An Overview of the Tourpedia Linked Dataset with a Focus on Relations Discovery among Places

Tourpedia (http://tour-pedia.org) is an open initiative which contains a linked dataset of tourism places, i.e. accommodations, attractions, points of interest (POIs) and restau- rants. Tourpedia extracts and integrates information about places from four different social social media: Facebook, Foursquare, Google Places and Booking.com. The resulting knowledge base currently consists of more than 6M RDF triples and describes almost 500.000 places, each of which is identified by a globally unique identifier, which can be dereferenced over the Web into a RDF description. This paper gives an overview of the Tourpedia knowledge base and illustrates how new relations are discovered among places through Named Entity Recognition (NER) tools.?

Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain

Online platforms, serving as the primary conduit for travelers to seek, compare, and secure travel accommodations, require a profound understanding of user dynamics to craft competitive and enticing offerings. Concurrently, recent advancements in Natural Language Processing, particularly large language models, have made substantial strides in capturing the complexity of human language. Simultaneously, knowledge graphs have become a formidable instrument for structuring and categorizing information. This paper introduces a cutting-edge deep learning methodology integrating large language models with domain-specific knowledge graphs to classify tourism offers. It aims at aiding hospitality operators in understanding their accommodation offerings’ market positioning, taking into account the visit propensity and user review ratings, with the goal of optimizing the offers themselves and enhancing their appeal. Comparative analysis against alternative methods on two datasets of London accommodation offers attests to our approach’s effectiveness, demonstrating superior results

Prototipo de una aplicación móvil con información centralizada de los recursos turísticos: Caso de uso de las parroquias A y B.

El turismo desempeña un papel crucial en el crecimiento económico y el desarrollo de las comunidades locales. A medida que el mundo se vuelve más conectado y la tecnología avanza, las aplicaciones móviles han emergido como una herramienta indispensable en la industria del turismo. Estas aplicaciones proporcionan a los viajeros información instantánea y acceso a una amplia gama de servicios y actividades turísticas. En el Ecuador estas aplicaciones se enfocan en lugares específicos, como ciudades y cantones, lo que obliga a los turistas a instalar múltiples aplicaciones que tienen el mismo propósito. Esta fragmentación dificulta la experiencia del usuario y dificulta la centralización de la información turística. Además, la falta de una aplicación móvil integral obliga a los turistas a buscar información en los medios oficiales de cada lugar, un proceso tedioso y poco eficiente. Como resultado, la descentralización de la información y la escasez de promoción en las zonas rurales dificultan el potencial del turismo en estas áreas. Bajo este contexto, se ha desarrollado un prototipo de aplicación móvil y una aplicación web complementaria con el objetivo de promover y gestionar de manera centralizada los recursos turísticos.Tourism plays a crucial role in economic growth and the development of local communities. As the world becomes more connected and technology advances, mobile applications have emerged as an indispensable tool in the tourism industry. These applications provide travelers with instant information and access to a wide range of services and tourist activities. In Ecuador, these applications focus on specific locations such as cities and towns, which forces tourists to install multiple applications that serve the same purpose: promoting tourist resources. This fragmentation hinders the user experience and complicates the centralization of tourist informa- tion. Additionally, the lack of a comprehensive mobile application compels tourists to search for information through official channels of each location, a tedious and inefficient process. Consequently, the decentralization of information and the limited promotion in rural areas pose challenges to the potential of tourism in these regions.Under this context, a prototype mobile application and a complementary web application have been developed with the objective of promoting and managing tourist resources in a centralized manner.0000-0002-2438-922

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain

The tourism and hospitality sectors have become increasingly important in the last few years and the companies operating in this field are constantly challenged with providing new innovative services. At the same time, (big-) data has become the “new oil” of this century and Knowledge Graphs are emerging as the most natural way to collect, refine, and structure this heterogeneous information. In this paper, we present a methodology for semi-automatic generating a Tourism Knowledge Graph (TKG), which can be used for supporting a variety of intelligent services in this space, and a new ontology for modelling this domain, the Tourism Analytics Ontology (TAO). Our approach processes and integrates data from Booking.com, Airbnb, DBpedia, and GeoNames. Due to its modular structure, it can be easily extended to include new data sources or to apply new enrichment and refinement functions. We report a comprehensive evaluation of the functional, logical, and structural dimensions of TKG and TAO

Social Media Monitoring and Analysis: Multi-domain Perspectives

Social Media Platforms, such as Facebook or Twitter, are part of everyday life as powerful communication tools. They let users communicate anywhere-anytime, improve their own public image and readily share information. For this reason, a growing number of individuals such as professionals as well as companies have opened an account in one or more Social Media platforms. Due to the widespread use and growing numbers of users, a huge amount of data is generated every day. This information may play a crucial role in various decision-making processes. In this setting, research topics connected to monitoring and analysis of Social Media data are becoming increasingly important. The present work stems from data collection methodologies from different Social Media sources. It introduces the problems involved in storing semi-structured data, and in possible information gaps due to privacy policies. These facets are described according to the application domain as well as the Social Media platform. Subsequently, a theoretical generic architecture for handling data from Social Media sources is presented. We present three simplified versions of this architecture in three different domains: Online Reputation, Social Media Intelligence, and Opinion Mining in tourism. In the last part of the work, we introduce Social Media Analysis in these three domains. For the first, we present the project SocialTrends, a web application able to monitor “public” people on Facebook, Twitter, and YouTube. In the second, we introduce an innovative approach for measuring the interactions between users in public spaces such as Facebook (public-by-design). Finally, we present Tour-pedia, a web application that displays a sentiment map of tourist locations in several cities according to different categories (accommodation, restaurants, points of interest and attractions)

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed