Application of inferences in ontological modeling

Abstract

Abstract Data modeling supported by ontologies favors the organization of knowledge and the use of inference machines to obtain new knowledge about data and support expert decision-making. However, it is necessary to discuss the techniques to promote inferences and explore the semantics between the data. The research problem is to verify how the use of semantic technologies would represent an advantageous alternative, in relation to the state of the art, to analyze, systematize and organize the information of a domain, accelerating a decision-making process. It presents the concepts behind the development of an artifact that employs semantic technologies and a domain ontology to integrate data from Building Information Modeling projects and other tabular data external to the project to automate Leadership in Energy certification tasks and Environmental Design. To demonstrate the artifact, data is semantically annotated and a knowledge graph is generated in Resource Description Framework format. Once integrated into the graph, inferences are applied to evaluate the certification criteria through queries. The results suggest that the application of these technologies promotes semantic extension, facilitates integration with other knowledge bases, and conceptually organizes data to better retrieve information and generate new knowledge.