The DATEX II standard, originally conceived for traffic information exchange among control centers and road operators, has undergone a transformative journey, emerging as a fundamental European standard for open traffic data. Propelled by the ITS Directive, which positions DATEX II as the preferred language for traffic information publication at National Access Points, it stands as a cornerstone in the realm of open traffic data. This article explores the transformation of DATEX II, with a specific focus on the exemplary LOD-RoadTran18 project.

Ontologies within the Semantic Web represent a transformative advancement, offering standardized data schemes that seamlessly interconnect with diverse vocabularies and facilitate efficient information exchange globally. This paper explores the intersection of ontologies and road safety, a domain critical to public well-being. The analysis focuses on existing ontologies related to Safety Related Traffic Information, delving into their implications for enhanced road safety, optimized traffic management, and streamlined safety-related data exchange. The study discerns common patterns, best practices, and areas for improvement across various ontological models, thereby contributing to the development of standardized solutions for managing road safety information. Using an ad hoc metrics system for this type of information will assist us in determining which ontology is most suitable and/or the need for its adaptation or utilization. Acknowledging the vital role of ontologies in promoting data interoperability, knowledge sharing, and informed decision-making, this research underscores the potential of integrating Semantic Web technologies and ontological models to revolutionize safety-related information management. The findings advocate for the transformative impact of ontologies in reducing road accidents and safeguarding human lives, emphasizing their significance in advancing the evolution of safer and more efficient road networks.

This paper discusses a segment of the "Artificial Intelligence and Semantics of Earth Observation (EO) data for the establishment of the Valencia Anchor Station as a supersite of the CEOS LPV Program (ASOTVAS)." The ASOTVAS project aims to apply AI and data semantics algorithms to the Valencia Anchor Station’s control zone (10 km x 10 km). A data semantics analysis explores concepts such as vocabularies, ontologies, and knowledge maps, with an emphasis on the reuse of existing models. Crucially, an ontological model with clear data definitions is developed to correlate Copernicus Program data with station parameters (e.g., FAPAR, LST, soil moisture, etc.). Using the ontology, the actual data are aggregated to create a knowledge map, which includes both an assertion model (concepts and properties) and an instance model (specific instances for ontological relationships). The main task is to develop an ontology-based representation scheme for terms of the application domain, which facilitates knowledge capture, distribution, and maintenance. The scheme enables computer processing for tasks related to information handling and supporting information exchange. Widely accepted ontology specification methodologies are employed. To avoid unnecessary efforts, emphasis is put on the reuse of existing ontologies or vocabularies, expanded, and adapted to the described case study. The optimal granularity is chosen for efficient ontology operation. The conceptual and technological requirements of the representation scheme are discussed. The methodology is based on software quality standards.

In this article we demonstrate a particular use of ontologies based on the European specifications of the DATEX II UML model with Safety Related Traffic Information (SRTI) version 3 scope. These specifications are designed and developed as an exchange of traffic data and mechanisms for sending information by a working group to establish and standardize the interface between traffic control and information centers. It is the standard reference for sending and receiving traffic information between applications developed by different countries in Europe. This specification of DATEX II XML/UML describes concepts and data structures related to traffic, but the description is only syntactic, not semantic. Therefore, the objective to be achieved in this part of the research has been to develop a semantic description of the contents based on the UML data model and to link the data of the new general ontology with other vocabularies or related ontologies already developed and available on the internet.

The SeMap project, funded by the BBVA Foundation, has among its main objectives, to provide semantics to digitised heritage objects, and to make this information more accessible through web visualisation systems that use spatio-temporal maps. This project has had the collaboration and support of the Ministry of Culture and Sport of the Spanish Government, through CERES, the Digital Network of Museum Collections in Spain. This network manages data related to more than 400,000 objects, located in 113 museums of different specialities and from various thematic and geographical areas. These data are currently accessible through a web portal, but there is no public access to a knowledge graph with this information. Thanks to the collaboration of this entity, SeMap granted the access to this information. With the adequate process, this data has been represented by a knowledge graph. Using the resources offered by the Google Maps and Geonames APIs, it has been possible to georeferenced the data with an acceptable level of precision. This fact, has allowed the design and development of a spatio-temporal map visualisation system with a high level of interaction. This paper describes the process of creating a knowledge graph with this information, based on the CIDOC-CRM model, and using the CERES Cultural Heritage vocabularies and thesauri. The technologies and techniques used to visualise its contents are also described. Finally, the results of the evaluation of the project with end users are presented. In order to evaluate the project, an evaluation session based on the System Usability Scale (SUS) has been conducted. Forty-five secondary school students participated in this session, accessing the tool from the classroom and with the devices with which they usually work.

