The Semantic Web has addressed the development of technologies for efficient data exchange through formats such as RDF and the use of SPARQL as a standard query language. However, users often face difficulties in obtaining unexpected or erroneous results without a clear understanding of the source of the problem, resulting in wasted time. In this context, a method for validating SPARQL queries, divided into five phases executed before processing in RDF engines, is proposed. This method is based on an ontology and considers both syntactic and semantic validations, generating detailed error reports to prevent problems during execution. Experimental results show the efficiency of the approach: when querying a dataset of 16,005 triples, syntactic validation did not exceed 0.4508 ms and semantic validation did not exceed 6.2343 ms. In contrast, queries without validation presented longer response times, with differences of up to 68.2492 ms in syntactic validation and 144.4288 ms in semantic validation. This approach not only improves query accuracy and efficiency, but also significantly reduces time and errors in information retrieval in semantic environments.

The rapid adoption of Internet of Things (IoT) technology has allowed the development of applications where devices such as sensors generate data periodically. These devices are integrated into IoT systems where large volumes of data are managed and whose processing and handling for the proper operation of the IoT system is necessary. One of the main challenges in IoT is the effective data management and real-time communication between heterogeneous devices. Ontologies, which are semantic representations of knowledge, are built to provide semantic interoperability. However, the diversity of applications and the scope of new technologies makes current ontology's proposals incomplete in modeling privacy and security, energy awareness, and ethics at the same time. In this context, we propose an enhanced Ontology, called IoTO++, for improving semantic interoperability in IoT environments. We validated the proposal considering lexical, structural and domain knowledge levels. Results show that even though lite ontologies are more maintainable, compatible and transferable, ontologies with more annotations are better in terms of functional adequacy. This includes but is not limited to characteristics like, knowledge reuse, acquisition and representation. Furthermore, IoTO++ demonstrates superior performance in the domain knowledge level, proving to be a more effective solution for modeling the variety of IoT applications.

Nowadays, the advancement of technology allows the use of social robots for various daily tasks such as therapies, teaching assistants, restaurant services, among others. Human-Robot Interaction (HRI) is under constant study due to the new capabilities that robots acquire thanks to their improved hardware (e.g., more joints). Robots receive information through sensors such as cameras and microphones and can thus modify their behavior and adapt to different situations. However, an exhaustive real-time analysis of data within the robot requires excessive computing power and energy usage, which are limited in social robots. In this context, we propose a lightweight Machine Learning model to balance accuracy and audio processing time to recognize the emotions of happiness, sadness, anger, and neutral in real-time, aiming to improve HRI. Additionally, an empirical analysis to identify the most relevant audio features for emotion recognition is presented. The objective is to generate a lighter and more appropriate model for the robot's hardware. Results show better accuracy by using the RAVDESS, IEMOCAP, and RAVDESS+IEMOCAP datasets and a recognition time around 1 second.

In robotics, object detection in images or videos, obtained in real-time from sensors of robots can be used to support the implementation of service robot tasks (e.g., navigation, model its social behavior, recognize objects in a specific domain), usually accomplished in indoor environments. However, traditional deep learning based object detection techniques present limitations in such indoor environments, specifically related to the detection of small objects and the management of high density of multiple objects. Coupled with these limitations, for specific domains (e.g., hospitals, museums), it is important that the robot, apart from detecting objects, extracts and knows information of the targeted objects. Ontologies, as a part of the Semantic Web, are presented as a feasible option to formally represent the information related to the objects of a particular domain. In this context, this work proposes an object detection and recognition process based on a Deep Learning algorithm, object descriptors, and an ontology. ODROM, an Object Detection and Recognition algorithm supported by Ontologies and applied to Museums, is an implementation to validate the proposal. Experiments show that the usage of ontologies is a good way of desambiguating the detection, obtained with a and$\mathbf{mAP}{@}0.5=0.88$and a$\mathbf{mAP}{@}[0.5:0.95]=61\%$.

Twitter is one of the most popular information source available on the Web. Thus, there exist many studies focused on analyzing the credibility of the shared information. Most proposals use either Twitter API or web scraping to extract the data to perform such analysis. Both extraction techniques have advantages and disadvantages. In this work, we present a study to evaluate their performance and behavior. The motivation for this research comes from the necessity to know ways to extract online information in order to analyze in real-time the credibility of the content posted on the Web. To do so, we develop a framework which offers both alternatives of data extraction and implements a previously proposed credibility model. Our framework is implemented as a Google Chrome extension able to analyze tweets in real-time. Results report that both methods produce identical credibility values, when a robust normalization process is applied to the text (i.e., tweet). Moreover, concerning the time performance, web scraping is faster than Twitter API, and it is more flexible in terms of obtaining data; however, web scraping is very sensitive to website changes.

In the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we present an R DF D atatype in F erring F ramework, called RDF-F, which provides two independent datatype inference processes: 1) a four-step process consisting of (i) a predicate information analysis (i.e., deduce the datatype from existing range property), (ii) an analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical space), (iii) a semantic analysis of the predicate name and its context, and (iv) generalization of Numeric and Binary datatypes to ensure the integration; and 2) a non-ambiguous lexical-space-matching process, where literal values are inferred by the modification of their representation, following new lexical spaces. We evaluated the performance and the accuracy of both processes with datasets from DBpedia. Results show that the execution time of both indicators is linear and their accuracy can increase up to 97.10 and 99.30%, respectively.