Abstract
The Internet and more specifically the World Wide Web have revolutionized the tourism industry. Visitors can now search for substantial amounts of information about the tourism destinations that they wish or ponder visiting and, in this way, decide and plan their trips. This new paradigm generated numerous advantages for the tourist and constituted an empowerment in what concerns to its independence from the tourist agents. Through the trail of information that this process generates, the tourism industry has the possibility to know the interests of their putative clients before they even visit them. In this way, knowing the profile of interest of the visitors is now also an empowerment of the tourism industry as it starts to have tools that allow better understand the needs and expectations of visitors and, in this way, better manage their activities, converging to a more assertive and efficient business response. This article, supported by the fundamentals of Data Analytics and using the Google Trends tool, presents and discusses a study about the intersections of the Portuguese region of the Coa Valley and the Coa museum, in order to better understand the relations of interest between the region and one of his most prominent ex-libris. It was identified the most searched used keywords, distinguishing national and international tourists and, for these, characterizing their nationality.

Abstract
The present work surveys research that integrates successfully a number of complementary fields in Artificial Intelligence. Starting from integrations in Reinforcement Learning: Deep Reinforcement Learning and Relational Reinforcement Learning, we then present Neural-Symbolic Learning and Reasoning since it is applied to Deep Reinforcement Learning. Finally, we present integrations in Deep Reinforcement Learning, such as, Relational Deep Reinforcement Learning. We propose that this road is breaking through barriers in Reinforcement Learning and making us closer to Artificial General Intelligence, and we share views about the current challenges to get us further towards this goal. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
Since early 2000, Microfinance Institutions (MFI) have been using credit scoring for their risk assessment. However, one of the main problems of credit scoring in microfinance is the lack of structured financial data. To address this problem, MFI have started using non-traditional data which can be extracted from the digital footprint of their users. The non-traditional data can be used to build algorithms that can identify good borrowers as in traditional banking. This paper proposes an assembled method to evaluate the predictive power of the non-traditional method. By using the Weight of Evidence (WoE), a transformation based on the distribution within the feature, as feature transformation method, and then applying extremely randomized trees for feature selection, we were able to improve the accuracy of the credit scoring model by 20.20% when compared to the credit scoring model built with the traditional implementation of WoE. This paper shows how the assembling of WoE with different feature selection criteria can result in more robust credit scoring models in microfinance. © 2020, Springer Nature Switzerland AG.

Abstract
The Web Ontology Language (OWL) is a World Wide Web Consortium standard, based on the Resource Description Format standard. It is used to define ontologies. While large ontologies are useful for different applications, some tools require partial ontologies, based mostly on a hierarchical relationship of classes. In this article we present bOWL, a basic OWL browser, with the main goal of being pluggable into others, more significant, web applications. The tool was tested through its integration on LeXmart, a dictionary editing tool.

Abstract
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

Abstract
The wine industry has a high business volume and adds value to the economy. This chapter intends to predict the wine firm performance of three of the most relevant Portuguese regions, by resorting to data available on the Portuguese Farm Accountancy Data Network (PTFADN, Resultados médios por exploração. Available on http://www.gpp.pt/index.php/rica/rede-de-informacao-de-contabilidades-agricolas-rica. Accessed 13 Mar 2018, 2001–2015). The existing social, economic and environmental parameters allowed us to perform function fitting with MATLAB, in order to attain information about the variable’s behaviour. Through the Agent-Based Model (ABM) simulations, it is possible to realize that, in general, the Alentejo region is substantially well prepared to deal with negative scenarios when compared with North and Central regions. Alternative scenarios can be performed in order to develop overall governmental policy recommendations, so as to ensure the sustainability of the three regions. © 2020, The Author(s).