Characterizations of Decomposable Dependency Models

Decomposable dependency models possess a number of interesting and useful proper- ties. This paper presents new characterizations of decomposable models in terms of in- dependence relationships, which are obtained by adding a single axiom to the well-known set characterizing dependency models that are isomorphic to undirected graphs. We also brie y discuss a potential application of our results to the problem of learning graphical models from data.Spanish Comisión Interministerial de Ciencia y Tec- nología (CICYT) TIC96-078

A scoring function for learning Bayesian networks based on mutual information and conditional independence tests

We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.I would like to acknowledge support for this work from the Spanish ‘Consejería de Innovación Ciencia y Empresa de la Junta de Andalucía’, under Project TIC-276

A Fuzzy Inference Model Based on an Uncertainty Forward Propagation Approach

The management of uncertainty and imprecision is becoming more and more important in knowledge-based systems. Fuzzy logic provides a systematic basis for representing and inferring with this kind of knowledge. This paper describes an approach for fuzzy inference based on an uncertainty forward propagation method and a change in the granularity of the elements involved. The proposed model is able to handle very general kinds of facts and rules, and it also verifies the most usual properties required by a fuzzy inference model

Discovering a tourism destination with social media data: BERT-based sentiment analysis

Purpose – The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users. Design/methodology/approach – The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models. Findings – The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated. Research limitations/implications – The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place. Practical implications – The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions. Originality/value – This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion PID2019-106758GB-C31European Commissio

Bayesian network learning algorithms using structural restrictions

The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. The main goal of this paper is to study whether the algorithms for automatically learning the structure of a Bayesian network from data can obtain better results by using this prior knowledge. Three types of restrictions are formally defined: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions. We analyze the possible interactions between these types of restrictions and also how the restrictions can be managed within Bayesian network learning algorithms based on both the score + search and conditional independence paradigms. Then we particularize our study to two classical learning algorithms: a local search algorithm guided by a scoring function, with the operators of arc addition, arc removal and arc reversal, and the PC algorithm. We also carry out experiments using these two algorithms on several data sets.Spanish Junta de Comunidades de Castilla-La Mancha and Ministerio Educación y Ciencia Projects PBC-02-002 and TIN2004- 06204-C03-0

Analyzing tourist data on Twitter: a case study in the province of Granada at Spain

This work has been funded by the Spanish Ministerio de Economía y Competitividad under project TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

Committee-Based Profiles for Politician Finding

One step towards breaking down barriers between citizens and politicians is to help people identify those politicians who share their concerns. This paper is set in the field of expert finding and is based on the automatic construction of politicians’ profiles from their speeches on parliamentary committees. These committee-based profiles are treated as documents and are indexed by an information retrieval system. Given a query representing a citizen’s concern, a profile ranking is then obtained. In the final step, the different results for each candidate are combined in order to obtain the final politician ranking. We explore the use of classic combination strategies for this purpose and present a new approach that improves state-of-the-art performance and which is more stable under different conditions. We also introduce a two-stage model where the identification of a broader concept (such as the committee) is used to improve the final politician ranking.This work has been funded by the Spanish Ministerio de Economı́a y Competitividad under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

Positive unlab ele d learning for building recommender systems in a parliamentary setting

Our goal is to learn about the political interests and preferences of Members of Parliament (MPs) by mining their parliamentary activity in order to develop a recommendation/filtering system to determine how relevant documents should be distributed among MPs. We propose the use of positive unlabeled learning to tackle this problem since we only have information about relevant documents (the interventions of each MP in debates) but not about irrelevant documents and so it is not possible to use standard binary classifiers which have been trained with positive and negative examples. Additionally, we have also developed a new positive unlabeled learning algorithm that compares favorably with: (a) a baseline approach which assumes that every intervention by any other MP is irrelevant; (b) another well-known positive unlabeled learning method; and (c) an approach based on information retrieval methods that matches documents and legislators’ representations. The experiments have been conducted with data from the regional Spanish Andalusian Parliament.This work has been funded by the Spanish “Ministerio de Economía y Competitividad” under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

Predicting IR Personalization Performance using Pre-retrieval Query Predictors

Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements.This work has been supported by the Spanish Andalusian “Consejerı́a de Innovación, Ciencia y Empresa” postdoctoral phase of project P09-TIC-4526, the Spanish “Ministerio de Economı́a y Competitividad” projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions

Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo Regional (FEDER), proyectos TEC2015-69496-R y TIN2016-77902-C3-2-