonly used in the linguistic description/summarization of data discipline) from a natural language generation perspective. For this, we provide an extensive discussion of some general convergence points and an exploration of the relationship between the different tasks involved...
We explore the implications of using fuzzy techniques (mainly those commonly used in the linguistic description/summarization of data discipline) from a natural language generation perspective. For this, we provide an extensive discussion of some general convergence points and an exploration of the relationship between the different tasks involved...
This paper explores the current state of the task of generating easily understandable information from data for people using natural language, which is currently addressed by two independent research fields: the natural language generation field - and, more specifically, the data-to-text sub-field - and the linguistic descriptions of data field. Bo...
In this paper we present a reliable motion planner that takes into account the kinematic restrictions, the shape of the robot and the motion uncertainty along the path. Our approach is based on a state lattice that predicts the uncertainty along the paths and obtains the one which minimizes both the probability of collision and the cost. The uncert...
In this paper we present a data-to-text service which automatically produces textual forecasts about the air quality state for every municipality in Galicia (NW Spain) for the Galician Meteorology Agency (MeteoGalicia). We discuss the context and the details about the conception of the service, as well as a technical and formal description of the s...
In this paper we present a general model for building linguistic descriptions of data (LDD) solutions, which is based on computational models of perception inspired in the computational theory of perceptions (CTP) and in fields of knowledge different from the computational intelligence area. The elements in the model aim to consider the richness an...
In regression problems, the use of TSK fuzzy systems is widely extended due
to the precision of the obtained models. Moreover, the use of simple linear TSK
models is a good choice in many real problems due to the easy understanding of
the relationship between the output and input variables. In this paper we
present FRULER, a new genetic fuzzy syste...
In this paper we present a service which automatically generates textual short-term reports for the students' behavior in virtual learning environments. Through this approach, we show how textual reporting is a coherent way of providing information that can complement ---and even enhance--- visual statistics and help teachers to understand in a com...
We present in this paper an application which automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic descr...
The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these...
In this paper we present the results and performance of five different classifiers applied to the task of automatically generating textual weather forecasts from raw meteorological data. The type of forecasts this methodology can be applied to are template-based ones, which can be transformed into an intermediate language that can directly be mappe...
Abstract: We are living in a world which is increasingly flooded with vast amounts of data. As a consequence, the use of techniques allowing to exploit and explain the information contained in this raw data has become mandatory. In this context, more human-friendly alternatives to standard techniques like statistics or data mining approaches are be...
The application of fuzzy techniques in robotics has become widespread in the last years and in different fields of robotics, such as behavior design, coordination of behavior, perception, localization, etc. The significance of the contributions was high until the end of the 1990s, where the main aim in robotics was the implementation of basic behav...
Certainly, Enric Trillas (hereinafter, ET) is a master. The name ‘master’ comes from the Latin magister, a term that in the ancient Rome defined a person who had authority or power over other people. ET is a magister because he has authority over students and disciples who want to gain knowledge about the mathematical models of imprecise reasoning.
The linguistic description of data intends to provide texts that convey the most important information contained in the data. One of the main tasks to be carried out in order to build a linguistic description is the extraction and representation of the knowledge to be transmitted. To perform this task, adequate mechanisms for knowledge representati...
In this paper, a new approximate syllogistic reasoning schema is described that expands some of the approaches expounded in the literature into two ways: (i) a number of different types of quantifiers (logical, absolute, proportional, comparative and exception) taken from Theory of Generalized Quantifiers and similarity quantifiers, taken from stat...
In this paper, a new approximate syllogistic reasoning schema is described that expands some of the approaches expounded in the literature into two ways: (i) a number of different types of quantifiers (logical, absolute, proportional, comparative and exception) taken from Theory of Generalized Quantifiers and similarity quantifiers, taken from stat...
The main goal of this work is to analyze the behaviour of the F A quantifier fuzzification mechanism [23, 22, 17]. As we prove in the paper, this model has a very solid theorethical behaviour, superior to most of the models defined in the literature. Moreover, we show that the underlying probabilistic interpretation has very interesting consequence...