A characteristic of intelligent cities is urban development where local and national governments regulate land use plans in order to efficiently plan and control urban and real state growth.

Every day the devices connected autonomously to the communication networks are increased. New applications arise for this type of interconnected objects, thus offering new alternatives for solutions of any kind, through the use of technology based on ubiquitous computing. That is why this work is inspired by these new technologies for its application in the educational field, with the purpose of contributing to the imposition of the use of IoT as a tool1 in the classroom.

The research about the use of technology for persons with autism spectrum disorders (ASD) has rapidly increased in the last decade. This fact has accompanied with the sharp development and use of apps for mobile devices by these people and their caregivers. The election of the adequate apps for persons with autism is a very difficult task, with many variables involved with the person with ASD, the family, the practitioners and the community where they live. This paper describes a recommendation system that reuse an ontology, which supports the information about the apps and all the variables previously cited. The data about the apps are automatically obtained from recognized web repositories and reports published on Facebook/Twitter from experts. This hybrid recommendation system gets the data from the ontology, in order to score the technology and to recommend the best set of apps for the studied person1.

Radio Environment Maps (REM) can be an essential tool for numerous applications in future 5G wireless networks. In this paper, we employ a popular geo-statistical method called ordinary kriging to estimate the REM of an area covered by a eNodeB equipped with multiple antennas. Wireless sensors are distributed over the area of interest and adaptive clusters of sensors are arranged in order to improve the quality of the estimation. In this paper, we modify the distributed clustering algorithm proposed in a previous work to reduce the complexity of kriging prediction. Simulations are performed to detail the cluster formation technique and to analyze the performance in comparison with centralized and classical interpolation methods. The computational complexity is verified in terms of the number of message exchanges among the sensor nodes. Simulation results demonstrate that clusters are formed by an average of 5 sensor nodes.

Radio environment maps can be a powerful tool for achieving efficient context-aware resource allocation in 5G heterogeneous networks. In this paper, we consider an heterogeneous network formed by a traditional cellular network and a wireless sensor network. The role of the wireless sensor network is to estimate the radio environment map of the cell using a geostatistical interpolation technique named Kriging. A distributed clustering algorithm was proposed in a previous work in order to decrease the complexity of the estimation. In our contribution, the clustering formation process is modified to include the communication cost as a metric to determine which nodes are included in each cluster. Simulation results show that the proposed algorithm improves the estimation quality for sparse wireless sensor networks, and preserves the network lifetime by forming clusters with an average of 5 nodes.

One of the objectives of the Smart Cities is the efficient and sustainable use of transport infrastructures. Sustainability is understood as the application of new technologies to minimize the loss of time and energy, and at the same time, to improve the satisfaction of citizens. Achieving this objective calls for making use of the mobility data collected from the existing transport infrastructure and services, as well as from the citizens themselves. After the data collection process, information must be stored (use of open data bases) and made accessible (available) to specialized applications and users. This paper highlights the opportunity to take advantage of emerging technologies, like the open source data platform CKAN, or to use services for data linked queries (SPARQL endpoint). As an example of application of these technologies to the transport domain, two use cases are presented: the proposed implementation of an information access point and a national project called “CONECTA”.

Providing recommendations on social systems has been in the spotlight of both academics and industry for some time already. Social network giants like Facebook, LinkedIn, Myspace, etc., are eager to find the silver bullet of recommendation. These applications permit clients to shape a few certain social networks through their day-by-day social cooperative communications. In the meantime, today's online experience depends progressively on social association. One of the main concerns in social network is establishing a successful business plan to make more profit from the social network. Doing a business on every platform needs a good business plan with some important solutions such as advertise the products or services of other companies which would be a kind of marketing for those external businesses. In this study a philosophy of a system speaking to of a comprehensive structure of advertisement recommender system for social networks will be presented. The framework uses a semantic logic to provide the recommended products and this capability can differentiate the recommender part of the framework from classical recommender methods. Briefly, the framework proposed in this study has been designed in a form that can generate advertisement recommendations in a simplified and effective way for social network users.

