An Intelligent environment can respond to the necessities of the users according to the context, this is so that the individuals can have the ideal climatic conditions in order to go about their activities, these conditions are related through a series of special norms. The events that we describe in this paper are in relation to the events of the environment (temperature, humidity, brightness, and presence), also involved are different variables like time, space, or person, such are important in order to be able to model what is occurring in a determined place. In this project, we propose a personalized ontological design for the academic dominion. The ontological model is utilized for the identification of environmental events according to the data acquired from the environment through the simulation of intellectual agents. Also, our ontological model is used to rationalize with the information obtained from the identified events. The model of ontologies based on events considers four contextual questions like a perspective modular: person, seasonality (weather), spatiality (location), network (resources in order to acquire environmental data) and event (academic events). And the detector is based on rules obtained from the standards of optimum climatic conditions of a physical space.

The extraction of relationships in natural language processing (NLP) is a task that consists of identifying interactions between entities within a text. This approach facilitates comprehension of context and meaning. In the medical field, this is of particular significance due to the substantial volume of information contained in scientific articles. This paper explores various training strategies for medical relationship extraction using large pre-trained language models. The findings indicate significant variations in performance between models trained with general domain data and those specialized in the medical domain. Furthermore, a methodology is proposed that utilizes language models for relation extraction with hyperparameter optimization techniques. This approach uses a triplet-based system. It provides a framework for the organization of relationships between entities and facilitates the development of medical knowledge graphs in the Spanish language. The training process was conducted using a dataset constructed and validated by medical experts. The dataset under consideration focused on relationships between entities, including anatomy, medications, and diseases. The final model demonstrated an 85.9% accuracy rate in the relationship classification task, thereby substantiating the efficacy of the proposed approach.