Internet of Things (IoT) is a technology that consists of interconnected entities (intelligent physical objects, services and software systems) via the Internet, which serve automatically and in a coordinated way in data management to achieve greater efficiency and productivity in industrial processes and contribute to improve the quality of people life. As the use of devices grows, so do the challenges for creating secure, operable, scalable and cost-effective IoT systems. The specialized literature shows interest in the application of best practices for the construction of IoT systems and so improving the compatibility of hardware and software systems, communication problems, security and privacy. This paper searchs in indexed journals in order to identify best practices and their current components to create IoT systems. The study applies a systematic literature review method implemented in six phases (planning, primary search, preliminary selection, selection, data extraction and analysis) that allows information to be extracted consistently. We obtain 97 papers applying the method to carry out the analysis. The results show there are different proposals that use some mechanism of knowledge representation for the creation or improvement IoT systems. The paper is a theoretical basis for creating better IoT systems in organizations and reducing costs.

La presente experiencia pedagógica tuvo como propósito fortalecer las competencias investigativas de los estudiantes de III y IV semestre de Ingeniería de la Universidad Católica de Pereira llevando a cabo la implementación de la estrategia denominada colectivos de docentes y estudiantes, en la cual se busca que el estudiante desarrolle proyectos que lo lleven a desplazar las barreras de su propio conocimiento a través de la problematización, indagación y autoconstrucción, abordando temáticas que fueran de interés global e implementando un enfoque STEM y además consiguiendo vincular y conectar los contenidos y conocimientos de las asignaturas de ciencias básicas y las asignaturas disciplinares. Se presentan los resultados del seguimiento durante tres semestres consecutivos donde se pudo observar la evolución en los proyectos desarrollados por los estudiantes, así como una mejora en sus competencias comunicativas.

Automatic term extraction is a branch of Natural Language Processing (NLP) used to automatically generate lexicographic materials, such as glossaries, vocabularies, and dictionaries. It allows the creation of standard bases for building unified theories and translations between languages. Scientific literature shows great interest in the construction of automatic term extractors and includes several approaches, tools, algorithms, and methods that can be used for their construction; however, the number of articles in specialized databases is vast, and literature reviews are not recent. This paper presents a systematic literature mapping of the existing material for developing automatic term extractors to provide an overview of approaches, tools, algorithms, and methods used to create them. For this purpose, scientific articles in the domain published between 2015 and 2022 are reviewed and categorized. The mapping results show that among the most used approaches are statistical, with 21.85%; linguistic, with 9.75%; and hybrid, with 68.29%. In addition, there are various computational tools for terminology extraction where authors use different methods for their construction and whose results are measured under the criteria of precision and recall. Finally, 113 documents were selected to answer the research questions and to demonstrate how automatic term extractors are constructed. This paper presents a global summary of primary studies as an essential tool to approach this type of computational system construction.

Automatic Term Extraction is a key area in Natural Language Processing (NLP) focused on generating lexicographic materials essential for standardized theories and cross-lingual translations. While prior studies have surveyed the field, a systematic mapping that concurrently analyzes the relationships between approaches, tools, algorithms and methods has been lacking. This paper fills that gap by presenting a Systematic Literature Mapping (SLM) of 113 scientific papers published between 2015 and 2022. Our analysis provides a novel four-dimensional taxonomy (Approach, Domain, Evaluation, Tool Integration) for Automatic Term Extractors (ATE) and reveals a clear preference for hybrid approaches (50%), which integrate linguistic and statistical approaches, over purely statistical (38.2%) or linguistic (11.8%) ones. The findings show that Python's NLTK library is the most widely adopted tool, while algorithms like C-Value (50%) and TF-IDF (31%) dominate due to their robust performance. The unique contribution of this work is its granular, data-driven overview of the contemporary Automatic Term Extraction landscape, highlighting a critical need for more formal development methodologies and broader domain application. This study serves as a crucial resource for researchers by mapping dominant trends and identifying key research gaps.