This paper proposes a content recommendation mechanism as part of a model for implementing ubiquitous learning for supporting people with chronic diseases who are treated at home, so that they can learn more about treatments for their disease. In the proposed approach, the learning takes place based on day-to-day activities and real situations. In this case, the model supports the development of tools that can learn about the user's context, based on data obtained via sensors installed on users or in their home, as well as data supplied directly by the user interface of their mobile devices, and data provided by the healthcare team, and, after that, recommend contents about their diseases.

The Internet is active and very dynamic. It has enabled an unprecedented evolution in telematics and information systems, and in turn a global revolution in the use of technology and digital citizenry. Although many see the technologies that have driven this revolution as inventions of the past 1 to 2 decades, quite a few had their genesis before the Internet became mainstream. In exploring the history of the Internet, we see how it has facilitated innovations across all areas of technology and social life, and with a little imagination are perhaps able to get a glimpse of what is to come. A lot of technologies related to the Web environment have emerged in this area: Blogs, Wikis, Social Networks, Content Managers, etc. Finally, it's important to understand the concept of semantic annotation and information extraction using technologies such as rich snippets, SPARQL, GRDDL etc., tools that enable better management and knowledge recovering. Some examples of application of web 3.0 are: Linked Open Data, semantic search engines, semantic web services, social Semantic Web, semantic wiki etc.

Cloud computing is a technological paradigm that permits to offer computing services over the Internet. This new service model is closely related to previous, well-known distributed computing initiatives such as Web services and grid computing. In the current socio-economic climate, the affordability of cloud computing has gained it popularity among today's innovations. Under these circumstances, more and more cloud services become available. Consequently, it is becoming more and more difficult for service consumers to find and access those cloud services that fulfill their requirements. In this work, a cloud computing ontology is proposed that facilitates a semantic identification, discovery and access to the services in the cloud. Moreover, a tool that (semi-) automatically annotates cloud services and stores their semantic description is introduced.

The Semantic Web is a powerful approach used by some intelligent applications to manage complex information in many different fields. Nevertheless, there are not standard tools to design and built the applications, therefore each problem need to be analysed separately in order to meet the requirements.

In this work we demonstrate a particular use of ontologies based on the European specifications DATEXII. These specifications are designed and developed as a traffic and travel data exchange mechanism by a European task force to set up and standardise the interface between traffic control and information centres. It is the reference for applications that are developed and implemented in Europe.

Currently, freight transport carried through a country or through different countries undergoes many delays due to merchandise controls. Merchandise control is only carried out at some border posts or when the authorities demand it. Such control can be an important problem since there are different laws for the same merchandise, according to the legislation of the country and even within the same country.

The present paper describes the main characteristics and components of the tool developed for integrating the definition of profiles for semantic web services. This tool is based on the languages DAML-S and OWL-S and it includes the ontology visualization and consistency verification that specify the concepts a web service interacts with. Starting from a service description interpreted by a computer and by the means used for accessing the service, it is possible to find out which software agents use the service. The tool can generate information in different formats, such as the SVG format proposed by the W3C, for representing graphic information based on XML. The tool was developed in Java language and is being used for the visualization of ontologies and for the semantic description of services in traffic information systems that the LISITT group (Laboratorio Integrado de Sistemas Inteligentes y Tecnologías de la información de Tráfico) developed at the Robotics Institute at the University of Valencia.

The Transportation and traffic domain is cross-disciplinary; Different areas must be covered to solve complex problems. It also involves the use of different systems for monitoring, visualization, information distribution, control, communication, etc. All of these systems are implemented using different levels of technology. In most cases the control of these systems is handled by [graphic] user interfaces. In the past years the number of systems has grown substantially and because of their "monolytic" design, they have different and in most cases not-compatible user-interfaces. A generic interface would be useful to improve not only the different processes but also the efficiency of the operator

This paper describes the main contributions in relation with the develop of an XBRL (Extensible Business Reporting Language) taxonomy proposed by the PRISMA research group at University UNAB. This taxonomy has been developed for public institutions in Colombia. This taxonomy is based on the information exchange of financial information. It is also described the creation of a wiki system which includes real information of existing XBRL projects in the world and the creation of a demostration center of XBR reports.